{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:47:34Z","timestamp":1780609654525,"version":"3.54.1"},"reference-count":75,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,30]],"date-time":"2020-03-30T00:00:00Z","timestamp":1585526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017M1A3A3A02015981"],"award-info":[{"award-number":["NRF-2017M1A3A3A02015981"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1D1A1B03028129"],"award-info":[{"award-number":["NRF-2017R1D1A1B03028129"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003629","name":"Korea Meteorological Administration","doi-asserted-by":"publisher","award":["KMIPA 2017-7010"],"award-info":[{"award-number":["KMIPA 2017-7010"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012261","name":"Ministry of the Interior and Safety","doi-asserted-by":"publisher","award":["2019-MOIS32-015"],"award-info":[{"award-number":["2019-MOIS32-015"]}],"id":[{"id":"10.13039\/501100012261","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003621","name":"Ministry of Science, ICT and Future Planning","doi-asserted-by":"publisher","award":["IITP-2019-2018-0-01424"],"award-info":[{"award-number":["IITP-2019-2018-0-01424"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas\u2014Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea\u2014were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%\u201395 % for both Concord and Lake Tapps and 80%\u201384% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.<\/jats:p>","DOI":"10.3390\/rs12071097","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1097","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery"],"prefix":"10.3390","volume":"12","author":[{"given":"Junghee","family":"Lee","sequence":"first","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1907-8006","authenticated-orcid":false,"given":"Daehyeon","family":"Han","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minso","family":"Shin","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4506-6877","authenticated-orcid":false,"given":"Jungho","family":"Im","sequence":"additional","affiliation":[{"name":"School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junghye","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Management Engineering, UNIST, Ulsan 44949, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lindi J.","family":"Quackenbush","sequence":"additional","affiliation":[{"name":"Department of Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/S0921-8181(00)00021-7","article-title":"The impact of land use\u2014Land cover changes due to urbanization on surface microclimate and hydrology: A satellite perspective","volume":"25","author":"Carlson","year":"2000","journal-title":"Glob. Planet. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10661-007-9699-x","article-title":"Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area","volume":"136","author":"Geymen","year":"2008","journal-title":"Environ. Monit. Assess."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5721\/EuJRS20124501","article-title":"Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics","volume":"45","author":"Fichera","year":"2012","journal-title":"Eur. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2012.10.010","article-title":"Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record","volume":"128","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.rse.2015.12.040","article-title":"A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery","volume":"175","author":"Fu","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-use\/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1080\/15481603.2019.1613803","article-title":"Using the random forest algorithm to integrate hydroacoustic data with satellite images to improve the mapping of shallow nearshore benthic features in a marine protected area in Jamaica","volume":"56","author":"McLaren","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2998","DOI":"10.1175\/1520-0493(2001)129<2998:RBCPAC>2.0.CO;2","article-title":"Relationship between Convective Precipitation and Cloud-to-Ground Lightning in the Iberian Peninsula","volume":"129","author":"Soriano","year":"2002","journal-title":"Mon. Weather Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1046","DOI":"10.1080\/15481603.2019.1611024","article-title":"Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: The Choc\u00f3-Darien of South America and the Great Basin of North America","volume":"56","author":"Fagua","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in neural information processing systems, Lake Tahoe, NV, USA."