{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T21:43:33Z","timestamp":1780350213133,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T00:00:00Z","timestamp":1605744000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772397"],"award-info":[{"award-number":["61772397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key R&amp;D Program of China","award":["2016YFE0200400"],"award-info":[{"award-number":["2016YFE0200400"]}]},{"DOI":"10.13039\/501100013253","name":"Open Research Fund of Key Laboratory of Digital Earth Science","doi-asserted-by":"publisher","award":["2019LDE005"],"award-info":[{"award-number":["2019LDE005"]}],"id":[{"id":"10.13039\/501100013253","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The fusion of multi-spectral and synthetic aperture radar (SAR) images could retain the advantages of each data, hence benefiting accurate land cover classification. However, some current image fusion methods face the challenge of producing unexpected noise. To overcome the aforementioned problem, this paper proposes a novel fusion method based on weighted median filter and Gram\u2013Schmidt transform. In the proposed method, Sentinel-2A images and GF-3 images are respectively subjected to different preprocessing processes. Since weighted median filter does not strongly blur edges while filtering, it is applied to Sentinel-2A images for reducing noise. The processed Sentinel images are then transformed by Gram\u2013Schmidt with GF-3 images. Two popular methods, principal component analysis method and traditional Gram\u2013Schmidt transform, are used as the comparison methods in the experiment. In addition, random forest, a powerful ensemble model, is adopted as the land cover classifier due to its fast training speed and excellent classification performance. The overall accuracy, Kappa coefficient and classification map of the random forest are used as the evaluation criteria of the fusion method. Experiments conducted on five datasets demonstrate the superiority of the proposed method in both objective metrics and visual impressions. The experimental results indicate that the proposed method can improve the overall accuracy by up to 5% compared to using the original Sentinel-2A and has the potential to improve the satellite-based land cover classification accuracy.<\/jats:p>","DOI":"10.3390\/rs12223801","type":"journal-article","created":{"date-parts":[[2020,11,19]],"date-time":"2020-11-19T10:46:26Z","timestamp":1605782786000},"page":"3801","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A Novel Image Fusion Method of Multi-Spectral and SAR Images for Land Cover Classification"],"prefix":"10.3390","volume":"12","author":[{"given":"Yinghui","family":"Quan","sequence":"first","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingping","family":"Tong","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1907-2664","authenticated-orcid":false,"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-6702","authenticated-orcid":false,"given":"Gabriel","family":"Dauphin","sequence":"additional","affiliation":[{"name":"Laboratory of Information Processing and Transmission, L2TI, Institut Galil\u00e9e, University Paris XIII, 93430 Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengdao","family":"Xing","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/JSTARS.2014.2313572","article-title":"Comparative Assessment of Supervised Classifiers for Land Use Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data","volume":"7","author":"Shiraishi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1109\/TGRS.2007.912448","article-title":"Synthesis of Multispectral Images to High Spatial Resolution: A Critical Review of Fusion Methods Based on Remote Sensing Physics","volume":"46","author":"Thomas","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/LGRS.2013.2256875","article-title":"Sparse Representation Based Pansharpening Using Trained Dictionary","volume":"11","author":"Cheng","year":"2014","journal-title":"Geosci. Remote Sens. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2016.10.030","article-title":"Fusion of Sentinel-2 images","volume":"187","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"241","article-title":"Wavelet-based image fusion and quality assessment","volume":"6","author":"Shi","year":"2005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/JSTARS.2017.2768059","article-title":"A Review on Recent Developments in Fully Polarimetric SAR Image Despeckling","volume":"11","author":"Ma","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","first-page":"269","article-title":"System design and key technologies of the GF-3 satellite","volume":"46","author":"Qingjun","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"94654","DOI":"10.1109\/ACCESS.2020.2991199","article-title":"A New RD-RFM Stereo Geolocation Model for 3D Geo-Information Reconstruction of SAR-Optical Satellite Image Pairs","volume":"8","author":"Jiao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, J., and Wang, Q. (2020, January 24\u201326). Fusion of POLSAR and Multispectral Satellite Images: A New Insight for Image Fusion. Proceedings of the 2020 IEEE International Conference on Computational Electromagnetics (ICCEM), Singapore.","DOI":"10.1109\/ICCEM47450.2020.9219457"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., W Myint, S., Du, Z., Li, Y., Cao, J., Liu, L., and Wu, Z. (2020). Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China\u2019s Largest Artificially Planted Mangroves. Remote Sens., 12.","DOI":"10.3390\/rs12122039"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1109\/LGRS.2009.2017283","article-title":"Separation Between Water and Land in SAR Images Using Region-Based Level Sets","volume":"6","author":"Silveira","year":"2009","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1347","DOI":"10.1109\/LGRS.2018.2843886","article-title":"Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion","volume":"15","author":"Haut","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/LGRS.2013.2251316","article-title":"Classification Based on 3-D DWT and Decision Fusion for Hyperspectral Image Analysis","volume":"11","author":"Ye","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s11220-020-00280-9","article-title":"Comparative Analysis of SVM and ANN Classifiers using Multilevel Fusion of Multi-Sensor Data in Urban Land Classification","volume":"21","author":"Vohra","year":"2020","journal-title":"Sens. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s12518-010-0033-x","article-title":"Investigation of fusion of SAR and Landsat data for shoreline super resolution mapping: The northeastern Mediterranean Sea coast in Egypt","volume":"2","author":"Taha","year":"2010","journal-title":"Appl. Geomat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2212","DOI":"10.1109\/JSTARS.2013.2272773","article-title":"An Area-Based Image Fusion Scheme for the Integration of SAR and Optical Satellite Imagery","volume":"6","author":"Byun","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Montgomery, J., Brisco, B., Chasmer, L., Devito, K., Cobbaert, D., and Hopkinson, C. (2019). SAR and Lidar Temporal Data Fusion Approaches to Boreal Wetland Ecosystem Monitoring. Remote Sens., 11.","DOI":"10.3390\/rs11020161"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.1109\/JSTARS.2019.2945188","article-title":"A Novel Multispectral, Panchromatic and SAR Data Fusion for Land Classification","volume":"12","author":"Iervolino","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.inffus.2020.01.003","article-title":"Pixel level fusion techniques for SAR and optical images: A review","volume":"59","author":"Kulkarni","year":"2020","journal-title":"Inf. Fusion"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gao, H., Wang, C., Wang, G., Zhu, J., Tang, Y., Shen, P., and Zhu, Z. (2018). A crop classification method integrating GF-3 PolSAR and Sentinel-2A optical data in the Dongting Lake Basin. Sensors, 18.","DOI":"10.3390\/s18093139"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tang, X., You, S., Duan, K., Xiang, H., and Luo, H. (2020). A Novel Feature-Level Fusion Framework Using Optical and SAR Remote Sensing Images for Land Use\/Land Cover (LULC) Classification in Cloudy Mountainous Area. Appl. Sci., 10.","DOI":"10.3390\/app10082928"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Feng, W., Huang, W., and Ren, J. (2018). Class Imbalance Ensemble Learning Based on the Margin Theory. Appl. Sci., 8.","DOI":"10.3390\/app8050815"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1109\/JSTARS.2019.2922297","article-title":"Dynamic Synthetic Minority Over-Sampling Technique-Based Rotation Forest for the Classification of Imbalanced Hyperspectral Data","volume":"12","author":"Feng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1109\/JPROC.2012.2189089","article-title":"Human Settlements: A Global Challenge for EO Data Processing and Interpretation","volume":"101","author":"Gamba","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.geoderma.2019.02.031","article-title":"Spatial filtering and Bayesian data fusion for mapping soil properties: A case study combining legacy and remotely sensed data in Iran","volume":"344","author":"Rasaei","year":"2019","journal-title":"Geoderma"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1134\/S1054661816040131","article-title":"Urban areas extraction from multi sensor data based on machine learning and data fusion","volume":"27","author":"Puttinaovarat","year":"2017","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/19479830903561035","article-title":"Multi-source remote sensing data fusion: Status and trends","volume":"1","author":"Zhang","year":"2010","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","first-page":"1141","article-title":"Study of remote sensing image fusion and its application in image classification","volume":"37","author":"Wenbo","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","article-title":"A review of remote sensing image fusion methods","volume":"32","author":"Ghassemian","year":"2016","journal-title":"Inf. Fusion"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S1566-2535(01)00036-7","article-title":"A new look at IHS-like image fusion methods","volume":"2","author":"Tu","year":"2001","journal-title":"Inf. Fusion"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/0034-4257(87)90088-5","article-title":"Color enhancement of highly correlated images. II. Channel ratio and chromaticity transformation techniques","volume":"22","author":"Gillespie","year":"1987","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1291","DOI":"10.1109\/TGRS.2004.825593","article-title":"Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition","volume":"42","author":"Saleta","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1109\/TGRS.2014.2363477","article-title":"Model-Based Fusion of Multi- and Hyperspectral Images Using PCA and Wavelets","volume":"53","author":"Palsson","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3230","DOI":"10.1109\/TGRS.2007.901007","article-title":"Improving Component Substitution Pansharpening Through Multivariate Regression of MS +Pan Data","volume":"45","author":"Aiazzi","year":"2007","journal-title":"Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/S0924-2716(03)00014-5","article-title":"Fusion