{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:52:28Z","timestamp":1760241148986,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,15]],"date-time":"2019-12-15T00:00:00Z","timestamp":1576368000000},"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":["41501378"],"award-info":[{"award-number":["41501378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province, China","award":["BK20150835"],"award-info":[{"award-number":["BK20150835"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Automatic extraction of built-up areas from very high-resolution (VHR) satellite images has received increasing attention in recent years. However, due to the complexity of spectral and spatial characteristics of built-up areas, it is still a challenging task to obtain their precise location and extent. In this study, a patch-based framework was proposed for unsupervised extraction of built-up areas from VHR imagery. First, a group of corner-constrained overlapping patches were defined to locate the candidate built-up areas. Second, for each patch, its salient textures and structural characteristics were represented as a feature vector using integrated high-frequency wavelet coefficients. Then, inspired by visual perception, a patch-level saliency model of built-up areas was constructed by incorporating Gestalt laws of proximity and similarity, which can effectively describe the spatial relationships between patches. Finally, built-up areas were extracted through thresholding and their boundaries were refined by morphological operations. The performance of the proposed method was evaluated on two VHR image datasets. The resulting average F-measure values were 0.8613 for the Google Earth dataset and 0.88 for the WorldView-2 dataset, respectively. Compared with existing models, the proposed method obtains better extraction results, which show more precise boundaries and preserve better shape integrity.<\/jats:p>","DOI":"10.3390\/rs11243022","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:19:38Z","timestamp":1576473578000},"page":"3022","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Automatic Extraction of Built-Up Areas from Very High-Resolution Satellite Imagery Using Patch-Level Spatial Features and Gestalt Laws of Perceptual Grouping"],"prefix":"10.3390","volume":"11","author":[{"given":"Yixiang","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Zhiyong","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Bo","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077, China"}]},{"given":"Pengdong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Surveying and Geoinformatics, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mboga, N., Persello, C., Bergado, J.R., and Stein, A. 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