{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:53:47Z","timestamp":1760151227414,"version":"build-2065373602"},"reference-count":60,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"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":["61876141 and 61373111"],"award-info":[{"award-number":["61876141 and 61373111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Provincial Natural Science Foundation of Shaanxi of China","award":["2019JZ-26"],"award-info":[{"award-number":["2019JZ-26"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The recently proposed multi-objective clustering methods convert the segmentation problem to a multi-objective optimization problem by extracting multiple features from an image to be segmented as clustering data. However, most of these methods fail to consider the impacts of different features on segmentation results when calculating the similarity using the Euclidean distance. In this paper, feature domination is defined to segment the image efficiently, and then an adaptive feature weights based double-layer multi-objective method (AFWDLMO) for image segmentation is presented. The proposed method mainly contains two layers: a weight determination layer and a clustering layer. In the weight determination layer, AFWDLMO adaptively identifies the dominant feature of an image to be segmented and specifies its optimal weight through differential evolution. In the clustering layer, multi-objective clustering functions are established and optimized based on the acquired optimal weight, and a set of solutions with high segmentation accuracy is found. The segmentation results on several texture images and SAR images show that the proposed method is better than several existing state-of-the-art segmentation algorithms.<\/jats:p>","DOI":"10.3390\/rs14051117","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Feature Weights Based Double-Layer Multi-Objective Method for SAR Image Segmentation"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0502-4074","authenticated-orcid":false,"given":"Ruochen","family":"Liu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception, Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Minlei","family":"Xue","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception, Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Haoyuan","family":"Lv","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception, Image Understanding of Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1109\/TGRS.2016.2611060","article-title":"Adaptive Hybrid Conditional Random Field Model for SAR Image Segmentation","volume":"55","author":"Wang","year":"2017","journal-title":"IEEE Trans. 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