{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:05:21Z","timestamp":1774922721538,"version":"3.50.1"},"reference-count":349,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T00:00:00Z","timestamp":1669507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Historically, geoscience has been a prominent domain for applications of computer vision and pattern recognition. The numerous challenges associated with geoscience-related imaging data, which include poor imaging quality, noise, missing values, lack of precise boundaries defining various geoscience objects and processes, as well as non-stationarity in space and\/or time, provide an ideal test bed for advanced computer vision techniques. On the other hand, the developments in pattern recognition, especially with the rapid evolution of powerful graphical processing units (GPUs) and the subsequent deep learning breakthrough, enable valuable computational tools, which can aid geoscientists in important problems, such as land cover mapping, target detection, pattern mining in imaging data, boundary extraction and change detection. In this landscape, classical computer vision approaches, such as active contours, superpixels, or descriptor-guided classification, provide alternatives that remain relevant when domain expert labelling of large sample collections is often not feasible. This issue persists, despite efforts for the standardization of geoscience datasets, such as Microsoft\u2019s effort for AI on Earth, or Google Earth. This work covers developments in applications of computer vision and pattern recognition on geoscience-related imaging data, following both pre-deep learning and post-deep learning paradigms. Various imaging modalities are addressed, including: multispectral images, hyperspectral images (HSIs), synthetic aperture radar (SAR) images, point clouds obtained from light detection and ranging (LiDAR) sensors or digital elevation models (DEMs).<\/jats:p>","DOI":"10.3390\/rs14236017","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"6017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Computer Vision and Pattern Recognition for the Analysis of 2D\/3D Remote Sensing Data in Geoscience: A Survey"],"prefix":"10.3390","volume":"14","author":[{"given":"Michalis A.","family":"Savelonas","sequence":"first","affiliation":[{"name":"Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece"}]},{"given":"Christos N.","family":"Veinidis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece"}]},{"given":"Theodoros K.","family":"Bartsokas","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, 15784 Athens, Greece"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1544","DOI":"10.1109\/TKDE.2018.2861006","article-title":"Machine learning for the geosciences: Challenges and op-portunities","volume":"31","author":"Karpatne","year":"2019","journal-title":"IEEE Tran. Knowl. Dat. Eng."},{"key":"ref_2","unstructured":"NASA, and USGS (2022, June 24). Landsat Data Archive, Available online: https:\/\/landsat.gsfc.nasa.gov\/data\/."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106844","DOI":"10.1016\/j.compag.2022.106844","article-title":"A comprehensive review of remote sensing platforms, sensors, and applications in nut crops","volume":"197","author":"Jafarbiglu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Manfreda, S., McCabe, M.F., Miller, P.E., Lucas, R., Madrigal, V.P., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., and Ciraolo, G. (2018). On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sens., 10.","DOI":"10.20944\/preprints201803.0097.v1"},{"key":"ref_5","unstructured":"Peckham, S.D. (2014, January 15\u201319). The CSDMS standard names: Cross-domain naming conventions for describing process models, data sets and their associated variables. Proceedings of the International Congress on Environmental Modelling and Software, San Diego, CA, USA."},{"key":"ref_6","unstructured":"(2022, June 24). Microsoft, AI for Earth. Available online: https:\/\/www.microsoft.com\/en-us\/ai\/ai-for-earth."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Env."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/s41597-022-01307-4","article-title":"Dynamic World, near real-time global 10\u2009m land use land cover mapping","volume":"9","author":"Brown","year":"2022","journal-title":"Sci. Data"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1177\/0309133317703092","article-title":"Cameras and settings for aerial surveys in the geosciences","volume":"41","author":"Smith","year":"2017","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Eismann, M.T. (2012). Hyperspectral Remote Sensing, SPIE Press.","DOI":"10.1117\/3.899758"},{"key":"ref_11","unstructured":"Gewali, U.B., Monteiro, S.T., and Saber, E. (2018). Machine learning based hyperspectral image analysis: A survey. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2014.11.001","article-title":"Urban land cover classification using airborne LiDAR data: A review","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3681","DOI":"10.1109\/TGRS.2014.2381602","article-title":"Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification","volume":"53","author":"Li","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2006.12.004","article-title":"Feature extraction of hyperspectral images using wavelet and matching pursuit","volume":"62","author":"Hsu","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","first-page":"542","article-title":"Classification of hyperspectral images by using ex-tended morphological attribute profiles and independent component analysis","volume":"8","author":"Villa","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.neucom.2020.04.138","article-title":"Hyperspectral image classification based on sparse modeling of spectral blocks","volume":"407","author":"Azar","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1109\/34.765655","article-title":"Using spin images for efficient object recognition in cluttered 3D scenes","volume":"21","author":"Johnson","year":"1999","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., Marton, Z.C., and Beetz, M. (2008, January 22\u201326). Aligning point cloud views using persistent feature histograms. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4650967"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tombari, F., Salti, S., and Stefano, L.D. (2010, January 5\u201311). Unique signatures of histograms for local surface description. Proceedings of the European Conference on Computer Vision, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15558-1_26"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","unstructured":"Csurka, G., Dance, C., Fan, L., Willamowski, J., and Bray, C. (2004, January 11\u201314). Visual categorization with bags of keypoints. Proceedings of the European Conference on Computer Vision, Prague, Czech Republic."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2015","DOI":"10.1109\/JSTARS.2015.2444405","article-title":"Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification","volume":"8","author":"Hu","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1109\/TPAMI.2009.132","article-title":"Visual Word Ambiguity","volume":"32","author":"Veenman","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Blake, A., Kohli, P., and Rother, C. (2011). Markov Random Fields for Vision and Image Processing, The MIT Press.","DOI":"10.7551\/mitpress\/8579.001.0001"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2017.03.007","article-title":"Superpixels: An evaluation of the state-of-the-art","volume":"166","author":"Stutz","year":"2018","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_27","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, CA, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Le Cun, Y., Boser, B., Denker, J., Henderson, D., Howard, R., Hubbard, W., and Jackel, L. (1989, January 27\u201330). Handwritten digit recognition with a back-propagation network. Proceedings of the International Conference on Neural Information Processing Systems, Denver, CO, USA.","DOI":"10.1109\/35.41400"},{"key":"ref_29","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","unstructured":"Schmidhuber, J. (1993). Network Architectures, Objective Functions, and Chain Rule, Institut fur Informatik, Technische Universitat Munchen."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neur. Comput."},{"key":"ref_34","unstructured":"Kingma, D.P., and Welling, M. (2014, January 14\u201316). Auto-encoding variational Bayes. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_35","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative Adversarial Nets. Proceedings of the International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"eaau0323","DOI":"10.1126\/science.aau0323","article-title":"Machine learning for data-driven discovery in solid Earth geoscience","volume":"363","author":"Bergen","year":"2019","journal-title":"Science"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.gsf.2015.07.003","article-title":"Machine learning in geosciences and remote sensing","volume":"7","author":"Lary","year":"2016","journal-title":"Geosci. Front."