{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:38:18Z","timestamp":1760240298921,"version":"build-2065373602"},"reference-count":56,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T00:00:00Z","timestamp":1556236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41801275"],"award-info":[{"award-number":["41801275"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["ZR2018BD007"],"award-info":[{"award-number":["ZR2018BD007"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["18CX05030A, 18CX02179A"],"award-info":[{"award-number":["18CX05030A, 18CX02179A"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The high interior heterogeneity of land surface covers in high-resolution image of coastal cities makes classification challenging. To meet this challenge, a Multi-Scale Superpixels-based Classification method using Optimized Spectral\u2013Spatial features, denoted as OSS-MSSC, is proposed in this paper. In the proposed method, the multi-scale superpixels are firstly generated to capture the local spatial structures of the ground objects with various sizes. Then, the normalized difference vegetation index and extend multi-attribute profiles are introduced to extract the spectral\u2013spatial features from the multi-spectral bands of the image. To reduce the redundancy of the spectral\u2013spatial features, the crossover-based search algorithm is utilized for feature optimization. The pre-classification results at each single scale are, therefore, obtained based on the optimized spectral\u2013spatial features and random forest classifier. Finally, the ultimate classification is generated via the majority voting of those pre-classification results in each scale. Experimental results on the Gaofen-2 image of Qingdao and WorldView-2 image of Hong Kong, China confirmed the effectiveness of the proposed method. The experiments verify that the OSS-MSSC method not only works effectively on the homogeneous regions, but also is able to preserve the small local spatial structures in the high-resolution remote sensing images of coastal cities.<\/jats:p>","DOI":"10.3390\/rs11090998","type":"journal-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T07:52:59Z","timestamp":1556265179000},"page":"998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Mapping of Coastal Cities Using Optimized Spectral\u2013Spatial Features Based Multi-Scale Superpixel Classification"],"prefix":"10.3390","volume":"11","author":[{"given":"Aizhu","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Shuang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Genyun","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Shandong Provincial Climate Center, Jinan 250000, China"}]},{"given":"Hang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Yunhua","family":"Zhao","sequence":"additional","affiliation":[{"name":"Qingdao Geotechnical Investigation and Surveying Research Institute, Qingdao 266032, China"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"given":"Ji","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8684-6610","authenticated-orcid":false,"given":"Zhenjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China"},{"name":"Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ocecoaman.2015.06.004","article-title":"Analysis and trends of the world\u2019s coastal cities and agglomerations","volume":"114","year":"2015","journal-title":"Ocean Coast. 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