{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T06:36:20Z","timestamp":1780986980402,"version":"3.54.1"},"reference-count":26,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T00:00:00Z","timestamp":1618185600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","award":["IITP-2021-2016-0-00288"],"award-info":[{"award-number":["IITP-2021-2016-0-00288"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, a convolutional neural network-based registration framework is proposed for remote sensing to improve the registration accuracy between two remote-sensed images acquired from different times and viewpoints. The proposed framework consists of four stages. In the first stage, key-points are extracted from two input images\u2014a reference and a sensed image. Then, a patch is constructed at each key-point. The second stage consists of three processes for patch matching\u2014candidate patch pair list generation, one-to-one matched label selection, and geometric distortion compensation. One-to-one matched patch pairs between two images are found, and the exact matching is found by compensating for geometric distortions in the matched patch pairs. A global geometric affine parameter set is computed using the random sample consensus algorithm (RANSAC) algorithm in the third stage. Finally, a registered image is generated after warping the input sensed image using the affine parameter set. The proposed high-accuracy registration framework is evaluated using the KOMPSAT-3 dataset by comparing the conventional frameworks based on machine learning and deep-learning-based frameworks. The proposed framework obtains the least root mean square error value of 34.922 based on all control points and achieves a 68.4% increase in the matching accuracy compared with the conventional registration framework.<\/jats:p>","DOI":"10.3390\/rs13081482","type":"journal-article","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T11:05:06Z","timestamp":1618225506000},"page":"1482","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A CNN-Based High-Accuracy Registration for Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3388-3765","authenticated-orcid":false,"given":"Wooju","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 139701, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2794-9932","authenticated-orcid":false,"given":"Donggyu","family":"Sim","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Kwangwoon University, Seoul 139701, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7249-3647","authenticated-orcid":false,"given":"Seoung-Jun","family":"Oh","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Kwangwoon University, Seoul 139701, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/S0262-8856(03)00137-9","article-title":"Image registration methods: A survey","volume":"21","author":"Zitiva","year":"2003","journal-title":"Image Vis. 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