{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:09:57Z","timestamp":1775146197478,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42071444"],"award-info":[{"award-number":["42071444"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the context of the problem of image blur and nonlinear reflectance difference between bands in the registration of hyperspectral images, the conventional method has a large registration error and is even unable to complete the registration. This paper proposes a robust and efficient registration algorithm based on iterative clustering for interband registration of hyperspectral images. The algorithm starts by extracting feature points using the scale-invariant feature transform (SIFT) to achieve initial putative matching. Subsequently, feature matching is performed using four-dimensional descriptors based on the geometric, radiometric, and feature properties of the data. An efficient iterative clustering method is proposed to perform cluster analysis on the proposed descriptors and extract the correct matching points. In addition, we use an adaptive strategy to analyze the key parameters and extract values automatically during the iterative process. We designed four experiments to prove that our method solves the problem of blurred image registration and multi-modal registration of hyperspectral images. It has high robustness to multiple scenes, multiple satellites, and multiple transformations, and it is better than other similar feature matching algorithms.<\/jats:p>","DOI":"10.3390\/rs13081491","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T12:34:50Z","timestamp":1618317290000},"page":"1491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Interband Registration Method for Hyperspectral Images Based on Adaptive Iterative Clustering"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0725-894X","authenticated-orcid":false,"given":"Shiyong","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China"}]},{"given":"Ruofei","family":"Zhong","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China"}]},{"given":"Qingyang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China"},{"name":"Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China"}]},{"given":"Ke","family":"Qiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Media Convergence Production Technology and Systems, Beijing 100048, China"}]},{"given":"Qing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"ref_1","first-page":"11","article-title":"Fusion of high spatial resolution and high spectral resolution remote sensing images","volume":"000(010)","author":"Ma","year":"2003","journal-title":"Infrared"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"20","author":"Lowe","year":"2004","journal-title":"Int. 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