{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T04:10:07Z","timestamp":1783829407096,"version":"3.55.0"},"reference-count":51,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Forestry Science &amp; Technology Innovation and Extension Project","award":["LYKJ[2022]02"],"award-info":[{"award-number":["LYKJ[2022]02"]}]},{"name":"Jiangsu Forestry Science &amp; Technology Innovation and Extension Project","award":["202310298113Y"],"award-info":[{"award-number":["202310298113Y"]}]},{"name":"Provincial Student Innovation Training Program","award":["LYKJ[2022]02"],"award-info":[{"award-number":["LYKJ[2022]02"]}]},{"name":"Provincial Student Innovation Training Program","award":["202310298113Y"],"award-info":[{"award-number":["202310298113Y"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Concerning the ever-changing wetland environment, the efficient extraction of wetland information holds great significance for the research and management of wetland ecosystems. China\u2019s vast coastal wetlands possess rich and diverse geographical features. This study employs the SegFormer model and Sentinel-2 data to conduct a wetland classification study for coastal wetlands in Yancheng, Jiangsu, China. After preprocessing the Sentinel data, nine classification objects (construction land, Spartina alterniflora (S. alterniflora), Suaeda salsa (S. salsa), Phragmites australis (P. australis), farmland, river system, aquaculture and tidal falt) were identified based on the previous literature and remote sensing images. Moreover, mAcc, mIoU, aAcc, Precision, Recall and F-1 score were chosen as evaluation indicators. This study explores the potential and effectiveness of multiple methods, including data image processing, machine learning and deep learning. The results indicate that SegFormer is the best model for wetland classification, efficiently and accurately extracting small-scale features. With mIoU (0.81), mAcc (0.87), aAcc (0.94), mPrecision (0.901), mRecall (0.876) and mFscore (0.887) higher than other models. In the face of unbalanced wetland categories, combining CrossEntropyLoss and FocalLoss in the loss function can improve several indicators of difficult cases to be segmented, enhancing the classification accuracy and generalization ability of the model. Finally, the category scale pie chart of Yancheng Binhai wetlands was plotted. In conclusion, this study achieves an effective segmentation of Yancheng coastal wetlands based on the semantic segmentation method of deep learning, providing technical support and reference value for subsequent research on wetland values.<\/jats:p>","DOI":"10.3390\/rs15153714","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:09:01Z","timestamp":1690333741000},"page":"3714","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Semantic Segmentation of China\u2019s Coastal Wetlands Based on Sentinel-2 and Segformer"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-1059-4820","authenticated-orcid":false,"given":"Xufeng","family":"Lin","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youwei","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6704-8979","authenticated-orcid":false,"given":"Demin","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yinlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China"},{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"},{"name":"National Positioning Observation Station of Hung-Tse Lake Wetland Ecosystem in Jiangsu Province, Huaian 223100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongbo","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China"},{"name":"Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2017.07.034","article-title":"A novel strategy for wetland area extraction using multispectral MODIS data","volume":"200","author":"Bansal","year":"2017","journal-title":"Remote Sens. 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