{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:46:06Z","timestamp":1776357966228,"version":"3.51.2"},"reference-count":179,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T00:00:00Z","timestamp":1696204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Research Partnership for Sustainable Development (SATREPS)","award":["JPMJSA1704"],"award-info":[{"award-number":["JPMJSA1704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing image scene classification with deep learning (DL) is a rapidly growing field that has gained significant attention in the past few years. While previous review papers in this domain have been confined to 2020, an up-to-date review to show the progression of research extending into the present phase is lacking. In this review, we explore the recent articles, providing a thorough classification of approaches into three main categories: Convolutional Neural Network (CNN)-based, Vision Transformer (ViT)-based, and Generative Adversarial Network (GAN)-based architectures. Notably, within the CNN-based category, we further refine the classification based on specific methodologies and techniques employed. In addition, a novel and rigorous meta-analysis is performed to synthesize and analyze the findings from 50 peer-reviewed journal articles to provide valuable insights in this domain, surpassing the scope of existing review articles. Our meta-analysis shows that the most adopted remote sensing scene datasets are AID (41 articles) and NWPU-RESISC45 (40). A notable paradigm shift is seen towards the use of transformer-based models (6) starting from 2021. Furthermore, we critically discuss the findings from the review and meta-analysis, identifying challenges and future opportunities for improvement in this domain. Our up-to-date study serves as an invaluable resource for researchers seeking to contribute to this growing area of research.<\/jats:p>","DOI":"10.3390\/rs15194804","type":"journal-article","created":{"date-parts":[[2023,10,2]],"date-time":"2023-10-02T11:56:49Z","timestamp":1696247809000},"page":"4804","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Deep Learning for Remote Sensing Image Scene Classification: A Review and Meta-Analysis"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-4848","authenticated-orcid":false,"given":"Aakash","family":"Thapa","sequence":"first","affiliation":[{"name":"School of Information, Computer and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3452-8845","authenticated-orcid":false,"given":"Teerayut","family":"Horanont","sequence":"additional","affiliation":[{"name":"School of Information, Computer and Communication Technology (ICT), Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5331-9897","authenticated-orcid":false,"given":"Bipul","family":"Neupane","sequence":"additional","affiliation":[{"name":"Earth Observation and AI Research Group, Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3053, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4875-2127","authenticated-orcid":false,"given":"Jagannath","family":"Aryal","sequence":"additional","affiliation":[{"name":"Earth Observation and AI Research Group, Department of Infrastructure Engineering, The University of Melbourne, Parkville, VIC 3053, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1109\/JPROC.2015.2449668","article-title":"Multimodal classification of remote sensing images: A review and future directions","volume":"103","author":"Tuia","year":"2015","journal-title":"Proc. IEEE"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10590","DOI":"10.1109\/TGRS.2020.3047447","article-title":"Learning deep cross-modal embedding networks for zero-shot remote sensing image scene classification","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1080\/01431161.2012.705443","article-title":"Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA","volume":"34","author":"Cheng","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1080\/01431161.2016.1171928","article-title":"Using convolutional features and a sparse autoencoder for land-use scene classification","volume":"37","author":"Othman","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"042005","DOI":"10.1117\/1.JRS.10.042005","article-title":"Sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images","volume":"10","author":"Kunlun","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2296","DOI":"10.1080\/01431161.2014.890762","article-title":"A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1007\/s11760-015-0804-2","article-title":"Land-use scene classification using multi-scale completed local binary patterns","volume":"10","author":"Chen","year":"2016","journal-title":"Signal, Image Video Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6281","DOI":"10.1080\/01431161.2018.1458346","article-title":"Land-use scene classification based on a CNN using a constrained extreme learning machine","volume":"39","author":"Weng","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/LGRS.2015.2478966","article-title":"Land-use scene classification in high-resolution remote sensing images using improved correlatons","volume":"12","author":"Qi","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26377","DOI":"10.1109\/ACCESS.2021.3057868","article-title":"Urban remote sensing scene recognition based on lightweight convolution neural