{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T18:21:34Z","timestamp":1783794094169,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"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":["61860206013"],"award-info":[{"award-number":["61860206013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multi-aspect synthetic aperture radar (SAR) images contain more information available for automatic target recognition (ATR) than images from a single view. However, the sensitivity to aspect angles also makes it hard to extract and integrate information from multi-aspect images. In this paper, we propose a novel method based on the variations features to realize automatic building detection in the image level. First, to get a comprehensive description of target variation patterns, statistical characteristic variances are derived from three representative and complementary categories. Then, these obtained features are fused and put in the K-means classifier for prescreening, whose results are used as the training sets in supervised classification later to avoid manual labeling. Second, for more precise detection performance, finer features in vector forms are obtained by principal component analysis (PCA). The variation patterns of these feature vectors are explored in two different manners of correlation and fluctuation analyses and processed by separate support vector machines (SVMs) after fusion. Finally, the independent SVM detection results are fused according to a maximum probability rule. Experiments conducted on two different airborne data sets demonstrate the robustness and effectiveness of the proposed method, in spite of significant target signature variabilities and cluttered background.<\/jats:p>","DOI":"10.3390\/rs14061409","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T03:36:23Z","timestamp":1647401783000},"page":"1409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automatic Building Detection for Multi-Aspect SAR Images Based on the Variation Features"],"prefix":"10.3390","volume":"14","author":[{"given":"Qi","family":"Liu","sequence":"first","affiliation":[{"name":"The Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Institute of Tracking and Telecommunications Technology, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidong","family":"Yu","sequence":"additional","affiliation":[{"name":"The Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100194, China"},{"name":"The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Hong","sequence":"additional","affiliation":[{"name":"The School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/JSTARS.2017.2787728","article-title":"Multiple Mode SAR Raw Data Simulation and Parallel Acceleration for Gaofen-3 Mission","volume":"11","author":"Zhang","year":"2018","journal-title":"IEEE J. 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