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3042064","article-title":"Deep learning advances in computer vision with 3D data: A survey","volume":"50","author":"Ioannidou","year":"2018","journal-title":"ACM Comp. Surv."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7954154","DOI":"10.1155\/2016\/7954154","article-title":"Deep Learning for Remote Sensing Image Understanding","volume":"2016","author":"Zhang","year":"2016","journal-title":"J. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4761","DOI":"10.1038\/s41467-021-24952-6","article-title":"Machine learning and earthquake forecasting\u2014Next steps","volume":"12","author":"Beroza","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5039","DOI":"10.1109\/TGRS.2011.2157166","article-title":"Three-Dimensional Gabor Wavelets for Pixel-Based Hyperspectral Imagery Classification","volume":"49","author":"Shen","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comp. Vis."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Frome, A., Huber, D., Kolluri, R., B\u00fclow, T., and Malik, J. (2004, January 11\u201314). Recognizing Objects in Range Data Using Regional Point Descriptors. Proceedings of the 8th European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24672-5_18"},{"key":"ref_47","unstructured":"Rusu, R.B., Bradski, G., Thibaux, R., and Hsu, J. (2014, January 14\u201318). Fast 3D recognition and pose using the viewpoint feature histogram. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Sivic, J., and Zisserman, A. (2004, January 14\u201318). Video Google: A text retrieval approach to object matching in videos. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","article-title":"Aggregating Local Image Descriptors into Compact Codes","volume":"34","author":"Jegou","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Perronnin, F., Liu, Y., Sanchez, J., and Poirier, H. (2010, January 13\u201318). Large-scale image retrieval with compressed Fisher vectors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540009"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1109\/TGRS.2013.2274101","article-title":"An Active Contour Model Based on Texture Distribution for Extracting Inhomogeneous Insulators From Aerial Images","volume":"52","author":"Wu","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/0021-9991(88)90002-2","article-title":"Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formula-tions","volume":"79","author":"Osher","year":"1988","journal-title":"J. Comp. Phys."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1109\/LGRS.2010.2047711","article-title":"SVM- and MRF-based method for accurate classification of hyperspectral images","volume":"7","author":"Tarabalka","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"2966","DOI":"10.1109\/TCYB.2015.2484324","article-title":"Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization","volume":"46","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1109\/36.481897","article-title":"A Markov random field model for classification of multisource satellite imagery","volume":"34","author":"Solberg","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1610","DOI":"10.1016\/j.cviu.2013.07.004","article-title":"Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey","volume":"117","author":"Wang","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/TPAMI.2004.1262177","article-title":"What energy functions can be minimized via graph cuts?","volume":"26","author":"Kolmogorov","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1109\/TGRS.2015.2466657","article-title":"Integrating Hierarchical Segmentation Maps with MRF Prior for Classification of Hyperspectral Images in a Bayesian Framework","volume":"54","author":"Golipour","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/JPROC.2012.2211551","article-title":"Land-Cover Mapping by Markov Modeling of Spatial\u2013Contextual Information in Very-High-Resolution Remote Sensing Images","volume":"101","author":"Moser","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_61","unstructured":"Neubert, P., and Protzel, P. (2012). Superpixel Benchmark and Comparison, Karlsruher Instituts f\u00fcr Technologie (KIT) Scientific Publishing."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Csillik, O. (2017). Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels. Remote Sens., 9.","DOI":"10.3390\/rs9030243"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC Superpixels Compared to State-of-the-Art Superpixel Methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Moore, A.P., Prince, J., Warrell, J., Mohammed, U., and Jones, G. (2008, January 23\u201328). Superpixel lattices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587471"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"2290","DOI":"10.1109\/TPAMI.2009.96","article-title":"TurboPixels: Fast Superpixels Using Geometric Flows","volume":"31","author":"Levinshtein","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Soatto, S. (2008, January 12\u201318). Quick shift and kernel methods for mode seeking. Proceedings of the European Conference on Computer Vision, Marseille, France.","DOI":"10.1007\/978-3-540-88693-8_52"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Webb, A.R., and Copsey, K.D. (2011). Statistical Pattern Recognition, Wiley. [3rd ed.].","DOI":"10.1002\/9781119952954"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neur. Net."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/34.824819","article-title":"Statistical pattern recognition: A review","volume":"22","author":"Jain","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","article-title":"Data clustering: A review","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comp. Surv."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"719","DOI":"10.9790\/3021-0204719725","article-title":"An Overview on Clustering Methods","volume":"2","author":"Madhulatha","year":"2012","journal-title":"IOSR J. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/TETC.2014.2330519","article-title":"A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis","volume":"2","author":"Fahad","year":"2014","journal-title":"IEEE Trans. Emerg. Top. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1002\/widm.53","article-title":"Algorithms for hierarchical clustering: An overview","volume":"2","author":"Murtagh","year":"2012","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"e1219","DOI":"10.1002\/widm.1219","article-title":"Algorithms for hierarchical clustering: An overview, II","volume":"7","author":"Murtagh","year":"2017","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/3477.809032","article-title":"A survey of fuzzy clustering algorithms for pattern recognition. I","volume":"29","author":"Baraldi","year":"1999","journal-title":"IEEE Trans. Sys. Man Cybern. Part B (Cybern.)"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/S0377-2217(00)00320-9","article-title":"Genetic clustering algorithms","volume":"135","author":"Chiou","year":"2001","journal-title":"Eur. J. Oper. Res."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.patcog.2005.07.005","article-title":"Unsupervised possibilistic clustering","volume":"39","author":"Yang","year":"2006","journal-title":"Pattern Recognit."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1002\/widm.30","article-title":"Density-based clustering","volume":"1","author":"Kriegel","year":"2011","journal-title":"WIREs Dat. Min. Knowl. Disc."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1109\/MSP.2010.939739","article-title":"Subspace Clustering","volume":"28","author":"Vidal","year":"2011","journal-title":"IEEE Sig. Proc. Mag."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","article-title":"Least squares quantization in PCM","volume":"28","author":"Lloyd","year":"1982","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/01969727308546046","article-title":"A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters","volume":"3","author":"Dunn","year":"1973","journal-title":"J. Cybern."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Gustafson, D.E., and Kessel, W.C. (1979, January 10\u201312). Fuzzy clustering with a fuzzy covariance matrix. Proceedings of the IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, San Diego, CA, USA.","DOI":"10.1109\/CDC.1978.268028"},{"key":"ref_83","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD\u201996), Portland, Oregon."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1145\/304181.304187","article-title":"OPTICS: Ordering points to identify the clustering structure","volume":"28","author":"Ankerst","year":"1999","journal-title":"SIGMOD Rec."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhang, T., Ramakrishnan, R., and Livny, M. (1996, January 4\u20136). BIRCH: An efficient data clustering method for very large databases. Proceedings of the 1996 ACM SIGMOD international conference on Management of Data\u2014SIGMOD\u201996, Montreal, QC, Canada.","DOI":"10.1145\/233269.233324"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1945","DOI":"10.1109\/TPAMI.2005.244","article-title":"Generalized principal component analysis (GPCA)","volume":"27","author":"Vidal","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedan","year":"2002","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_91","unstructured":"Theodoridis, S., and Koutroumbas, K. (2008). Pattern Recognition, Academic Press, Inc.. [4th ed.]