network","volume":"9","author":"Xia","year":"2021","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1080\/01431169208904084","article-title":"Knowledge-based crop classification of a Landsat Thematic Mapper image","volume":"13","author":"Janssen","year":"1992","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1080\/10106049908542126","article-title":"Effectiveness of subpixel analysis in detecting and quantifying urban imperviousness from Landsat Thematic Mapper imagery","volume":"14","author":"Ji","year":"1999","journal-title":"Geocarto Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1109\/TGRS.2008.2010404","article-title":"Active learning methods for remote sensing image classification","volume":"47","author":"Tuia","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","first-page":"12","article-title":"What\u2019s wrong with pixels? Some recent developments interfacing remote sensing and GIS","volume":"4","author":"Blaschke","year":"2001","journal-title":"Z. Geoinformationssyst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G. (2008). Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications, Springer Science & Business Media.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/S0924-2716(02)00162-4","article-title":"A comparison of three image-object methods for the multiscale analysis of landscape structure","volume":"57","author":"Hay","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1453","DOI":"10.1080\/01431160903475266","article-title":"Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine","volume":"31","author":"Li","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic object-based image analysis\u2013towards a new paradigm","volume":"87","author":"Blaschke","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"Blaschke, T., Burnett, C., and Pekkarinen, A. (2004). Remote Sensing Image Analysis: Including the Spatial Domain, Springer."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote sensing image scene classification meets deep learning: Challenges, methods, benchmarks, and opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/LGRS.2017.2731997","article-title":"Remote sensing image scene classification using bag of convolutional features","volume":"14","author":"Cheng","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"095064","DOI":"10.1117\/1.JRS.9.095064","article-title":"Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images","volume":"9","author":"Zhong","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., and Guo, Y. (2014, January 6\u201312). Multi-level adaptive active learning for scene classification. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Part VII.","DOI":"10.1007\/978-3-319-10584-0_16"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"11179","DOI":"10.1109\/JSTARS.2021.3122464","article-title":"Global context-based multilevel feature fusion networks for multilabel remote sensing image scene classification","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2008, January 12\u201315). Comparing SIFT descriptors and Gabor texture features for classification of remote sensed imagery. Proceedings of the 2008 15th IEEE International Conference on Image Processing, San Diego, CA, USA.","DOI":"10.1109\/ICIP.2008.4712139"},{"key":"ref_28","unstructured":"dos Santos, J.A., Penatti, O.A., and Torres, R.d.S. (2010, January 17\u201321). Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification. Proceedings of the International Conference on Computer Vision Theory and Applications, Angers, France."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1899","DOI":"10.1109\/JSTARS.2012.2228254","article-title":"Indexing of remote sensing images with different resolutions by multiple features","volume":"6","author":"Luo","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and Dos Santos, J.A. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.cviu.2007.07.005","article-title":"Unsupervised segmentation of natural images via lossy data compression","volume":"110","author":"Yang","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/TPAMI.2011.231","article-title":"CPMC: Automatic object segmentation using constrained parametric min-cuts","volume":"34","author":"Carreira","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L. (2019). Remote sensing image scene classification using CNN-CapsNet. Remote Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.patcog.2012.07.017","article-title":"Scene classification using a multi-resolution bag-of-features model","volume":"46","author":"Zhou","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/LGRS.2015.2513443","article-title":"Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery","volume":"13","author":"Zhu","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"4620","DOI":"10.1109\/JSTARS.2014.2339842","article-title":"Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model","volume":"7","author":"Zhao","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jogin, M., Madhulika, M.S., Divya, G.D., Meghana, R.K., and Apoorva, S. (2018, January 18\u201319). Feature extraction using convolution neural networks (CNN) and deep learning. Proceedings of the 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India.","DOI":"10.1109\/RTEICT42901.2018.9012507"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Scarpa, G., Gargiulo, M., Mazza, A., and