{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T07:49:41Z","timestamp":1774424981133,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T00:00:00Z","timestamp":1643068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0825806"],"award-info":[{"award-number":["2018YFC0825806"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"R&amp;D Program of Beijing Municipal Education Commission","award":["KM202111417007"],"award-info":[{"award-number":["KM202111417007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For multi-temporal high resolution remote sensing images, the image registration is important but difficult due to the high resolution and low-stability land-cover. Especially, the changing of land-cover, solar altitude angle, radiation intensity, and terrain fluctuation distortion in the overlapping areas can represent different image characteristics. These time-varying properties cause traditional registration methods with known reference information to fault. Therefore, in this paper we propose a comprehensive coarse-to-fine registration (CCFR) algorithm. First, we design a low-rank constraint-based batch reference extraction (LRC-BRE) method. Under the low-rank constraint, the stable features with highly spatial co-occurrence can be reconstructed via matrix decomposition, and are set as reference images to batch registration. Second, we improve the general feature registration with block feature matching and local linear transformation (BFM-LLT) operators including match outlier filtering (MOF) on regional mutual information and dual-weighted block fitting (DWBF). Finally, based on combining LRC-BRE and BFM-LLT, CCFR is integrated. Experimental results show that the proposed method has a good batch alignment effect, especially in the registration of large difference image pairs. The proposed CCFR achieves a significant performance improvement over many state-of-the-art registration algorithms.<\/jats:p>","DOI":"10.3390\/rs14030573","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T21:07:11Z","timestamp":1643144831000},"page":"573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Coarse-to-Fine Image Registration for Multi-Temporal High Resolution Remote Sensing Based on a Low-Rank Constraint"],"prefix":"10.3390","volume":"14","author":[{"given":"Peijing","family":"Zhang","sequence":"first","affiliation":[{"name":"School for Informatics and Cyber Security, People\u2019s Public Security University of China, Beijing 100038, China"},{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 100083, China"},{"name":"College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China"}]},{"given":"Xiaoyan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Astronautics, Beihang University, Beijing 102206, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-8322","authenticated-orcid":false,"given":"Yan","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Smart Mechanical Innovation Design Service, Beijing Union University, Beijing 100101, China"},{"name":"College of Robotics, Beijing Union University, Beijing 100101, China"}]},{"given":"Chengyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Remote Sensing Center of Public Security, People\u2019s Public Security University of China, Beijing 100038, China"}]},{"given":"Xu","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7135","DOI":"10.1109\/TGRS.2016.2596290","article-title":"An Integrated Framework for the Spatio\u2013Temporal\u2013Spectral Fusion of Remote Sensing Images","volume":"54","author":"Shen","year":"2016","journal-title":"IEEE Trans. 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