."},{"key":"ref_92","first-page":"263","article-title":"Solving multi-class learning problems via error-correcting output codes","volume":"2","author":"Dietterich","year":"1995","journal-title":"J. Art. Intell. Res."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Theodoridis, S. (2015). Machine Learning, a Bayesian and Optimization Perspective, Academic Press.","DOI":"10.1016\/B978-0-12-801522-3.00012-4"},{"key":"ref_94","unstructured":"Chong, E.K.P., and Zak, S.H. (2001). An Introduction to Optimization, Wiley."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1063\/1.4822376","article-title":"Fundamentals of Artificial Neural Networks","volume":"10","author":"Hassoun","year":"1995","journal-title":"Comput. Phys."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Gurney, K. (1997). An Introduction to Neural Networks, Taylor & Francis, Inc.","DOI":"10.4324\/9780203451519"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/2.485891","article-title":"Artificial neural networks: A tutorial","volume":"29","author":"Jain","year":"1996","journal-title":"Computer"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/S0169-7439(97)00061-0","article-title":"Introduction to multi-layer feed-forward neural networks","volume":"39","author":"Svozil","year":"1997","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Kohonen, T. (2001). Self-Organizing Maps, Springer. [3rd ed.].","DOI":"10.1007\/978-3-642-56927-2"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"2554","DOI":"10.1073\/pnas.79.8.2554","article-title":"Neural networks and physical systems with emergent collective computational abilities","volume":"79","author":"Hopfield","year":"1982","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF02478259","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"5","author":"Mcculloch","year":"1943","journal-title":"Bull. Math. Biophys."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_103","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (July, January 26). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_104","unstructured":"Chung, J., Cho, C.G.K., and Bengio, Y. (2014). Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014, January 25). On the properties of neural machine translation: Encoder\u2013decoder approaches. Proceedings of the Workshop on Syntax, Semantics and Structure in Statistical Translation, Doha, Qatar.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_107","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2016). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Savelonas, M., Vernikos, I., Mantzekis, D., Spyrou, E., Tsakiri, A., and Karkanis, S. (2021). Hybrid Representation of Sensor Data for the Classification of Driving Behaviour. Appl. Sci., 11.","DOI":"10.3390\/app11188574"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in Vision: A Survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., and Xu, Y. (2022). A Survey on Vision Transformer. IEEE Trans. Pattern Anal. Mach. Intell., 1.","DOI":"10.1109\/TPAMI.2022.3152247"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Aleissaee, A.A., Kumar, A., Anwer, R.M., Khan, S., Cholakkal, H., Xia, G.-S., and Khan, F.S. (2022). Transformers in remote sensing: A survey. arXiv.","DOI":"10.3390\/rs15071860"},{"key":"ref_112","unstructured":"Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J. (2016). Unrolled generative adversarial networks. arXiv."},{"key":"ref_113","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein GAN. arXiv."},{"key":"ref_114","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P. (2017, January 4\u20139). InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_115","unstructured":"Karras, T., Aila, T., Laine, S., and Lehtinen, J. (2017). Progressive growing of GANs for improved quality, stability, and variation. arXiv."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J., Zhou, T., and Efros, A.A. (2017, January 21\u201326). Image-to-image translation with conditional adversarial networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1109\/TGRS.2015.2490078","article-title":"Meaningful object segmentation from SAR images via a multiscale nonlocal active contour model. IEEE Trans","volume":"54","author":"Xia","year":"2016","journal-title":"Geosci. Remote Sens."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.rse.2016.01.003","article-title":"Semi-automated landslide inventory mapping from bitemporal aerial photographs using change detection and level set method","volume":"175","author":"Li","year":"2016","journal-title":"Remote Sens. Env."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of hyperspectral images by exploiting spectral\u2013spatial in-formation of superpixel via multiple kernels. IEEE Trans","volume":"53","author":"Fang","year":"2015","journal-title":"Geosci. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"4186","DOI":"10.1109\/TGRS.2015.2392755","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Images with a Superpixel-Based Discriminative Sparse Model","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.patcog.2017.09.007","article-title":"Superpixel-based 3D deep neural networks for hyperspectral image classification","volume":"74","author":"Shi","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"3503","DOI":"10.1109\/TGRS.2010.2047020","article-title":"Automatic fuzzy clustering using modified differential evolution for image classification","volume":"48","author":"Maulik","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1023\/A:1008202821328","article-title":"Differential evolution\u2014A simple and efficient heuristic for global optimization over continuous spaces","volume":"11","author":"Storn","year":"1997","journal-title":"J. Glob. Optim."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/LGRS.2014.2322960","article-title":"Superpixel Segmentation for Polarimetric SAR Imagery Using Local Iterative Clustering","volume":"12","author":"Qin","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"3672","DOI":"10.1109\/TGRS.2016.2524557","article-title":"Spectral\u2013Spatial Sparse Subspace Clustering for Hyperspectral Remote Sensing Images","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.biosystemseng.2018.09.014","article-title":"Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images","volume":"177","author":"Reza","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1109\/TGRS.2015.2450759","article-title":"A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection","volume":"54","author":"Jia","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1492","DOI":"10.1126\/science.1242072","article-title":"Clustering by fast search and find of density peaks","volume":"344","author":"Rodriguez","year":"2014","journal-title":"Science"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"1431","DOI":"10.1109\/TGRS.2015.2480866","article-title":"Dual-Clustering-Based Hyperspectral Band Selection by Contextual Analysis","volume":"54","author":"Yuan","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_131","first-page":"5910","article-title":"Optimal Clustering Framework for Hyperspectral Band Selection","volume":"56","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1109\/TGRS.2018.2868796","article-title":"Laplacian-regularized low-rank subspace clustering for hyperspectral image band se-lection","volume":"57","author":"Zhai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/TIP.2010.2076294","article-title":"An Augmented Lagrangian Approach to the Constrained Optimization Formulation of Imaging Inverse Problems","volume":"20","author":"Afonso","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","article-title":"Investigation of the random forest framework for classification of hyperspectral data","volume":"43","author":"Ham","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1142\/S0218001404003411","article-title":"Adaptive feature spaces for land cover classification with limited ground truth","volume":"18","author":"Morgan","year":"2004","journal-title":"Int. J. Pattern Recognit. Art. Intell."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"Ho","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random Forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1016\/j.rse.2011.05.013","article-title":"Object-oriented mapping of landslides using Random Forests","volume":"115","author":"Stumpf","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10661-015-4489-3","article-title":"Land cover mapping based on random forest classification of multitemporal spectral and thermal images","volume":"187","author":"Eisavi","year":"2015","journal-title":"Environ. Monit. Assess."