Gaetano, R. (2018). A CNN-based fusion method for feature extraction from sentinel data. Remote Sens., 10.","DOI":"10.3390\/rs10020236"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2017.2723009","article-title":"Places: A 10 million image database for scene recognition","volume":"40","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Thapa, A., Neupane, B., and Horanont, T. (2022, January 2\u20137). Object vs Pixel-based Flood\/Drought Detection in Paddy Fields using Deep Learning. Proceedings of the 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI), Kanazawa, Japan.","DOI":"10.1109\/IIAIAAI55812.2022.00095"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Thapa, A., Horanont, T., and Neupane, B. (2022). Parcel-Level Flood and Drought Detection for Insurance Using Sentinel-2A, Sentinel-1 SAR GRD and Mobile Images. Remote Sens., 14.","DOI":"10.3390\/rs14236095"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1007\/s41651-019-0039-9","article-title":"Scene classification of high-resolution remotely sensed image based on ResNet","volume":"3","author":"Wang","year":"2019","journal-title":"J. Geovisualization Spat. Anal."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1414","DOI":"10.1109\/TNNLS.2020.3042276","article-title":"Looking closer at the scene: Multiscale representation learning for remote sensing image scene classification","volume":"33","author":"Wang","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1109\/TGRS.2019.2909695","article-title":"Scale-free convolutional neural network for remote sensing scene classification","volume":"57","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.isprsjprs.2018.01.023","article-title":"Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification","volume":"138","author":"Anwer","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4119","DOI":"10.1080\/01431161.2016.1207266","article-title":"Scene classification using multi-scale deeply described visual words","volume":"37","author":"Zhao","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_48","unstructured":"Sitaula, C., KC, S., and Aryal, J. (2023). Enhanced Multi-level Features for Very High Resolution Remote Sensing Scene Classification. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_52","unstructured":"Xia, G.S., Yang, W., Delon, J., Gousseau, Y., Sun, H., and Ma\u00eetre, H. (2010, January 5\u20137). Structural High-resolution Satellite Image Indexing. Proceedings of the ISPRS TC VII Symposium\u2014100 Years ISPRS, Vienna, Austria."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep learning based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5616614","DOI":"10.1109\/TGRS.2022.3140485","article-title":"Semi-supervised remote-sensing image scene classification using representation consistency siamese network","volume":"60","author":"Miao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2018.01.004","article-title":"PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval","volume":"145","author":"Zhou","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","article-title":"Scene classification with recurrent attention of VHR remote sensing images","volume":"57","author":"Wang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","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_58","unstructured":"Li, H., Tao, C., Wu, Z., Chen, J., Gong, J., and Deng, M. (2017). RSI-CB: A large scale remote sensing image classification benchmark via crowdsource data. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/TGRS.2017.2692281","article-title":"Domain adaptation network for cross-scene classification","volume":"55","author":"Othman","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"062032","DOI":"10.1088\/1742-6596\/1087\/6\/062032","article-title":"Feature extraction and image recognition with convolutional neural networks","volume":"1087","author":"Liu","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Co\u015fkun, M., U\u00e7ar, A., Yildirim, \u00d6., and Demir, Y. (2017, January 15\u201317). Face recognition based on convolutional neural network. Proceedings of the 2017 International Conference on Modern Electrical and Energy Systems (MEES), Kremenchuk, Ukraine.","DOI":"10.1109\/MEES.2017.8248937"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Guo, P., Valanarasu, J.M.J., Wang, P., Zhou, J., Jiang, S., and Patel, V.M. (October, January 27). Over-and-under complete convolutional rnn for mri reconstruction. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France. Part VI.","DOI":"10.1007\/978-3-030-87231-1_2"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_64","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010, January 22\u201327). Large-scale machine learning with stochastic gradient descent. Proceedings of the COMPSTAT\u20192010: 19th International Conference on Computational Statistics, Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_67","first-page":"487","article-title":"Learning deep features for scene recognition using places database","volume":"27","author":"Zhou","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"4104","DOI":"10.1109\/JSTARS.2017.2705419","article-title":"Aggregating rich hierarchical features for scene classification in remote sensing imagery","volume":"10","author":"Wang","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_69","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_70","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_71","first-page":"161","article-title":"A