{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T06:16:18Z","timestamp":1772950578060,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2013,9,12]],"date-time":"2013-09-12T00:00:00Z","timestamp":1378944000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Aquaculture coasts have become widely distributed in coastal zones as human activities are intensified. Due to the complexity in this type of coast, it is difficult to extract the coastline with traditional automated mapping approaches. In this paper, we present an automated method\u2014object-based region growing integrating edge detection (OBRGIE) for the extraction of this type of coastline. In this method, a new object feature named OMI (object merging index) is proposed to separate land and sea. The OBRGIE method was applied to Landsat Thematic Mapper (TM) (pixel size 30m) and Satellite Pour l\u2019Observation de la Terre (SPOT-5) (pixel size 10 m) images of two coastal segments with lengths of 272.7 km and 35.5 km respectively, and the accuracy of the extracted coastlines was assessed in comparison with the manually delineated coastlines. The mean and RMSE (root mean square error) are 16.0 m and 16.4 m respectively for the TM images, and 8.0 m and 8.6 m, respectively, for the SPOT-5 images, indicating that the proposed method derives coastlines with pixel accuracy. The OBRGIE method is also found to be robust to the segmentation scale parameter, and the OMI feature is much more effective than the spectral attribute in separating land and sea in aquaculture coasts. This method may provide an inexpensive means of fast coastline mapping from remotely sensed imagery with relatively fine-to-moderate spatial resolution in coastal sectors with intense human interference.<\/jats:p>","DOI":"10.3390\/rs5094470","type":"journal-article","created":{"date-parts":[[2013,9,12]],"date-time":"2013-09-12T12:13:05Z","timestamp":1378987985000},"page":"4470-4487","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["Extraction of Coastline in Aquaculture Coast from Multispectral Remote Sensing Images: Object-Based Region Growing Integrating Edge Detection"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1772-0915","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaomei","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing 100101, China"}]},{"given":"Shanshan","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"}]},{"given":"Fenzhen","family":"Su","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, IGSNRR, CAS, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2013,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"688","DOI":"10.2112\/03-0071.1","article-title":"Shoreline definition and detection: A review","volume":"21","author":"Boak","year":"2005","journal-title":"J. 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