{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:50:58Z","timestamp":1770292258001,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,14]],"date-time":"2022-10-14T00:00:00Z","timestamp":1665705600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Space Organization, Taiwan","award":["NSPO-S-110244"],"award-info":[{"award-number":["NSPO-S-110244"]}]},{"name":"National Space Organization, Taiwan","award":["MOST 111-2221-E-027-132"],"award-info":[{"award-number":["MOST 111-2221-E-027-132"]}]},{"name":"National Space Organization, Taiwan","award":["MOST 111-2119-M-027-001"],"award-info":[{"award-number":["MOST 111-2119-M-027-001"]}]},{"name":"National Space Organization, Taiwan","award":["MOST 110-2119-M-027-001"],"award-info":[{"award-number":["MOST 110-2119-M-027-001"]}]},{"name":"National Space Organization, Taiwan","award":["MOST 110-2221-E-027-101"],"award-info":[{"award-number":["MOST 110-2221-E-027-101"]}]},{"name":"Ministry of Science and Technology, Taiwan","award":["NSPO-S-110244"],"award-info":[{"award-number":["NSPO-S-110244"]}]},{"name":"Ministry of Science and Technology, Taiwan","award":["MOST 111-2221-E-027-132"],"award-info":[{"award-number":["MOST 111-2221-E-027-132"]}]},{"name":"Ministry of Science and Technology, Taiwan","award":["MOST 111-2119-M-027-001"],"award-info":[{"award-number":["MOST 111-2119-M-027-001"]}]},{"name":"Ministry of Science and Technology, Taiwan","award":["MOST 110-2119-M-027-001"],"award-info":[{"award-number":["MOST 110-2119-M-027-001"]}]},{"name":"Ministry of Science and Technology, Taiwan","award":["MOST 110-2221-E-027-101"],"award-info":[{"award-number":["MOST 110-2221-E-027-101"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Climate change and global warming lead to changes in the sea level and shoreline, which pose a huge threat to island regions. Therefore, it is important to effectively detect the shoreline changes. Taiwan is a typical island, located at the junction of the East China Sea and the South China Sea in the Pacific Northwest, and is deeply affected by shoreline changes. In this research, Taiwan was selected as the study area. In this research, an efficient shoreline detection method was proposed based on the semantic segmentation U-Net model using the Sentinel-1 synthetic aperture radar (SAR) data of Taiwan island. In addition, the batch normalization (BN) module was added to the convolution layers in the U-Net architecture to further improve the generalization ability of U-Net and accelerate the training process. A self-built shoreline dataset was introduced to train the U-Net model and test its detection efficiency. The dataset consists of a total of 4029 SAR images covering all coastal areas of Taiwan. The training samples of the dataset were annotated by morphological processing and manual inspection. The segmentation results of U-Net were then processed by edge detection and morphological postprocessing to extract the shoreline. The experimental results showed that the proposed method could achieve a satisfactory detection performance compared with the related methods using the data provided by the Ministry of the Interior of Taiwan from 2016 to 2019 for different coastal landforms in Taiwan. Within a 5-pixel difference between the detected shoreline and the ground truth data, the F1-Meaure of the proposed method exceeded 80%. In addition, the potential of this method in shoreline change detection was validated with a sandbar located on the southwestern coast of Taiwan. Finally, the entire shoreline of Taiwan has been described by the proposed approach and the detected shoreline length was close to the actual length.<\/jats:p>","DOI":"10.3390\/rs14205135","type":"journal-article","created":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T03:43:58Z","timestamp":1665978238000},"page":"5135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["U-Net for Taiwan Shoreline Detection from SAR Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Lena","family":"Chang","sequence":"first","affiliation":[{"name":"Department of Communications, Navigation and Control Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan"},{"name":"The Intelligent Maritime Research Center (IMRC), National Taiwan Ocean University, Keelung 202301, Taiwan"}]},{"given":"Yi-Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taiwan Ocean University, Keelung 202301, Taiwan"}]},{"given":"Meng-Che","family":"Wu","sequence":"additional","affiliation":[{"name":"National Space Organization, National Applied Research Laboratories, Hsinchu 30078, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8933-7483","authenticated-orcid":false,"given":"Mohammad","family":"Alkhaleefah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5834-1057","authenticated-orcid":false,"given":"Yang-Lang","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, National Taipei University of Technology, Taipei 106344, Taiwan"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1029\/2005GL024826","article-title":"A 20th century acceleration in global sea-level rise","volume":"33","author":"Church","year":"2006","journal-title":"Geophys. 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