of hyperspectral and radar data using the IHS transformation to enhance urban surface features","volume":"58","author":"Chen","year":"2003","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Shao, Z., Wu, W., and Guo, S. (2020). IHS-GTF: A Fusion Method for Optical and Synthetic Aperture Radar Data. Remote Sens., 12.","DOI":"10.3390\/rs12172796"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1391","DOI":"10.1109\/TGRS.2005.846874","article-title":"A comparative analysis of image fusion methods","volume":"43","author":"Wang","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1080\/13682199.2017.1289629","article-title":"Efficient Landsat image fusion using fuzzy and stationary discrete wavelet transform","volume":"65","author":"Singh","year":"2017","journal-title":"Imaging Sci. J."},{"key":"ref_42","first-page":"1","article-title":"Extracting spectral contrast in Landsat Thematic Mapper image data using selective principal component analysis","volume":"55","author":"Kwarteng","year":"1989","journal-title":"Photogramm. Eng. Remote Sens"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1109\/LGRS.2012.2191387","article-title":"Wavelet Fusion on Ratio Images for Change Detection in SAR Images","volume":"9","author":"Ma","year":"2012","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1109\/JSTARS.2016.2546553","article-title":"A General Framework for Urban Area Extraction Exploiting Multiresolution SAR Data Fusion","volume":"9","author":"Salentinig","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"104845","DOI":"10.1016\/j.knosys.2019.07.016","article-title":"New margin-based subsampling iterative technique in modified random forests for classification","volume":"182","author":"Feng","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Feng, W., Huang, W., Dauphin, G., Xia, J., Quan, Y., Ye, H., and Dong, Y. (August, January 28). Ensemble Margin Based Semi-Supervised Random Forest for the Classification of Hyperspectral Image with Limited Training Data. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898415"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1879","DOI":"10.1109\/LGRS.2019.2913387","article-title":"Imbalanced Hyperspectral Image Classification With an Adaptive Ensemble Method Based on SMOTE and Rotation Forest With Differentiated Sampling Rates","volume":"16","author":"Feng","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Quan, Y., Zhong, X., Feng, W., Dauphin, G., Gao, L., and Xing, M. (2020). A Novel Feature Extension Method for the Forest Disaster Monitoring Using Multispectral Data. Remote Sens., 12.","DOI":"10.3390\/rs12142261"},{"key":"ref_52","first-page":"469","article-title":"Trend and forecasting of the COVID-19 outbreak in China","volume":"80","author":"Li","year":"2020","journal-title":"J. Infect."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tian, S., Zhang, X., Tian, J., and Sun, Q. (2016). Random forest classification of wetland landcovers from multi-sensor data in the arid region of Xinjiang, China. Remote Sens., 8.","DOI":"10.3390\/rs8110954"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1109\/LGRS.2018.2806223","article-title":"Multisource Earth Observation Data for Land-Cover Classification Using Random Forest","volume":"15","author":"Xu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_55","first-page":"2410","article-title":"Land-cover classification using GF-2 images and airborne lidar data based on Random Forest","volume":"40","author":"Wu","year":"2019","journal-title":"Geosci. Remote Sens. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.1162\/089976698300017197","article-title":"Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms","volume":"10","author":"Dietterich","year":"1998","journal-title":"Neural Comput."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Xu, L., and Jia, J. (2014, January 23\u201328). 100+ Times Faster Weighted Median Filter (WMF). Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.362"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1016\/j.neucom.2016.02.047","article-title":"Union Laplacian pyramid with multiple features for medical image fusion","volume":"194","author":"Du","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Fan, Q., Yang, J., Hua, G., Chen, B., and Wipf, D. (2017, January 22\u201329). A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.351"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhang, H., Mao, Q., and Li, Z. (2018). Land cover classification with gf-3 polarimetric synthetic aperture radar data by random forest classifier and fast super-pixel segmentation. Sensors, 18.","DOI":"10.3390\/s18072014"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Thanh Noi, P., and Kappas, M. (2018). Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors, 18.","DOI":"10.3390\/s18010018"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"5326","DOI":"10.1109\/JSTARS.2020.3021052","article-title":"Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review","volume":"13","author":"Amani","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Stromann, O., Nascetti, A., Yousif, O., and Ban, Y. (2020). Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12010076"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3801\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:34:32Z","timestamp":1760178872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,19]]},"references-count":63,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223801"],"URL":"https:\/\/doi.org\/10.3390\/rs12223801","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,19]]}}}