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"3107","DOI":"10.1109\/JSTARS.2015.2396577","article-title":"Random Forests Unsupervised Classification: The Detection and Mapping of Solanum mauritianum Infestations in Plantation Forestry Using Hyperspectral Data","volume":"8","author":"Peerbhay","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_141","unstructured":"Scott, G.L., and Longuet-Higgins, H.C. (, January September). Feature grouping by relocalisation of eigenvectors of proximity matrix. Proceedings of the British Machine Vision Conference, Oxford, UK."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1111\/j.1538-4632.1995.tb00338.x","article-title":"The Local Indicators of Spatial Association\u2014LISA","volume":"27","author":"Anselin","year":"1995","journal-title":"Geogr. Anal."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"8368","DOI":"10.3390\/rs70708368","article-title":"The Improvement of Land Cover Classification by Thermal Remote Sensing","volume":"7","author":"Sun","year":"2015","journal-title":"Remote Sens."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1080\/01431161.2016.1277043","article-title":"Drone-based land-cover mapping using a fuzzy unordered rule induction al-gorithm integrated into object-based image analysis","volume":"38","author":"Kalantar","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"3374","DOI":"10.1109\/TGRS.2006.880628","article-title":"Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images","volume":"44","author":"Bazi","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1109\/TGRS.2004.842022","article-title":"Partially Supervised classification of remote sensing images through SVM-based probability density estimation","volume":"43","author":"Mantero","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.rse.2004.06.017","article-title":"Toward intelligent training of supervised image classifications: Directing training data acquisition for SVM classification","volume":"93","author":"Foody","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.rse.2006.04.001","article-title":"The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM","volume":"103","author":"Foody","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2008.915597","article-title":"Multiclass and Binary SVM Classification: Implications for Training and Classification Users","volume":"5","author":"Mathur","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/LGRS.2008.2009324","article-title":"A Composite Semisupervised SVM for Classification of Hyperspectral Images","volume":"6","author":"Marconcini","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2012.2202912","article-title":"An SVM Ensemble Approach Combining Spectral, Structural, and Semantic Features for the Classification of High-Resolution Remotely Sensed Imagery","volume":"51","author":"Huang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"WIREs Comp. Stat."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Chini, M., Pacifici, F., and Emery, W.J. (2009, January 12\u201317). Morphological operators applied to X-band SAR for urban land use classification. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417424"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"6219","DOI":"10.1080\/01431160902842359","article-title":"Quantitative indices based on 3D discrete wavelet transform for urban complexity estimation using remotely sensed imagery","volume":"30","author":"Yoo","year":"2009","journal-title":"Int. J. Remote Sens."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1109\/LGRS.2009.2035644","article-title":"Object Classification of Aerial Images with Bag-of-Visual Words","volume":"7","author":"Xu","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/TGRS.2013.2258676","article-title":"SVM Active Learning Approach for Image Classification Using Spatial Information","volume":"52","author":"Pasolli","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/LGRS.2006.877949","article-title":"Logistic Regression for Feature Selection and Soft Classification of Remote Sensing Data","volume":"3","author":"Cheng","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_159","first-page":"4085","article-title":"Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression with Active Learning","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. Royal Stat. Soc. Series B (Methodol.)"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"809","DOI":"10.1109\/TGRS.2011.2162649","article-title":"Spectral\u2013Spatial Hyperspectral Image Segmentation Using Subspace Multinomial Logistic Regression and Markov Random Fields","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/36.763299","article-title":"A neural-statistical approach to multitemporal and multisource remote-sensing image classification","volume":"37","author":"Bruzzone","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1016\/j.rse.2009.05.014","article-title":"Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks","volume":"113","author":"Hu","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TGRS.2003.817682","article-title":"Phytoplankton determination in an optically complex coastal region using a multilayer perceptron neural network","volume":"41","author":"Zibordi","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep supervised learning for hyperspectral data classification through convolutional neural networks. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sensors"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense Semantic Labeling of Subdecimeter Resolution Images with Convolutional Neural Networks","volume":"55","author":"Volpi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"7177","DOI":"10.1109\/TGRS.2017.2743222","article-title":"Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification","volume":"55","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/LGRS.2017.2657778","article-title":"Training Deep Convolutional Neural Networks for Land\u2013Cover Classification of High-Resolution Imagery","volume":"14","author":"Scott","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1109\/TGRS.2017.2756851","article-title":"Multisource Remote Sensing Data Classification Based on Convolutional Neural Network","volume":"56","author":"Xu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"5653","DOI":"10.1109\/TGRS.2017.2711275","article-title":"Integrating Multilayer Features of Convolutional Neural Networks for Remote Sensing Scene Classification","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_174","first-page":"1","article-title":"SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis","volume":"20","author":"Cai","year":"2007","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep Learning-Based Classification of Hyperspectral Data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"7048","DOI":"10.1109\/TGRS.2019.2910603","article-title":"Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.1109\/TGRS.2020.2964627","article-title":"Hyperspectral Image Classification with Convolutional Neural Network and Active Learning","volume":"58","author":"Cao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_179","first-page":"5517010","article-title":"Convolutional Neural Networks for Multimodal Remote Sensing Data Classification","volume":"60","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_180","first-page":"5502012","article-title":"Accelerating convolutional neural network-based hyperspectral image classifica-tion by step activation quantization","volume":"60","author":"Mei","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A. (2016, January 8\u201316). XNOR-Net: Image\u039det classification using binary convolutional neural networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"7790","DOI":"10.1109\/TGRS.2020.3038212","article-title":"Attention-Aware Pseudo-3-D Convolutional Neural Network for Hyperspectral Image Classification","volume":"59","author":"Lin","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1109\/TIP.2022.3144017","article-title":"Weighted Feature Fusion of Convolutional Neural Network and Graph Attention Network for Hyperspectral Image Classification","volume":"31","author":"Dong","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"5525921","DOI":"10.1109\/TGRS.2022.3160513","article-title":"Evolving block-based convolutional neural network for hyperspectral image classification","volume":"60","author":"Lu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1109\/LGRS.2017.2728698","article-title":"Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent Neural Networks","volume":"14","author":"Ienco","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"3639","DOI":"10.1109\/TGRS.2016.2636241","article-title":"Deep Recurrent Neural Networks for Hyperspectral Image Classification","volume":"55","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"4962","DOI":"10.1109\/TGRS.2017.2697453","article-title":"Recurrent Neural Networks to Correct Satellite Image Classification Maps","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_188","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2018). Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7040129"},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Minh, D.H.T., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep Recurrent Neural Network for Agricultural Classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.3390\/rs10081217"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1109\/LGRS.2018.2794581","article-title":"Deep recurrent neural networks for winter vegetation quality mapping via multitemporal SAR Sen-tinel-1","volume":"15","author":"Lalande","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"5384","DOI":"10.1109\/TGRS.2019.2899129","article-title":"Cascaded Recurrent Neural Networks for Hyperspectral Image Classification","volume":"57","author":"Hang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/01431160902882561","article-title":"Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests","volume":"31","author":"Lee","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_193","unstructured":"Beucher, S., and Lantu\u00e9joul, C. (1979, January 17\u201321). Use of watersheds in contour detection. Proceedings of the International Workshop on Image Processing: Real-Time Edge and Motion Detection\/Estimation, Rennes, France."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TGRS.2009.2012849","article-title":"Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine","volume":"47","author":"Kim","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Kim, Y.J., Nam, B.H., and Youn, H. (2019). Sinkhole detection and characterization using LiDAR-derived DEM with logistic regression. Remote Sens., 11.","DOI":"10.3390\/rs11131592"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"3786","DOI":"10.1109\/TGRS.2009.2025371","article-title":"Automatic Target Recognition by Means of Polarimetric ISAR Images and Neural Networks","volume":"47","author":"Martorella","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"3506","DOI":"10.1109\/TGRS.2006.879115","article-title":"Conservative polarimetric scatterers and their role in incorrect extensions of the Cameron decomposition","volume":"44","author":"Cameron","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.3390\/rs70201529","article-title":"Multilayer Perceptron Neural Networks Model for Meteosat Second Generation SEVIRI Daytime Cloud Masking","volume":"7","author":"Taravat","year":"2015","journal-title":"Remote Sens."},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","article-title":"Vehicle detection in satellite images by hybrid deep convolutional neural networks","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_201","first-page":"364","article-title":"Convolutional Neural Network with Data Augmentation for SAR Target Recognition","volume":"13","author":"Ding","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_203","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective Search for Object Recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_204","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1109\/TGRS.2017.2669341","article-title":"Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network","volume":"55","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_205","doi-asserted-by":"crossref","first-page":"4062","DOI":"10.1109\/TGRS.2018.2889677","article-title":"Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network","volume":"57","author":"Shao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_206","doi-asserted-by":"crossref","unstructured":"Hsieh, M.R., Lin, Y.L., and Hsu, W.H. (2017, January 22\u201329). Drone-based object counting by spatially regularized regional proposal network. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.446"},{"key":"ref_207","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_208","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2018.06.028","article-title":"Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning","volume":"216","author":"Kellenberger","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_209","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009, January 14\u201318). Curriculum learning. Proceedings of the International Conference on Machine Learning, New York, NY, USA.","DOI":"10.1145\/1553374.1553380"},{"key":"ref_210","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., and Girshick, R. (2016, January 27\u201330). Training region-based object detectors with online hard example mining. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref_211","doi-asserted-by":"crossref","unstructured":"Zhang, H., Wang, G., Lei, Z., and Hwang, J.N. (2019, January 21\u201325). Eye in the sky: Drone-based object tracking and 3D localization. Proceedings of the ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3350933"},{"key":"ref_212","doi-asserted-by":"crossref","unstructured":"Wang, G., Wang, Y., Zhang, J.N., Gu, R., and Hwang, J.N. (2019, January 21\u201325). Exploit the connectivity: Multi-object tracking with TrackletNet. Proceedings of the ACM International Conference on Multimedia, Nice, France.","DOI":"10.1145\/3343031.3350853"},{"key":"ref_213","unstructured":"Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., and Szeliski, R. (2006, January 17\u201322). A comparison and evaluation of multi-view stereo reconstruction algorithms. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, New York, NY, USA."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"104994","DOI":"10.1016\/j.apgeochem.2021.104994","article-title":"Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method","volume":"130","author":"Zhang","year":"2021","journal-title":"Appl. Geochem."},{"key":"ref_215","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral Image Classification Using Deep Pixel-Pair Features","volume":"55","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_216","doi-asserted-by":"crossref","unstructured":"Xu, D., and Wu, Y. (2020). MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection. Remote Sens., 12.","DOI":"10.3390\/rs12193118"},{"key":"ref_217","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv."},{"key":"ref_218","doi-asserted-by":"crossref","unstructured":"Xu, D., and Wu, Y. (2021). FE-YOLO: A Feature Enhancement Network for Remote Sensing Target Detection. Remote Sens., 13.","DOI":"10.3390\/rs13071311"},{"key":"ref_219","doi-asserted-by":"crossref","unstructured":"Qing, Y., Liu, W., Feng, L., and Gao, W. (2021). Improved YOLO Network for Free-Angle Remote Sensing Target Detection. Remote Sens., 13.","DOI":"10.3390\/rs13112171"},{"key":"ref_220","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G., and Sun, J. (2021, January 19\u201325). RepVGG: Making VGG-style convnets great again. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"ref_221","doi-asserted-by":"crossref","unstructured":"Wang, C., Wang, Q., Wu, H., Zhao, C., Teng, G., and Li, J. (2021). Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3. Remote Sens., 13.","DOI":"10.3390\/rs13112130"},{"key":"ref_222","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., and Doll\u00e1r, P. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_223","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1109\/JSTARS.2022.3140776","article-title":"Multiscale and Direction Target Detecting in Remote Sensing Images via Modified YOLO-v4","volume":"15","author":"Zakria","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_225","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_226","doi-asserted-by":"crossref","unstructured":"Ke, X., Zhang, X., and Zhang, T. (2022). GCBANet: A Global Context Boundary-Aware Network for SAR Ship Instance Segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14092165"},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Li, Q., Chen, Y., and Zeng, Y. (2022). Transformer with Transfer CNN for Remote-Sensing-Image Object Detection. Remote Sens., 14.","DOI":"10.3390\/rs14040984"},{"key":"ref_228","doi-asserted-by":"crossref","unstructured":"Xiao, X., Guo, W., Chen, R., Hui, Y., Wang, J., and Zhao, H. (2022). A Swin Transformer-Based Encoding Booster Integrated in U-Shaped Network for Building Extraction. Remote Sens., 14.","DOI":"10.3390\/rs14112611"},{"key":"ref_229","first-page":"2503605","article-title":"Multiscale Feature Learning by Transformer for Building Extraction From Satellite Images","volume":"19","author":"Chen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_230","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple Attending Path Neural Network for Building Footprint Extraction From Remote Sensed Imagery","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"228","DOI":"10.2747\/1548-1603.49.2.228","article-title":"Using Landsat Imagery and Census Data for Urban Population Density Modeling in Port-au-Prince, Haiti","volume":"49","author":"Joseph","year":"2012","journal-title":"GIScience Remote Sens."},{"key":"ref_232","doi-asserted-by":"crossref","unstructured":"Hengl, T., Heuvelink, G.B.M., Kempen, B., Leenaars, J.G.B., Walsh, M.G., Shepherd, K.D., Sila, A., MacMillan, R.A., de Jesus, J.M., and Tamene, L. (2015). Mapping soil properties of Africa at 250 m resolution: Random forests significantly improve current predictions. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0125814"},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/0016-7061(95)00007-B","article-title":"Further results on prediction of soil properties from terrain attributes: Heterotopic cokriging and regression-kriging","volume":"67","author":"Odeh","year":"1995","journal-title":"Geoderma"},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.cageo.2007.05.001","article-title":"About regression-kriging: From equations to case studies","volume":"33","author":"Hengl","year":"2007","journal-title":"Comput. Geosci."},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Stevens, F.R., Gaughan, A.E., Linard, C., and Tatem, A.J. (2015). Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0107042"},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1080\/10106049.2019.1595177","article-title":"Geographical random forests: A spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling","volume":"36","author":"Georganos","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_237","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1109\/TGRS.2009.2014688","article-title":"A Unified Model for Remotely Estimating Chlorophyll a in Lake Taihu, China, Based on SVM and In Situ Hyperspectral Data","volume":"47","author":"Sun","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/S0034-4257(98)00084-4","article-title":"Spectroscopic determination of leaf biochemistry using band-depth analysis of absorption features and stepwise multiple linear regression","volume":"67","author":"Kokaly","year":"1999","journal-title":"Remote Sens. Env."},{"key":"ref_239","doi-asserted-by":"crossref","first-page":"6329","DOI":"10.1029\/JB089iB07p06329","article-title":"Reflectance spectroscopy: Quantitative analysis techniques for remote sensing applications","volume":"89","author":"Clark","year":"1984","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing data","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.rse.2013.09.011","article-title":"Re-greening Sahel: 30years of remote sensing data and field observations (Mali, Niger)","volume":"140","author":"Dardel","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_242","doi-asserted-by":"crossref","unstructured":"Du, M., Wang, L., Zou, S., and Shi, C. (2018). Modeling the Census Tract Level Housing Vacancy Rate with the Jilin1-03 Satellite and Other Geospatial Data. Remote Sens., 10.","DOI":"10.3390\/rs10121920"},{"key":"ref_243","doi-asserted-by":"crossref","unstructured":"Tien Bui, D., Khosravi, K., Shahabi, H., Daggupati, P., Adamowski, J.F., Melesse, A.M., Pham, B.T., Pourghasemi, H.R., Mahmoudi, M., and Bahrami, S. (2019). Flood spatial modeling in northern Iran using remote sensing and gis: A com-parison between evidential belief functions and its ensemble with a multivariate logistic regression model. Remote Sens., 11.","DOI":"10.3390\/rs11131589"},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1080\/0143116031000103781","article-title":"Radial Basis Function and Multilayer Perceptron neural networks for sea water optically active parameter estimation in case II waters: A comparison","volume":"24","author":"Corsini","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"5918","DOI":"10.3390\/rs70505918","article-title":"Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models","volume":"7","author":"Ozturk","year":"2015","journal-title":"Remote Sens."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/j.gsf.2020.09.002","article-title":"Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks","volume":"12","author":"Pradhan","year":"2021","journal-title":"Geosci. Front."},{"key":"ref_247","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2015.2483641","article-title":"An Integrated Active Contour Approach to Shoreline Mapping Using HSI and DEM","volume":"54","author":"Sukcharoenpong","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/TGRS.2017.2679112","article-title":"A Coastline Detection Method in Polarimetric SAR Images Mixing the Region-Based and Edge-Based Active Contour Models","volume":"55","author":"Liu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_249","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1080\/01431161.2016.1266104","article-title":"Coastline extraction from SAR images using spatial fuzzy clustering and the active contour method","volume":"38","author":"Modava","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_250","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_251","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zhang, X., Zhao, X., and Xin, Q. (2018). Extracting Building Boundaries from High Resolution Optical Images and LiDAR Data by Integrating the Convolutional Neural Network and the Active Contour Model. Remote Sens., 10.","DOI":"10.3390\/rs10091459"},{"key":"ref_252","doi-asserted-by":"crossref","first-page":"2070","DOI":"10.1109\/TGRS.2008.916643","article-title":"A Novel Approach to Unsupervised Change Detection Based on a Semisupervised SVM and a Similarity Measure","volume":"46","author":"Bovolo","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"3178","DOI":"10.1109\/TGRS.2010.2045506","article-title":"Unsupervised Change Detection in Multispectral Remotely Sensed Imagery with Level Set Methods","volume":"48","author":"Bazi","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/TFUZZ.2013.2249072","article-title":"Fuzzy Clustering with a Modified MRF Energy Function for Change Detection in Synthetic Aperture Radar Images","volume":"22","author":"Gong","year":"2014","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_255","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1109\/LGRS.2013.2275738","article-title":"Using combined difference image and k-means clustering for SAR image change detection","volume":"11","author":"Zheng","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.1109\/TIP.2009.2029593","article-title":"Iterative Weighted Maximum Likelihood Denoising with Probabilistic Patch-Based Weights","volume":"18","author":"Deledalle","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_257","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.ins.2010.10.016","article-title":"Fuzzy clustering algorithms for unsupervised change detection in remote sensing images","volume":"181","author":"Ghosh","year":"2011","journal-title":"Inf. Sci."},{"key":"ref_258","first-page":"15","article-title":"Unsupervised change detection in VHR remote sensing imagery\u2014An object-based clustering approach in a dynamic urban environment","volume":"54","author":"Leichtle","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote Sens."},{"key":"ref_260","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/JSTARS.2014.2362769","article-title":"Segmentation and Classification Using Logistic Regression in Remote Sensing Imagery","volume":"8","author":"Khurshid","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"3439","DOI":"10.1109\/JSTARS.2016.2541678","article-title":"Automatic Change Detection in High-Resolution Remote Sensing Images by Using a Multiple Classifier System and Spectral\u2013Spatial Features","volume":"9","author":"Tan","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_262","unstructured":"Molin, R.D., Rosa, R.A.S., Bayer, F.M., Pettersson, M.I., and Machado, R. (August, January 28). A change detection algorithm for SAR images based on logistic regression. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_263","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/LGRS.2009.2021780","article-title":"Automatic Change Detection in Very High Resolution Images with Pulse-Coupled Neural Networks","volume":"7","author":"Pacifici","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_264","first-page":"873","article-title":"The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images","volume":"13","author":"Salmon","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_265","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1109\/LGRS.2013.2245855","article-title":"Ensemble of Multilayer Perceptrons for Change Detection in Remotely Sensed Images","volume":"11","author":"Roy","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"2108","DOI":"10.1109\/TGRS.2015.2496185","article-title":"Dirichlet-Derived Multiple Topic Scene Classification Model for High Spatial Resolution Remote Sensing Imagery","volume":"54","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_267","doi-asserted-by":"crossref","unstructured":"Lyu, H., Lu, H., and Mou, L. (2016). Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection. Remote Sens., 8.","DOI":"10.3390\/rs8060506"},{"key":"ref_268","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1109\/TGRS.2018.2863224","article-title":"Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery","volume":"57","author":"Mou","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_269","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/TGRS.2018.2865197","article-title":"Hyperspectral Image Denoising Employing a Spatial\u2013Spectral Deep Residual Convolutional Neural Network","volume":"57","author":"Yuan","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1007\/s11053-020-09742-z","article-title":"Random-drop data augmentation of deep convolutional neural network for mineral pro-spectivity mapping","volume":"30","author":"Li","year":"2021","journal-title":"Nat. Res. Res."