Hierarchical Approach to Remote Sensing Scene Classification","volume":"90","author":"Sen","year":"2022","journal-title":"PFG- Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Al Rahhal, M.M., Bazi, Y., Abdullah, T., Mekhalfi, M.L., AlHichri, H., and Zuair, M. (2018). Learning a multi-branch neural network from multiple sources for knowledge adaptation in remote sensing imagery. Remote Sens., 10.","DOI":"10.3390\/rs10121890"},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"165356","DOI":"10.1016\/j.ijleo.2020.165356","article-title":"Remote sensing image scene classification using CNN-MLP with data augmentation","volume":"221","author":"Shawky","year":"2020","journal-title":"Optik"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"18195","DOI":"10.1109\/ACCESS.2021.3052977","article-title":"A multi-level convolution pyramid semantic fusion framework for high-resolution remote sensing image scene classification and annotation","volume":"9","author":"Sun","year":"2021","journal-title":"IEEE Access"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"8639367","DOI":"10.1155\/2018\/8639367","article-title":"A two-stream deep fusion framework for high-resolution aerial scene classification","volume":"2018","author":"Yu","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1080\/2150704X.2017.1415477","article-title":"Parallel multi-stage features fusion of deep convolutional neural networks for aerial scene classification","volume":"9","author":"Ye","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Dong, R., Xu, D., Jiao, L., Zhao, J., and An, J. (2020). A fast deep perception network for remote sensing scene classification. Remote Sens., 12.","DOI":"10.3390\/rs12040729"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/TNNLS.2017.2716952","article-title":"Broad learning system: An effective and efficient incremental learning system without the need for deep architecture","volume":"29","author":"Chen","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_82","unstructured":"M\u00e4enp\u00e4\u00e4, T., and Pietik\u00e4inen, M. (2005). Handbook of Pattern Recognition and Computer Vision, World Scientific."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Liu, F. (2018). Dense connectivity based two-stream deep feature fusion framework for aerial scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10071158"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Huang, H., and Xu, K. (2019). Combing triple-part features of convolutional neural networks for scene classification in remote sensing. Remote Sens., 11.","DOI":"10.3390\/rs11141687"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 18\u201319). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1109\/JSTARS.2020.2996760","article-title":"Object-guided remote sensing image scene classification based on joint use of deep-learning classifier and detector","volume":"13","author":"Yang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Part V.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Petrovska, B., Atanasova-Pacemska, T., Corizzo, R., Mignone, P., Lameski, P., and Zdravevski, E. (2020). Aerial scene classification through fine-tuning with adaptive learning rates and label smoothing. Appl. Sci., 10.","DOI":"10.3390\/app10175792"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Smith, L.N. (2017, January 24\u201331). Cyclical learning rates for training neural networks. Proceedings of the 2017 IEEE winter conference on applications of computer vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.58"},{"key":"ref_91","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks. arXiv."},{"key":"ref_92","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","volume":"28","author":"Han","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1487","DOI":"10.13031\/2013.34909","article-title":"Detection of overparameterization and overfitting in an automatic calibration of SWAT","volume":"53","author":"Whittaker","year":"2010","journal-title":"Trans. ASABE"},{"key":"ref_94","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"3345","DOI":"10.3934\/mbe.2019167","article-title":"A full convolutional network based on DenseNet for remote sensing scene classification","volume":"16","author":"Zhang","year":"2019","journal-title":"Math. Biosci. Eng."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Yu, D., Xu, Q., Guo, H., Zhao, C., Lin, Y., and Li, D. (2020). An efficient and lightweight convolutional neural network for remote sensing image scene classification. Sensors, 20.","DOI":"10.3390\/s20071999"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., RoyChowdhury, A., and Maji, S. (2015, January 11\u201318). Bilinear CNN models for fine-grained visual recognition. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.170"},{"key":"ref_99","first-page":"698","article-title":"Deep metric learning method for high resolution remote sensing image scene classification","volume":"48","author":"Lihua","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, J., Tian, J., Zhuo, L., and Zhang, J. (2020). Residual dense network based on channel-spatial attention for the scene classification of a high-resolution remote sensing image. Remote Sens., 12.","DOI":"10.3390\/rs12111887"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Chen, L., Zhang, H., Xiao, J., Nie, L., Shao, J., Liu, W., and Chua, T.S. (2017, January 21\u201326). Sca-cnn: Spatial and channel-wise attention in convolutional networks for image captioning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.667"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1647","DOI":"10.1109\/LGRS.2019.2949253","article-title":"Combining multilevel features for remote sensing image scene classification with attention model","volume":"17","author":"Ji","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"9530","DOI":"10.1109\/JSTARS.2021.3109661","article-title":"A multiscale attention network for remote sensing scene images classification","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Shen, J., Zhang, T., Wang, Y., Wang, R., Wang, Q., and Qi, M. (2021). A dual-model architecture with grouping-attention-fusion for remote sensing scene classification. Remote Sens., 13.","DOI":"10.3390\/rs13030433"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"23009","DOI":"10.1007\/s11042-020-08713-z","article-title":"Multi-view feature learning for VHR remote sensing image classification","volume":"80","author":"Guo","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1926","DOI":"10.1109\/LGRS.2020.3011405","article-title":"Remote sensing image scene classification based on an enhanced attention module","volume":"18","author":"Zhao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_108","first-page":"1","article-title":"MINet: Multilevel inheritance network-based aerial scene classification","volume":"19","author":"Hu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.1109\/JSTARS.2019.2919317","article-title":"A lightweight and discriminative model for remote sensing scene classification with multidilation pooling module","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1109\/TNNLS.2019.2920374","article-title":"Skip-connected covariance network for remote sensing scene classification","volume":"31","author":"He","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","article-title":"Scene classification via a gradient boosting random convolutional network framework","volume":"54","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_113","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_114","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_115","unstructured":"Ba, J.L., Kiros, J.R., and Hinton, G.E. (2016). Layer normalization. arXiv."},{"key":"ref_116","unstructured":"Hendrycks, D., and Gimpel, K. (2016). Gaussian error linear units (gelus). arXiv."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Bazi, Y., Bashmal, L., Rahhal, M.M.A., Dayil, R.A., and Ajlan, N.A. (2021). Vision transformers for remote sensing image classification. Remote Sens., 13.","DOI":"10.3390\/rs13030516"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Bashmal, L., Bazi, Y., and Al Rahhal, M. (2021, January 11\u201316). Deep vision transformers for remote sensing scene classification. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553684"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1109\/JSTARS.2022.3230835","article-title":"Vision transformer with contrastive learning for remote sensing image scene classification","volume":"16","author":"Bi","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_120","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_121","first-page":"5618715","article-title":"Vision transformer: An excellent teacher for guiding small networks in remote sensing image scene classification","volume":"60","author":"Xu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"6004405","DOI":"10.1109\/LGRS.2023.3266008","article-title":"A Local-global Interactive Vision Transformer for Aerial Scene Classification","volume":"20","author":"Peng","year":"2023","journal-title":"IEEE Geosci. Remote. Sens. Lett."},{"key":"ref_123","first-page":"5626915","article-title":"EMTCAL: Efficient multiscale transformer and cross-level attention learning for remote sensing scene classification","volume":"60","author":"Tang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhao, H., and Li, J. (2021). TRS: Transformers for remote sensing scene classification. Remote Sens., 13.","DOI":"10.3390\/rs13204143"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"6516005","DOI":"10.1109\/LGRS.2022.3205417","article-title":"MFST: A Multi-Level Fusion Network for Remote Sensing Scene Classification","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"15473","DOI":"10.1038\/s41598-022-19831-z","article-title":"Transformer based on channel-spatial attention for accurate classification of scenes in remote sensing image","volume":"12","author":"Guo","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_128","first-page":"2672","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Aryal, J. (2021). Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040808"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/LGRS.2017.2752750","article-title":"MARTA GANs: Unsupervised representation learning for remote sensing image classification","volume":"14","author":"Lin","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/2150704X.2018.1453173","article-title":"Remote sensing image scene classification based on generative adversarial networks","volume":"9","author":"Xu","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_132","first-page":"971","article-title":"Self-normalizing neural networks","volume":"30","author":"Klambauer","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1109\/LGRS.2018.2890413","article-title":"SiftingGAN: Generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro","volume":"16","author":"Ma","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1080\/2150704X.2020.1746854","article-title":"An improved unsupervised representation learning generative adversarial network for remote sensing image scene classification","volume":"11","author":"Wei","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"54135","DOI":"10.1109\/ACCESS.2020.2981358","article-title":"Semi-supervised representation learning for remote sensing image classification based on generative adversarial networks","volume":"8","author":"Yan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.1109\/LGRS.2019.2960026","article-title":"Multilayer feature fusion network for scene classification in remote sensing","volume":"17","author":"Xu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"5751","DOI":"10.1109\/TNNLS.2021.3071369","article-title":"Deep feature aggregation framework driven by graph convolutional network for scene classification in remote sensing","volume":"33","author":"Xu","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_138","first-page":"3859","article-title":"Dynamic routing between capsules","volume":"30","author":"Sabour","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Wang, C., Wu, Y., Wang, Y., and Chen, Y. (2021). Scene recognition using deep softpool capsule network based on residual diverse branch block. Sensors, 21.","DOI":"10.3390\/s21165575"},{"key":"ref_140","first-page":"6505105","article-title":"Pairwise comparison network for remote-sensing scene classification","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0004-3702(96)00034-3","article-title":"Solving the multiple instance problem with axis-parallel rectangles","volume":"89","author":"Dietterich","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"5629217","DOI":"10.1109\/TGRS.2022.3201755","article-title":"All Grains, One Scheme (AGOS): Learning Multigrain Instance Representation for Aerial Scene Classification","volume":"60","author":"Bi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"2661231","DOI":"10.1155\/2022\/2661231","article-title":"CAW: A Remote-Sensing Scene Classification Network Aided by Local Window Attention","volume":"2022","author":"Wang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_144","first-page":"8020305","article-title":"When CNNs meet vision transformer: A joint framework for remote sensing scene classification","volume":"19","author":"Deng","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_145","first-page":"8011605","article-title":"Remote sensing image scene classification based on global\u2013local dual-branch structure model","volume":"19","author":"Xu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.isprsjprs.2017.11.004","article-title":"A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification","volume":"145","author":"Han","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"28746","DOI":"10.1109\/ACCESS.2020.2968771","article-title":"Remote sensing scene classification based on multi-structure deep features fusion","volume":"8","author":"Xue","year":"2020","journal-title":"IEEE Access"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"5843816","DOI":"10.1155\/2021\/5843816","article-title":"Satellite and scene image classification based on transfer learning and fine tuning of ResNet50","volume":"2021","author":"Shabbir","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_149","first-page":"6003105","article-title":"Effective multiscale residual network with high-order feature representation for optical remote sensing scene classification","volume":"19","author":"Li","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1007\/s11760-020-01801-5","article-title":"A very high-resolution scene classification model using transfer deep CNNs based on saliency features","volume":"15","author":"Shawky","year":"2021","journal-title":"Signal, Image Video Process."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"170408","DOI":"10.1016\/j.ijleo.2022.170408","article-title":"Extracting feature fusion and co-saliency clusters using transfer learning techniques for improving remote sensing scene classification","volume":"273","author":"Aljabri","year":"2023","journal-title":"Optik"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TIFS.2016.2569061","article-title":"Discriminant correlation analysis: Real-time feature level fusion for multimodal biometric recognition","volume":"11","author":"Haghighat","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1109\/LGRS.2018.2880136","article-title":"Deep network ensembles for aerial scene classification","volume":"16","author":"Dede","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_154","unstructured":"Huang, G., Li, Y., Pleiss, G., Liu, Z., Hopcroft, J.E., and Weinberger, K.Q. (2017). Snapshot ensembles: Train 1, get m for free. arXiv."