{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:39:46Z","timestamp":1760240386031,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T00:00:00Z","timestamp":1558656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The problem of image segmentation can be reduced to the clustering of pixels in the intensity space. The traditional fuzzy c-means algorithm only uses pixel membership information and does not make full use of spatial information around the pixel, so it is not ideal for noise reduction. Therefore, this paper proposes a clustering algorithm based on spatial information to improve the anti-noise and accuracy of image segmentation. Firstly, the image is roughly clustered using the improved L\u00e9vy grey wolf optimization algorithm (LGWO) to obtain the initial clustering center. Secondly, the neighborhood and non-neighborhood information around the pixel is added into the target function as spatial information, the weight between the pixel information and non-neighborhood spatial information is adjusted by information entropy, and the traditional Euclidean distance is replaced by the improved distance measure. Finally, the objective function is optimized by the gradient descent method to segment the image correctly.<\/jats:p>","DOI":"10.3390\/s19102385","type":"journal-article","created":{"date-parts":[[2019,5,24]],"date-time":"2019-05-24T11:20:46Z","timestamp":1558696846000},"page":"2385","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Adaptive Segmentation of Remote Sensing Images Based on Global Spatial Information"],"prefix":"10.3390","volume":"19","author":[{"given":"Muqing","family":"Li","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3668-7572","authenticated-orcid":false,"given":"Luping","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan","family":"Gao","sequence":"additional","affiliation":[{"name":"Research Institute of Vibration Engineering, ZhengZhou University, 100 Kexue Avenue of Gaoxin Section, ZhengZhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Life Sciences and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xian 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.ins.2010.10.016","article-title":"Fuzzy clustering algorithms for unsupervised change detection in remote sensing images","volume":"181","author":"Ghosh","year":"2011","journal-title":"Inf. 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