{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:37:39Z","timestamp":1773787059439,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,25]],"date-time":"2021-12-25T00:00:00Z","timestamp":1640390400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"The National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61890964"],"award-info":[{"award-number":["61890964"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China-Korea Joint Ocean Research Center, China","award":["PI-2019-1-01"],"award-info":[{"award-number":["PI-2019-1-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium\u2013high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium\u2013high spatial resolution satellite data is required. This study proposes the red tide detection U\u2212Net (RDU\u2212Net) model by considering the HY\u22121D Coastal Zone Imager (HY\u22121D CZI) as an example. RDU\u2212Net employs the channel attention model to derive the inter\u2212channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi\u2212feature dataset including the HY\u22121D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU\u2212Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1\u2212score and Kappa are 0.87. Compared with the existing method, the F1\u2212score is improved by 0.07\u20130.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF\u22121 Wide Field of View 2 (WFV2) images.<\/jats:p>","DOI":"10.3390\/rs14010088","type":"journal-article","created":{"date-parts":[[2021,12,27]],"date-time":"2021-12-27T01:06:54Z","timestamp":1640567214000},"page":"88","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Red Tide Detection Method for HY\u22121D Coastal Zone Imager Based on U\u2212Net Convolutional Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2886-8090","authenticated-orcid":false,"given":"Xin","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}]},{"given":"Rongjie","family":"Liu","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Yi","family":"Ma","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"},{"name":"Technology Innovation Center for Ocean Telemetry, MNR, Qingdao 266061, China"}]},{"given":"Yanfang","family":"Xiao","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]},{"given":"Jing","family":"Ding","sequence":"additional","affiliation":[{"name":"National Satellite Ocean Application Service, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6437-5571","authenticated-orcid":false,"given":"Jianqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"National Satellite Ocean Application Service, Beijing 100081, China"}]},{"given":"Quanbin","family":"Wang","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2017.03.027","article-title":"Red tide time series forecasting by combining ARIMA and deep belief network","volume":"125","author":"Qin","year":"2017","journal-title":"Knowl. -Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JRS.11.012003","article-title":"Modified MODIS fluorescence line height data product to improve image interpretation for red tide monitoring in the eastern Gulf of Mexico","volume":"11","author":"Hu","year":"2016","journal-title":"J. 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