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"1540","DOI":"10.1109\/LGRS.2019.2902675","article-title":"A combined deep learning model for the scene classification of high-resolution remote sensing image","volume":"16","author":"Dong","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_156","unstructured":"Rachmadi, R.F., and Purnama, K. (2014, January 22). Large-Scale Scene Classification Using Gist Feature. Proceedings of the Seminar on Intelligent Technology and Its Application, Surabaya, Indonesia."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1007\/s11063-021-10463-4","article-title":"Compact deep color features for remote sensing scene classification","volume":"53","author":"Anwer","year":"2021","journal-title":"Neural Process. Lett."},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1007\/s12524-021-01310-z","article-title":"High-resolution remote sensing image scene classification by merging multilevel features of convolutional neural networks","volume":"49","author":"Zhang","year":"2021","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"3502105","DOI":"10.1109\/LGRS.2023.3249791","article-title":"Interclass Similarity Transfer for Imbalanced Aerial Scene Classification","volume":"20","author":"Jing","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_160","first-page":"991","article-title":"220 band aviris hyperspectral image data set: June 12, 1992 indian pine test site 3","volume":"10","author":"Baumgardner","year":"2015","journal-title":"Purdue Univ. Res. Repos."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Khalid, M.J., Irfan, M., Ali, T., Gull, M., Draz, U., Glowacz, A., Sulowicz, M., Dziechciarz, A., AlKahtani, F.S., and Hussain, S. (2020). Integration of discrete wavelet transform, DBSCAN, and classifiers for efficient content based image retrieval. Electronics, 9.","DOI":"10.3390\/electronics9111886"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Kaur, P., Khehra, B.S., and Mavi, E.B.S. (2021, January 9\u201311). Data augmentation for object detection: A review. Proceedings of the 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Lansing, MI, USA.","DOI":"10.1109\/MWSCAS47672.2021.9531849"},{"key":"ref_163","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_164","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). {TensorFlow}: A system for {Large-Scale} machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_165","unstructured":"Chollet, F. (2021). Deep Learning with Python, Simon and Schuster."},{"key":"ref_166","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_167","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_168","unstructured":"Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv."},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Botev, A., Lever, G., and Barber, D. (2017, January 14\u201319). Nesterov\u2019s accelerated gradient and momentum as approximations to regularised update descent. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966082"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"4500","DOI":"10.1109\/TNNLS.2019.2955777","article-title":"diffGrad: An optimization method for convolutional neural networks","volume":"31","author":"Dubey","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_171","unstructured":"Steinwart, I., and Christmann, A. (2008). Support Vector Machines, Springer Science & Business Media."},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Hou, X., and Zhang, L. (2007, January 17\u201322). Saliency detection: A spectral residual approach. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383267"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Risojevi\u0107, V., and Stojni\u0107, V. (2021). Do we still need ImageNet pre-training in remote sensing scene classification?. arXiv.","DOI":"10.5194\/isprs-archives-XLIII-B3-2022-1399-2022"},{"key":"ref_174","unstructured":"Koch, G., Zemel, R., and Salakhutdinov, R. (2015, January 6\u201311). Siamese neural networks for one-shot image recognition. Proceedings of the ICML Deep Learning Workshop, Lille, France."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H., and Hospedales, T.M. (2018, January 18\u201322). Learning to compare: Relation network for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00131"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Li, X., Pu, F., Yang, R., Gui, R., and Xu, X. (2020). AMN: Attention metric network for one-shot remote sensing image scene classification. Remote Sens., 12.","DOI":"10.3390\/rs12244046"},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"19891","DOI":"10.1109\/ACCESS.2020.3044192","article-title":"Few-shot scene classification with multi-attention deepemd network in remote sensing","volume":"9","author":"Yuan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2022.07.013","article-title":"Task-specific contrastive learning for few-shot remote sensing image scene classification","volume":"191","author":"Zeng","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"6983","DOI":"10.1109\/TGRS.2020.3027387","article-title":"RS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification","volume":"59","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4804\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:04:11Z","timestamp":1760130251000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4804"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,2]]},"references-count":179,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194804"],"URL":"https:\/\/doi.org\/10.3390\/rs15194804","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,2]]}}}