{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:19:48Z","timestamp":1760228388966,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,14]],"date-time":"2022-05-14T00:00:00Z","timestamp":1652486400000},"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":["41971412","42171341"],"award-info":[{"award-number":["41971412","42171341"]}],"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>When mosaicking adjacent nightlight images of a large area that lacks human activities, traditional registration methods have difficulty realizing the tie point registrations due to the lack of structural information. In order to address this issue, this study devises an easy-to-implement engineering solution that allows for the registration of sparse light areas with high efficiency while guaranteeing accuracy in non-sparse light areas. The proposed method first extracts the sparsely distributed light point positions through use of roundness detection and the centroid method. Then, geometric positioning forward and backward algorithms and the random consistency sampling detection algorithm (RANSAC) are used in order to achieve a rough registration of the nightlight images and the remaining tie points are expanded through the affine model. Through experimentation it was found that, compared with traditional registration methods, the proposed method is more reliable and has a wider distribution in sparse light areas. Finally, through the registration test of 275 scenes of nightlight images of China from Luojia-1, the coverage ratio of the tie points was increased from 59.3% from the traditional method to 95.3% in this study and the accuracy of the block adjustment was 0.63 pixels, which verifies the effectiveness of the method. The proposed method provides a basis for the registration, block adjustment, and mosaicking of nightlight images.<\/jats:p>","DOI":"10.3390\/rs14102372","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T09:48:22Z","timestamp":1652608102000},"page":"2372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Luojia-1 Nightlight Image Registration Based on Sparse Lights"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8094-6124","authenticated-orcid":false,"given":"Zhichao","family":"Guan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3987-5336","authenticated-orcid":false,"given":"Guo","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5777-4144","authenticated-orcid":false,"given":"Yonghua","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9692-822X","authenticated-orcid":false,"given":"Xin","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing Satellite, China Academy of Space Technology (CAST), Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9599","DOI":"10.1080\/01431161.2011.569581","article-title":"Population spatialization in China based on night-time imagery and land use data","volume":"32","author":"Zeng","year":"2011","journal-title":"Int. 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