{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T18:49:49Z","timestamp":1784054989685,"version":"3.55.0"},"reference-count":72,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,9,26]],"date-time":"2019-09-26T00:00:00Z","timestamp":1569456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010012","name":"National Aerospace Science Foundation of China","doi-asserted-by":"publisher","award":["41671342, U1609203"],"award-info":[{"award-number":["41671342, U1609203"]}],"id":[{"id":"10.13039\/501100010012","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LR19D010001, LQ18D010001"],"award-info":[{"award-number":["LR19D010001, LQ18D010001"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mapping different land cover types with satellite remote sensing data is significant for restoring and protecting natural resources and ecological services in coastal wetlands. In this paper, we propose a hierarchical classification framework (HCF) that implements two levels of classification scheme to identify different land cover types of coastal wetlands. The first level utilizes the designed decision tree to roughly group land covers into four rough classes and the second level combines multiple features (i.e., spectral feature, texture feature and geometric feature) of each class to distinguish different subtypes of land covers in each rough class. Two groups of classification experiments on Landsat and Sentinel multispectral data and China Gaofen (GF)-5 hyperspectral data are carried out in order to testify the classification behaviors of two famous coastal wetlands of China, that is, Yellow River Estuary and Yancheng coastal wetland. Experimental results on Landsat data show that the proposed HCF performs better than support vector machine and random forest in classifying land covers of coastal wetlands. Moreover, HCF is suitable for both multispectral data and hyperspectral data and the GF-5 data is superior to Landsat-8 and Sentinel-2 multispectral data in obtaining fine classification results of coastal wetlands.<\/jats:p>","DOI":"10.3390\/rs11192238","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T03:03:15Z","timestamp":1569553395000},"page":"2238","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["A Hierarchical Classification Framework of Satellite Multispectral\/Hyperspectral Images for Mapping Coastal Wetlands"],"prefix":"10.3390","volume":"11","author":[{"given":"Leilei","family":"Jiao","sequence":"first","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7001-2037","authenticated-orcid":false,"given":"Gang","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China"},{"name":"State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangbo","family":"Ren","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources of China, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinnian","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Infrared System Detection and Imaging Technologies, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,26]]},"reference":[{"key":"ref_1","unstructured":"Perillo, G., Wolanski, E., Cahoon, D.R., and Hopkinson, C.S. 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