{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T15:52:49Z","timestamp":1776613969630,"version":"3.51.2"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,15]],"date-time":"2023-01-15T00:00:00Z","timestamp":1673740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"National Key Research and Development Program of China","award":["2021YFE0117800"],"award-info":[{"award-number":["2021YFE0117800"]}]},{"name":"National Key Research and Development Program of China","award":["CAS-WX2021SF-0106"],"award-info":[{"award-number":["CAS-WX2021SF-0106"]}]},{"name":"14th Five-year Informatization Plan of Chinese Academy of Sciences","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"14th Five-year Informatization Plan of Chinese Academy of Sciences","award":["2021YFE0117800"],"award-info":[{"award-number":["2021YFE0117800"]}]},{"name":"14th Five-year Informatization Plan of Chinese Academy of Sciences","award":["CAS-WX2021SF-0106"],"award-info":[{"award-number":["CAS-WX2021SF-0106"]}]},{"name":"National Data Sharing Infrastructure of Earth System Science","award":["2022YFF0711602"],"award-info":[{"award-number":["2022YFF0711602"]}]},{"name":"National Data Sharing Infrastructure of Earth System Science","award":["2021YFE0117800"],"award-info":[{"award-number":["2021YFE0117800"]}]},{"name":"National Data Sharing Infrastructure of Earth System Science","award":["CAS-WX2021SF-0106"],"award-info":[{"award-number":["CAS-WX2021SF-0106"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Water resources are important strategic resources related to human survival and development. Water body extraction from remote sensing images is a very important research topic for the monitoring of global and regional surface water changes. Deep learning networks are one of the most effective approaches and training data is indispensable for ensuring the network accurately extracts water bodies. The training data for water body extraction includes water body samples and non-water negative samples. Cloud shadows are essential negative samples due to the high similarity between water bodies and cloud shadows, but few studies quantitatively evaluate the impact of cloud shadow samples on the accuracy of water body extraction. Therefore, the training datasets with different proportions of cloud shadows were produced, and each of them includes two types of cloud shadow samples: the manually-labeled cloud shadows and unlabeled cloud shadows. The training datasets are applied on a novel transformer-based water body extraction network to investigate how the negative samples affect the accuracy of the water body extraction network. The evaluation results of Overall Accuracy (OA) of 0.9973, mean Intersection over Union (mIoU) of 0.9753, and Kappa of 0.9747 were obtained, and it was found that when the training dataset contains a certain proportion of cloud shadows, the trained network can handle the misclassification of cloud shadows well and more accurately extract water bodies.<\/jats:p>","DOI":"10.3390\/rs15020514","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T04:31:32Z","timestamp":1673843492000},"page":"514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Effect of Negative Samples on the Accuracy of Water Body Extraction Using Deep Learning Networks"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9051-1925","authenticated-orcid":false,"given":"Jia","family":"Song","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}]},{"given":"Xiangbing","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Resource and Environmental Science, Wuhan University, Wuhan 430072, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,15]]},"reference":[{"key":"ref_1","unstructured":"Zhang, Z., Prinet, V., and Ma, S. 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