},{"key":"ref_271","doi-asserted-by":"crossref","first-page":"3443","DOI":"10.1007\/s11053-020-09668-6","article-title":"Effects of Random Negative Training Samples on Mineral Prospectivity Mapping","volume":"29","author":"Zuo","year":"2020","journal-title":"Nat. Resour. Res."},{"key":"ref_272","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.oregeorev.2014.09.007","article-title":"Receiver operating characteristics (ROC) as validation tool for prospectivity models\u2014A magmatic Ni\u2013Cu case study from the Central Lapland Greenstone Belt, Northern Finland","volume":"71","author":"Lahti","year":"2015","journal-title":"Ore Geol. Rev."},{"key":"ref_273","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3065461","article-title":"Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks","volume":"60","author":"Molini","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_274","unstructured":"Laine, S., Karras, T., Lehtinen, J., and Aila, T. (2019, January 8\u201314). High-quality self-supervised deep image denoising. Proceedings of the International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_275","doi-asserted-by":"crossref","first-page":"10227","DOI":"10.1109\/TGRS.2020.3042974","article-title":"PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening","volume":"59","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_276","unstructured":"Pan, H. (2020). Cloud removal for remote sensing imagery via spatial attention generative adversarial network. arXiv."},{"key":"ref_277","unstructured":"(2022, September 27). Spot. Available online: https:\/\/earth.esa.int\/eogateway\/missions\/spot."},{"key":"ref_278","unstructured":"(2022, September 27). ERS. Available online: https:\/\/earth.esa.int\/eogateway\/missions\/ers."},{"key":"ref_279","unstructured":"(2022, September 27). RADARSAT. Available online: https:\/\/earth.esa.int\/eogateway\/missions\/radarsat."},{"key":"ref_280","unstructured":"(2022, September 27). IRS. Available online: https:\/\/earth.esa.int\/eogateway\/missions\/irs-1d."},{"key":"ref_281","unstructured":"(2022, September 27). WorldView. Available online: https:\/\/earth.esa.int\/eogateway\/missions\/worldview-3."},{"key":"ref_282","unstructured":"(2022, September 27). QuickBird. Available online: https:\/\/earth.esa.int\/eogateway\/catalog\/quickbird-full-archive."},{"key":"ref_283","unstructured":"(2022, September 27). Pleiades. Available online: https:\/\/earth.esa.int\/eogateway\/catalog\/pleiades-esa-archive."},{"key":"ref_284","unstructured":"(2022, September 27). AVIRIS, Available online: https:\/\/aviris.jpl.nasa.gov\/data\/free_data.html."},{"key":"ref_285","doi-asserted-by":"crossref","unstructured":"Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., and Nemani, R. (2015, January 3\u20136). DeepSat: A learning framework for satellite imagery. Proceedings of the SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, DC, USA.","DOI":"10.1145\/2820783.2820816"},{"key":"ref_286","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). DeepGlobe 2018: A challenge to parse the earth through satellite images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_287","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_288","unstructured":"Sumbul, G., Charfuelan, M., Demir, B.U.M., and Markl, V. (August, January 28). Big Earth Net: A large-scale benchmark archive for remote sensing image understanding. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan."},{"key":"ref_289","doi-asserted-by":"crossref","unstructured":"Schmitt, M., Hughes, L.H., Qiu, C., and Zhu, X.X. (2019). SEN12MS\u2014A curated dataset of georeferenced multi-spectral Sentinel-1\/2 im-agery for deep learning and data fusion. arXiv.","DOI":"10.5194\/isprs-annals-IV-2-W7-153-2019"},{"key":"ref_290","doi-asserted-by":"crossref","unstructured":"Xu, G., Fang, Y., Deng, M., Sun, G., and Chen, J. (2022). Remote Sensing Mapping of Build-Up Land with Noisy Label via Fault-Tolerant Learning. Remote Sens., 14.","DOI":"10.3390\/rs14092263"},{"key":"ref_291","unstructured":"(2022, September 27). ESA World Cover 10 m 2020 v100. Available online: https:\/\/doi.org\/10.5281\/zenodo.5571936."},{"key":"ref_292","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global land use\/land cover with Sentinel 2 and deep learning. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_293","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.scib.2019.03.002","article-title":"Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017","volume":"64","author":"Gong","year":"2019","journal-title":"Sci. Bull."},{"key":"ref_294","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1038\/514434c","article-title":"Open access to Earth land-cover map","volume":"514","author":"Jun","year":"2014","journal-title":"Nature"},{"key":"ref_295","doi-asserted-by":"crossref","unstructured":"Dell\u2019 Acqua, F., Iannelli, G.C., Kerekes, J., Moser, G., Pierce, L., and Goldoni, E. (2017, January 23\u201328). The IEEE GRSS data and algorithm standard evaluation (DASE) website: Incrementally building a standardized assessment for algorithm performance. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127528"},{"key":"ref_296","unstructured":"(2022, September 05). IEEE GRSS Data Fusion Contest. Available online: https:\/\/www.grss-ieee.org\/community\/technical-committees\/2022-ieee-grss-data-fusion-contest\/."},{"key":"ref_297","unstructured":"(2022, September 05). Target Detection Blind Test. Available online: http:\/\/dirsapps.cis.rit.edu\/blindtest\/."},{"key":"ref_298","doi-asserted-by":"crossref","unstructured":"Abady, L., Barni, M., Garzelli, A., and Tondi, B. (2020, January 21\u201325). GAN generation of synthetic multispectral satellite images. Proceedings of the SPIE 11533, Image and Signal Processing for Remote Sensing XXVI, Online.","DOI":"10.1117\/12.2575765"},{"key":"ref_299","unstructured":"(2022, September 05). Copernicus Open Access Hub. Available online: https:\/\/scihub.copernicus.eu\/dhus\/#\/home."},{"key":"ref_300","doi-asserted-by":"crossref","first-page":"5799","DOI":"10.1109\/TGRS.2019.2902431","article-title":"Edge-Enhanced GAN for Remote Sensing Image Superresolution","volume":"57","author":"Jiang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_301","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yao, Q., Kwok, J., and Ni, L.N. (2019). Generalizing from a few examples: A survey on few-shot learning. arXiv.","DOI":"10.1145\/3386252"},{"key":"ref_302","doi-asserted-by":"crossref","first-page":"594","DOI":"10.1016\/j.patrec.2020.08.020","article-title":"Fused 3-D spectral-spatial deep neural networks and spectral clustering for hyperspectral image classification","volume":"138","author":"Sellami","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_303","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s11831-017-9239-y","article-title":"Soft Computing Techniques for Land Use and Land Cover Monitoring with Multispectral Remote Sensing Images: A Review","volume":"26","author":"Thyagharajan","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_304","doi-asserted-by":"crossref","unstructured":"Kwan, C. (2019). Methods and challenges using multispectral and hyperspectral images for practical change detection applications. Information, 10.","DOI":"10.3390\/info10110353"},{"key":"ref_305","doi-asserted-by":"crossref","first-page":"4633","DOI":"10.1007\/s11831-021-09548-z","article-title":"A Review on SAR Image and its Despeckling","volume":"28","author":"Singh","year":"2021","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_306","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.neucom.2016.09.028","article-title":"SAR despeckling via classification-based nonlocal and local sparse representation","volume":"219","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_307","doi-asserted-by":"crossref","unstructured":"Wang, G., Bo, F., Chen, X., Lu, W., Hu, S., and Fang, J. (2022). A collaborative despeckling method for SAR images based on texture classification. Remote Sens., 14.","DOI":"10.3390\/rs14061465"},{"key":"ref_308","doi-asserted-by":"crossref","unstructured":"Choi, H., and Jeong, J. (2019). Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform. Remote Sens., 11.","DOI":"10.3390\/rs11101184"},{"key":"ref_309","doi-asserted-by":"crossref","unstructured":"Dalsasso, E., Yang, X., Denis, L., Tupin, F., and Yang, W. (2020). SAR Image Despeckling by Deep Neural Networks: From a Pre-Trained Model to an End-to-End Training Strategy. Remote Sens., 12.","DOI":"10.3390\/rs12162636"},{"key":"ref_310","doi-asserted-by":"crossref","first-page":"5200315","DOI":"10.1109\/TGRS.2020.3042694","article-title":"DeSpeckNet: Generalizing deep learning-based SAR image despeckling","volume":"60","author":"Mullissa","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_311","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TGRS.2014.2364525","article-title":"Adaptive Total Variation Regularization Based SAR Image Despeckling and Despeckling Evaluation Index","volume":"53","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_312","doi-asserted-by":"crossref","unstructured":"Muhadi, N.A., Abdullah, A.F., Bejo, S.K., Mahadi, M.R., and Mijic, A. (2020). The use of LiDAR-derived DEM in flood applications: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142308"},{"key":"ref_313","doi-asserted-by":"crossref","first-page":"6354","DOI":"10.1109\/TGRS.2017.2726901","article-title":"Fusion of hyperspectral and LiDAR data using sparse and low-rank component analysis","volume":"55","author":"Rasti","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_314","doi-asserted-by":"crossref","unstructured":"Zhou, L., Geng, J., and Jiang, W. (2022). Joint classification of hyperspectral and LiDAR data based on position-channel cooperative at-tention network. Remote Sens., 14.","DOI":"10.3390\/rs14143247"},{"key":"ref_315","doi-asserted-by":"crossref","unstructured":"Luo, S., Wang, C., Xi, X., Zeng, H., Li, D., Xia, S., and Wang, P. (2016). Fusion of airborne discrete-return LiDAR and hyperspectral data for land cover classification. Remote Sens., 8.","DOI":"10.3390\/rs8010003"},{"key":"ref_316","doi-asserted-by":"crossref","first-page":"290","DOI":"10.5589\/m13-038","article-title":"Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR\u2013SAR fusion using a random forest classifier","volume":"39","author":"Millard","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_317","doi-asserted-by":"crossref","first-page":"3453","DOI":"10.1109\/JSTARS.2018.2868119","article-title":"A Machine-Learning Approach to PolInSAR and LiDAR Data Fusion for Improved Tropical Forest Canopy Height Estimation Using NASA AfriSAR Campaign Data","volume":"11","author":"Pourshamsi","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_318","doi-asserted-by":"crossref","unstructured":"Seo, D.K., Kim, Y.H., Eo, Y.D., Lee, M.H., and Park, W.Y. (2018). Fusion of SAR and Multispectral Images Using Random Forest Regression for Change Detection. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7100401"},{"key":"ref_319","doi-asserted-by":"crossref","unstructured":"Zhang, H., Shen, H., Yuan, Q., and Guan, X. (2022). Multispectral and SAR image fusion based on Laplacian pyramid and sparse representation. Remote Sens., 14.","DOI":"10.3390\/rs14040870"},{"key":"ref_320","doi-asserted-by":"crossref","unstructured":"Hu, J., Hong, D., Wang, Y., and Zhu, X.X. (2019). A Comparative Review of Manifold Learning Techniques for Hyperspectral and Polarimetric SAR Image Fusion. Remote Sens., 11.","DOI":"10.3390\/rs11060681"},{"key":"ref_321","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1109\/LGRS.2017.2668299","article-title":"Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network","volume":"14","author":"Palsson","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_322","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3231215","article-title":"A Band Divide-and-Conquer Multispectral and Hyperspectral Image Fusion Method","volume":"60","author":"Sun","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_323","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2018.2890023","article-title":"Multisource and multitemporal data fusion in remote sensing a comprehensive review of the state of the art","volume":"7","author":"Ghamisi","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_324","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1109\/JPROC.2015.2462751","article-title":"Challenges and opportunities of multi-modality and data fusion in remote sensing","volume":"103","author":"Prasad","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_325","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.arcontrol.2021.03.003","article-title":"A comprehensive review of hyperspectral data fusion with LiDAR and SAR data","volume":"51","author":"Kahraman","year":"2021","journal-title":"Ann. Rev. Contr."},{"key":"ref_326","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2014.2361734","article-title":"A Critical Comparison Among Pansharpening Algorithms","volume":"53","author":"Vivone","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_327","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1002\/sam.11181","article-title":"A graph-based approach to find teleconnections in climate data","volume":"6","author":"Kawale","year":"2013","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"ref_328","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","article-title":"On hyperparameter optimization of machine learning algorithms: Theory and practice","volume":"415","author":"Yang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_329","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv."},{"key":"ref_330","unstructured":"Hern\u00e1ndez, A.M., Nieuwenhuyse, I.V., and Rojas-Gonzalez, S. (2021). A survey on multi-objective hyperparameter optimization algo-rithms for machine learning. arXiv."},{"key":"ref_331","first-page":"102520","article-title":"Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing","volume":"103","author":"Kakogeorgiou","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_332","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014, January 5\u201312). Visualizing and understanding convolutional networks. Proceedings of the European Conference of Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_333","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_334","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., and Guestrin, C. (2016). \u201cWhy Should I Trust You?\u201d: Explaining the Predictions of Any Classifier, Association for Computing Machinery.","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref_335","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., and Pradhan, B. (2021). Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI). Sensors, 21.","DOI":"10.3390\/s21144738"},{"key":"ref_336","doi-asserted-by":"crossref","unstructured":"Temenos, A., Tzortzis, I.N., Kaselimi, M., Rallis, I., Doulamis, A., and Doulamis, N. (2022). Novel Insights in Spatial Epidemiology Utilizing Explainable AI (XAI) and Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14133074"},{"key":"ref_337","first-page":"102869","article-title":"Explainable AI for earth observation: A review including societal and regulatory perspectives","volume":"112","author":"Gevaert","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf. ITC J."},{"key":"ref_338","first-page":"265","article-title":"Quantification of cave geomorphological characteristics based on multi source point cloud data interoperability","volume":"63","author":"Vassilakis","year":"2022","journal-title":"Zeitschr. Geomorphol."},{"key":"ref_339","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_340","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., and Lucey, S. (2019, January 15\u201320). PointNetLK: Robust & efficient point cloud registration using PointNet. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00733"},{"key":"ref_341","doi-asserted-by":"crossref","unstructured":"Ding, L., Cai, Y., Zhang, J., Gao, Y., Wang, J., Zheng, C., Lei, L., and Ma, A. (2021, January 11\u201316). PointNet: Learning point representation for high-resolution remote sensing imagery land-cover classification. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554009"},{"key":"ref_342","doi-asserted-by":"crossref","first-page":"4340","DOI":"10.1109\/TGRS.2020.3016820","article-title":"More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification","volume":"59","author":"Hong","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_343","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_344","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_345","doi-asserted-by":"crossref","unstructured":"Hazirbas, C., Ma, L., Domokos, C., and Cremers, D. (2016, January 20\u201324). FuseNet: Incorporating depth into semantic segmentation via fusion-based cnn architecture. Proceedings of the Asian Conference on Computer Vision, Taipei, Taiwan.","DOI":"10.1007\/978-3-319-54181-5_14"},{"key":"ref_346","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_347","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.neucom.2018.09.038","article-title":"Recent advances in convolutional neural network acceleration","volume":"323","author":"Zhang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_348","unstructured":"Mathieu, M., Henaff, M., and Le Cun, Y. (2013). Fast training of convolutional networks through FFTs. arXiv."},{"key":"ref_349","doi-asserted-by":"crossref","unstructured":"Jaderberg, M., Vedaldi, A., and Zisserman, A. (2014). Speeding up convolutional neural networks with low rank expansions. arXiv.","DOI":"10.5244\/C.28.88"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6017\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:28:01Z","timestamp":1760146081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,27]]},"references-count":349,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236017"],"URL":"https:\/\/doi.org\/10.3390\/rs14236017","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,27]]}}}