{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:20Z","timestamp":1760145980531,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T00:00:00Z","timestamp":1727308800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62073161","2022r083","2022r078","23KJB510012"],"award-info":[{"award-number":["62073161","2022r083","2022r078","23KJB510012"]}]},{"name":"Startup Foundation for Introducing Talent of NUIST","award":["62073161","2022r083","2022r078","23KJB510012"],"award-info":[{"award-number":["62073161","2022r083","2022r078","23KJB510012"]}]},{"name":"Natural Science Foundation of the Jiangsu Higher Education Institutions of China","award":["62073161","2022r083","2022r078","23KJB510012"],"award-info":[{"award-number":["62073161","2022r083","2022r078","23KJB510012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Robust and efficient multi-source image matching remains a challenging task due to nonlinear radiometric differences between image features. This paper proposes a pixel-level matching framework for multi-source images to overcome this issue. Firstly, a novel descriptor called channel features of phase congruency (CFPC) is first derived at each control point to create a pixelwise feature representation. The proposed CFPC is not only simple to construct but is also highly efficient and somewhat insensitive to noise and intensity changes. Then, a Fast Sequential Similarity Detection Algorithm (F-SSDA) is proposed to further improve the matching efficiency. Comparative experiments are conducted by matching different types of multi-source images (e.g., Visible\u2013SAR; LiDAR\u2013Visible; visible\u2013infrared). The experimental results demonstrate that the proposed method can achieve pixel-level matching accuracy with high computational efficiency.<\/jats:p>","DOI":"10.3390\/rs16193589","type":"journal-article","created":{"date-parts":[[2024,9,26]],"date-time":"2024-09-26T06:55:52Z","timestamp":1727333752000},"page":"3589","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Fast Sequential Similarity Detection Algorithm for Multi-Source Image Matching"],"prefix":"10.3390","volume":"16","author":[{"given":"Quan","family":"Wu","sequence":"first","affiliation":[{"name":"The School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210000, China"}]},{"given":"Qida","family":"Yu","sequence":"additional","affiliation":[{"name":"The School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, R., Gao, X., and Shi, F. 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