{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T15:28:24Z","timestamp":1760369304221,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T00:00:00Z","timestamp":1471564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents a novel image matching method for multi-source satellite images, which integrates global Shuttle Radar Topography Mission (SRTM) data and image segmentation to achieve robust and numerous correspondences. This method first generates the epipolar lines as a geometric constraint assisted by global SRTM data, after which the seed points are selected and matched. To produce more reliable matching results, a region segmentation-based matching propagation is proposed in this paper, whereby the region segmentations are extracted by image segmentation and are considered to be a spatial constraint. Moreover, a similarity measure integrating Distance, Angle and Normalized Cross-Correlation (DANCC), which considers geometric similarity and radiometric similarity, is introduced to find the optimal correspondences. Experiments using typical satellite images acquired from Resources Satellite-3 (ZY-3), Mapping Satellite-1, SPOT-5 and Google Earth demonstrated that the proposed method is able to produce reliable and accurate matching results.<\/jats:p>","DOI":"10.3390\/rs8080672","type":"journal-article","created":{"date-parts":[[2016,8,19]],"date-time":"2016-08-19T09:58:27Z","timestamp":1471600707000},"page":"672","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["An Image Matching Algorithm Integrating Global SRTM and Image Segmentation for Multi-Source Satellite Imagery"],"prefix":"10.3390","volume":"8","author":[{"given":"Xiao","family":"Ling","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Jinxin","family":"Xiong","sequence":"additional","affiliation":[{"name":"2012 Laboratory of HUAWEI Technology Co., Ltd., Shenzhen 518000, China"}]},{"given":"Xu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Zhipeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1111\/j.1477-9730.2011.00671.x","article-title":"Development and status of image matching in photogrammetry","volume":"27","author":"Gruen","year":"2012","journal-title":"Photogramm. 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