},{"key":"ref_14","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/15481603.2018.1538620","article-title":"Bathymetry retrieval from optical images with spatially distributed support vector machines","volume":"56","author":"Wang","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1080\/15481603.2019.1623003","article-title":"A probabilistic fusion of a support vector machine and a joint sparsity model for hyperspectral imagery classification","volume":"56","author":"Gao","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1080\/15481603.2018.1502910","article-title":"Vegetation species mapping in a coastal-dune ecosystem using high resolution satellite imagery","volume":"56","author":"Marcello","year":"2019","journal-title":"GIScience Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2019.09.009","article-title":"Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images","volume":"157","author":"Yoo","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2019.03.015","article-title":"A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem","volume":"151","author":"Mohammadimanesh","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing Fully Convolutional Networks, Random Forest, Support Vector Machine, and Patch-based Deep Convolutional Neural Networks for Object-based Wetland Mapping using Images from small Unmanned Aircraft System","volume":"55","author":"Liu","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/15481603.2019.1658960","article-title":"Mapping Rice Paddies in Complex Landscapes with Convolutional Neural Networks and Phenological Metrics","volume":"57","author":"Zhao","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kim, M., Lee, J., and Im, J. (2018). Deep learning-based monitoring of overshooting cloud tops from geostationary satellite data. GIScience Remote Sens., 1\u201330.","DOI":"10.1080\/15481603.2018.1457201"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1080\/15481603.2017.1323377","article-title":"Deep learning in remote sensing scene classification: A data augmentation enhanced convolutional neural network framework","volume":"54","author":"Yu","year":"2017","journal-title":"GIScience Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical image computing and computer-assisted intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"111203","DOI":"10.1016\/j.rse.2019.05.022","article-title":"Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network","volume":"230","author":"Wieland","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.07.007","article-title":"TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation","volume":"156","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, H., Zhang, C., Zhang, S., and Atkinson, P.M. (2019). A hybrid OSVM-OCNN method for crop classification from fine spatial resolution remotely sensed imagery. Remote Sens., 11.","DOI":"10.3390\/rs11202370"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Guidici, D., and Clark, M.L. (2017). One-Dimensional convolutional neural network land-cover classification of multi-seasonal hyperspectral imagery in the San Francisco Bay Area, California. Remote Sens., 9.","DOI":"10.3390\/rs9060629"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2018.01.021","article-title":"Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models","volume":"145","author":"Marcos","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sarkar, D., Bali, R., and Sharma, T. (2018). Practical Machine Learning with Python, Apress.","DOI":"10.1007\/978-1-4842-3207-1"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4604","DOI":"10.1109\/JSTARS.2018.2880783","article-title":"Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs With Multitemporal Satellite Imagery","volume":"11","author":"Kim","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3030","DOI":"10.1109\/JSTARS.2018.2846178","article-title":"Deep convolutional neural network for complex wetland classification using optical remote sensing imagery","volume":"11","author":"Rezaee","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Benjdira, B., Bazi, Y., Koubaa, A., and Ouni, K. (2019). Unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images. Remote Sens., 11.","DOI":"10.3390\/rs11111369"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep transfer learning for few-shot sar image classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"S Garea, A., Heras, D.B., and Arg\u00fcello, F. (2019). TCANet for Domain Adaptation of Hyperspectral Images. Remote Sens., 11.","DOI":"10.3390\/rs11192289"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Bejiga, M.B., Melgani, F., and Beraldini, P. (2019). Domain Adversarial Neural Networks for Large-Scale Land Cover Classification. Remote Sens., 11.","DOI":"10.3390\/rs11101153"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Scherer, D., M\u00fcller, A., and Behnke, S. (2010, January 15\u201318). Evaluation of pooling operations in convolutional architectures for object recognition. Proceedings of the International conference on artificial neural networks, Thessaloniki, Greece.","DOI":"10.1007\/978-3-642-15825-4_10"},{"key":"ref_43","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_44","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_45","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wu, H., and Prasad, S. (2017). Convolutional recurrent neural networks forhyperspectral data classification. Remote Sens., 9.","DOI":"10.3390\/rs9030298"},{"key":"ref_47","unstructured":"K\u00f6ppen, W., and Geiger, R. (1936). Handbuch der klimatologie, Gebr\u00fcder Borntraeger."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1127\/0941-2948\/2006\/0130","article-title":"World map of the K\u00f6ppen-Geiger climate classification updated","volume":"15","author":"Kottek","year":"2006","journal-title":"Meteorol. Zeitschrift."