{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:11:19Z","timestamp":1780675879770,"version":"3.54.1"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,13]],"date-time":"2021-05-13T00:00:00Z","timestamp":1620864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Jiangsu Province","award":["No. BK20180779"],"award-info":[{"award-number":["No. BK20180779"]}]},{"name":"Water Conservancy Science and Technology Project of Jiangsu Province","award":["No. 2020061"],"award-info":[{"award-number":["No. 2020061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>When the use of optical images is not practical due to cloud cover, Synthetic Aperture Radar (SAR) imagery is a preferred alternative for monitoring coastal wetlands because it is unaffected by weather conditions. Polarimetric SAR (PolSAR) enables the detection of different backscattering mechanisms and thus has potential applications in land cover classification. Gaofen-3 (GF-3) is the first Chinese civilian satellite with multi-polarized C-band SAR imaging capability. Coastal wetland classification with GF-3 polarimetric SAR imagery has attracted increased attention in recent years, but it remains challenging. The aim of this study was to classify land cover in coastal wetlands using an object-oriented random forest algorithm on the basis of GF-3 polarimetric SAR imagery. First, a set of 16 commonly used SAR features was extracted. Second, the importance of each SAR feature was calculated, and the optimal polarimetric features were selected for wetland classification by combining random forest (RF) with sequential backward selection (SBS). Finally, the proposed algorithm was utilized to classify different land cover types in the Yancheng Coastal Wetlands. The results show that the most important parameters for wetland classification in this study were Shannon entropy, Span and orientation randomness, combined with features derived from Yamaguchi decomposition, namely, volume scattering, double scattering, surface scattering and helix scattering. When the object-oriented RF classification approach was used with the optimal feature combination, different land cover types in the study area were classified, with an overall accuracy of up to 92%.<\/jats:p>","DOI":"10.3390\/s21103395","type":"journal-article","created":{"date-parts":[[2021,5,14]],"date-time":"2021-05-14T03:28:36Z","timestamp":1620962916000},"page":"3395","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Coastal Wetland Classification with GF-3 Polarimetric SAR Imagery by Using Object-Oriented Random Forest Algorithm"],"prefix":"10.3390","volume":"21","author":[{"given":"Xiaotong","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7073-0082","authenticated-orcid":false,"given":"Jia","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangsu Province Surveying &amp; Mapping Engineering Institute, Nanjing 210013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongmei","family":"Wang","sequence":"additional","affiliation":[{"name":"Jiangsu Provincial Hydraulic Research Institute, Nanjing 210017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.rse.2015.04.010","article-title":"Long term detection of water depth changes of coastal wetlands in the Yellow River Delta based on distributed scatterer interferometry","volume":"164","author":"Xie","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"McCarthy, M.C., and Dawes, R. (2018, January 18\u201322). The Rotational Spectrum and Potential Energy Surface of Ar-Sio AN Experimental Investigation. Proceedings of the 73rd International Symposium on Molecular Spectroscopy, Champaign-Urbana, IL, USA.","DOI":"10.15278\/isms.2018.FC01"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.isprsjprs.2020.11.018","article-title":"Kernel low-rank representation with elastic net for China coastal wetland land cover classification using GF-5 hyperspectral imagery","volume":"171","author":"Su","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dang, K.B., Nguyen, M.H., Nguyen, D.A., Phan, T.T.H., Giang, T.L., Pham, H.H., Nguyen, T.N., Tran, T.T.V., and Bui, D.T. (2020). Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12193270"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, N., Shi, R., Zhuo, W., Zhang, C., Zhou, B., Xia, Z., Tao, Z., Gao, W., and Tian, B. (2021). A Classification of Tidal Flat Wetland Vegetation Combining Phenological Features with Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13030443"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1007\/s11273-009-9169-z","article-title":"Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: A review","volume":"18","author":"Adam","year":"2010","journal-title":"Wetl. Ecol. Manag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Omari, K., Chenier, R., Touzi, R., and Sagram, M. (2020). Investigation of C-Band SAR Polarimetry for Mapping a High-Tidal Coastal Environment in Northern Canada. Remote Sens., 12.","DOI":"10.3390\/rs12121941"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/014311601750038857","article-title":"Evaluation of C-band SAR data for wetlands mapping","volume":"22","author":"Baghdadi","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/LGRS.2016.2597965","article-title":"Coastal zone classification with fully polarimetric SAR imagery","volume":"13","author":"Gou","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jensen, D., Cavanaugh, K.C., Simard, M., Okin, G.S., Casta\u00f1eda-Moya, E., McCall, A., and Twilley, R.R. (2019). Integrating imaging spectrometer and synthetic aperture radar data for estimating wetland vegetation aboveground biomass in coastal Louisiana. Remote Sens., 11.","DOI":"10.3390\/rs11212533"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10203","DOI":"10.1007\/s12517-015-1940-2","article-title":"Classification of coastal wetlands in eastern China using polarimetric SAR data","volume":"8","author":"Chen","year":"2015","journal-title":"Arab. J. Geosci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1036","DOI":"10.3390\/w5031036","article-title":"Wetland monitoring using the curvelet-based change detection method on polarimetric SAR imagery","volume":"5","author":"Schmitt","year":"2013","journal-title":"Water"},{"key":"ref_13","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_14","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_15","first-page":"139","article-title":"Mapping wetlands in Nova Scotia with multi-beam RADARSAT-2 Polarimetric SAR, optical satellite imagery, and Lidar data","volume":"68","author":"Jahncke","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","first-page":"3","article-title":"Wetland Classification for Black Duck Habitat Management Using Combined Polarimetric RADARSAT 2 and SPOT Imagery","volume":"42","author":"Zhang","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cazals, C., Rapinel, S., Frison, P.L., Bonis, A., Mercier, G., Mallet, C., Corgne, S., and Rudant, J.-P. (2016). Mapping and characterization of hydrological dynamics in a coastal marsh using high temporal resolution Sentinel-1A images. Remote Sens., 8.","DOI":"10.3390\/rs8070570"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"301","DOI":"10.5194\/isprs-annals-V-3-2020-301-2020","article-title":"Wetland mapping in New Brunswick, Canada with LANDSAT5-TM, Alos-Palsar, and RADARSAT-2 Imagery","volume":"3","author":"LaRocque","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, X., Shao, Y., Tian, W., Duan, Y., Li, K., and Liu, L. (2018, January 22\u201327). Evaluation of GF-3 Quad-Polarized SAR Imagery for Coastal Wetland Observation. Proceedings of the IGARSS 2018\u20132018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8517389"},{"key":"ref_20","first-page":"1641","article-title":"Wetland classification through integration of GF-3 and Sentinel-2B multispectral Deta over the Yellow River Delta","volume":"44","author":"Peng","year":"2019","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2343","DOI":"10.1109\/36.964970","article-title":"Quantitative comparison of classification capability: Fully polarimetric versus dual and single-polarization SAR","volume":"39","author":"Lee","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"13","article-title":"Wetland monitoring and mapping using synthetic aperture radar","volume":"1","author":"Dabboor","year":"2018","journal-title":"Wetl. Manag. Assess. Risk Sustain. Solut."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1109\/JSTARS.2012.2202091","article-title":"Evaluating full polarimetric C-and L-band data for mapping wetland conditions in a semi-arid environment in Central Spain","volume":"5","author":"Koch","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2017.05.010","article-title":"Random forest wetland classification using ALOS-2 L-band, RADARSAT-2 C-band, and TerraSAR-X imagery","volume":"130","author":"Mahdianpari","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"336","DOI":"10.5589\/m09-025","article-title":"A semi-automated tool for surface water mapping with RADARSAT-1","volume":"35","author":"Brisco","year":"2009","journal-title":"Can. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.isprsjprs.2020.10.007","article-title":"SAR analysis of wetland ecosystems: Effects of band frequency, polarization mode and acquisition dates","volume":"170","author":"Rapinel","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1080\/15481603.2017.1419602","article-title":"Remote sensing for wetland classification: A comprehensive review","volume":"55","author":"Mahdavi","year":"2018","journal-title":"Gisci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"290","DOI":"10.5589\/m13-038","article-title":"Wetland mapping with LiDAR derivatives, SAR polarimetric decompositions, and LiDAR\u2013SAR fusion using a random forest classifier","volume":"39","author":"Millard","year":"2013","journal-title":"Can. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Y., He, X., Xu, J., Zhang, R., and Lu, Y. (2020). Scattering feature set optimization and polarimetric SAR classification using object-oriented RF-SFS algorithm in coastal wetlands. Remote Sens., 12.","DOI":"10.3390\/rs12030407"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1080\/15481603.2019.1643530","article-title":"Separability analysis of wetlands in Canada using multi-source SAR data","volume":"56","author":"Amani","year":"2019","journal-title":"Gisci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.rse.2017.11.005","article-title":"Fisher linear discriminant analysis of coherency matrix for wetland classification using PolSAR imagery","volume":"206","author":"Mahdianpari","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Xie, Q., Wang, J., Liao, C., Shang, J., Lopez-Sanchez, J.M., Fu, H., and Liu, X. (2019). On the use of Neumann decomposition for crop classification using multi-temporal RADARSAT-2 polarimetric SAR data. Remote Sens., 11.","DOI":"10.3390\/rs11070776"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Liu, K., Myint, S.W., 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_34","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_35","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.rse.2011.11.001","article-title":"A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data","volume":"118","author":"Qi","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6020","DOI":"10.1080\/01431161.2018.1506592","article-title":"An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR imagery","volume":"39","author":"Kavzoglu","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/36.551935","article-title":"An entropy based classification scheme for land applications of polarimetric SAR","volume":"35","author":"Cloude","year":"1997","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"963","DOI":"10.1109\/36.673687","article-title":"A three-component scattering model for polarimetric SAR data","volume":"36","author":"Freeman","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.1109\/TGRS.2005.852084","article-title":"Four-component scattering model for polarimetric SAR image decomposition","volume":"43","author":"Yamaguchi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","unstructured":"Allain, S., Ferro-Famil, L., and Pottier, E. (2006, January 16\u201318). A polarimetric classification from PolSAR data using SERD\/DERD parameters. Proceedings of the 6th European Conference on Synthetic Aperture Radar (EUSAR 2006), Dresden, Germany."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1109\/TGRS.2008.926115","article-title":"Information theory-based approach for contrast analysis in polarimetric and\/or interferometric SAR images","volume":"46","author":"Morio","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/36.485127","article-title":"A review of target decomposition theorems in radar polarimetry","volume":"34","author":"Cloude","year":"1996","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Morandeira, N.S., Grings, F., Facchinetti, C., and Kandus, P. (2016). Mapping plant functional types in floodplain wetlands: An analysis of C-band polarimetric SAR data from RADARSAT-2. Remote Sens., 8.","DOI":"10.3390\/rs8030174"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"82","DOI":"10.5589\/m11-017","article-title":"Evaluation of C-band polarization diversity and polarimetry for wetland mapping","volume":"37","author":"Brisco","year":"2011","journal-title":"Can. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/TGRS.2016.2595626","article-title":"Detecting covariance symmetries in polarimetric SAR images","volume":"55","author":"Pallotta","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Boonprong, S., Cao, C., Chen, W., and Bao, S. (2018). Random forest variable importance spectral indices scheme for burnt forest recovery monitoring\u2014Multilevel RF-VIMP. Remote Sens., 10.","DOI":"10.3390\/rs10060807"},{"key":"ref_48","unstructured":"Raschka, S. (2015). Python Machine Learning, Packt Publishing Ltd."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3395\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:00:13Z","timestamp":1760162413000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/10\/3395"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,13]]},"references-count":48,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["s21103395"],"URL":"https:\/\/doi.org\/10.3390\/s21103395","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,13]]}}}