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.14358\/PERS.71.11.1275","article-title":"Urban classification using full spectral information of Landsat ETM+ imagery in Marion County, Indiana","volume":"71","author":"Lu","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_50","first-page":"251","article-title":"Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan","volume":"18","author":"Butt","year":"2015","journal-title":"Egypt. J. Remote Sens. Sp. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.rse.2015.04.004","article-title":"Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations","volume":"164","author":"Ke","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1080\/2150704X.2017.1280200","article-title":"Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network","volume":"8","author":"Zhang","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5886","DOI":"10.1002\/2016GL069298","article-title":"Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite","volume":"43","author":"Yumimoto","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"4407","DOI":"10.1080\/01431161.2011.552923","article-title":"Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment","volume":"32","author":"Pontius","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, S., Li, C., Qiu, S., Gao, C., Zhang, F., Du, Z., and Liu, R. (2020). EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification. Remote Sens., 12.","DOI":"10.3390\/rs12010066"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhou, K., Ming, D., Lv, X., Fang, J., and Wang, M. (2019). CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data. Remote Sens., 11.","DOI":"10.3390\/rs11172065"},{"key":"ref_58","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_59","first-page":"2677","article-title":"An extension on \u201cstatistical comparisons of classifiers over multiple data sets\u201d for all pairwise comparisons","volume":"9","author":"Garcia","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Douzas, G., Bacao, F., Fonseca, J., and Khudinyan, M. (2019). Imbalanced Learning in Land Cover Classification: Improving Minority Classes\u2019 Prediction Accuracy Using the Geometric SMOTE Algorithm. Remote Sens., 11.","DOI":"10.3390\/rs11243040"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"V A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1080\/2150704X.2017.1333650","article-title":"Selective convolutional neural networks and cascade classifiers for remote sensing image classification","volume":"8","author":"Wan","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.neucom.2019.07.034","article-title":"Multi-head CNN--RNN for multi-time series anomaly detection: An industrial case study","volume":"363","author":"Canizo","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Carranza-Garc\u00eda, M., Garc\u00eda-Guti\u00e9rrez, J., and Riquelme, J.C. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11030274"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1007\/s10044-015-0452-8","article-title":"Editing training data for multi-label classification with the k-nearest neighbor rule","volume":"19","author":"Kanj","year":"2016","journal-title":"Pattern Anal. Appl."},{"key":"ref_66","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (July, January 26). Learning deep features for discriminative localization. Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1080\/01431161.2010.481681","article-title":"Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach","volume":"1","author":"He","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3390\/rs5010019","article-title":"Harmonizing and combining existing land cover\/land use datasets for cropland area monitoring at the African continental scale","volume":"5","author":"Vancutsem","year":"2013","journal-title":"Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, Z., Li, X., and Yeh, A.G.-O. (2019). Integration of convolutional neural networks and object-based post-classification refinement for land use and land cover mapping with optical and sar data. Remote Sens., 11.","DOI":"10.3390\/rs11060690"},{"key":"ref_70","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_71","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1080\/2150704X.2014.882526","article-title":"High-resolution landcover classification using Random Forest","volume":"5","author":"Hayes","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Li, S., Yao, Y., Hu, J., Liu, G., Yao, X., and Hu, J. (2018). An ensemble stacked convolutional neural network model for environmental event sound recognition. Appl. Sci., 8.","DOI":"10.3390\/app8071152"},{"key":"ref_74","first-page":"1","article-title":"DeepInsight: A methodology to transform a non-image data to an image for convolution neural network architecture","volume":"9","author":"Sharma","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.rse.2014.10.018","article-title":"Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery","volume":"156","author":"Senf","year":"2015","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1097\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:13:26Z","timestamp":1760174006000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1097"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,30]]},"references-count":75,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071097"],"URL":"https:\/\/doi.org\/10.3390\/rs12071097","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,30]]}}}