{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:26:51Z","timestamp":1769830011030,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T00:00:00Z","timestamp":1649289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use\/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining the spectral correlation, spectral angle mapper (SAM), and Chebyshev distance. This index was then categorized into three groups of changed, unchanged, and uncertain classes using a fast multiple thresholding technique. In the fine stage, the subject image was first segmented into different clusters by the histogram-based fuzzy c-means (HFCM) algorithm. The optimal PIFs were then selected from unchanged and uncertain regions using each cluster\u2019s bivariate joint distribution analysis. In the RRN modeling step, two normalized subject images were first produced using the robust linear regression (RLR) and cluster-wise-RLR (CRLR) methods based on the clustered PIFs. Finally, the normalized images were fused using the Choquet fuzzy integral fusion strategy for overwhelming the discontinuity between clusters in the final results and keeping the radiometric rectification optimal. Several experiments were implemented on four different bi-temporal satellite images and a simulated dataset to demonstrate the efficiency of the proposed method. The results showed that the proposed method yielded superior RRN results and outperformed other considered well-known RRN algorithms in terms of both accuracy level and execution time.<\/jats:p>","DOI":"10.3390\/rs14081777","type":"journal-article","created":{"date-parts":[[2022,4,7]],"date-time":"2022-04-07T21:08:22Z","timestamp":1649365702000},"page":"1777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Integral Fusion Strategies"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0455-4882","authenticated-orcid":false,"given":"Armin","family":"Moghimi","sequence":"first","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"},{"name":"Institute of Photogrammetry and GeoInformation (IPI), Leibniz Universit\u00e4t Hannover (LUH), 30167 Hannover, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3329-5063","authenticated-orcid":false,"given":"Ali","family":"Mohammadzadeh","sequence":"additional","affiliation":[{"name":"Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6925-6010","authenticated-orcid":false,"given":"Turgay","family":"Celik","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg 2000, South Africa"},{"name":"The Wits Institute of Data Science, University of the Witwatersrand, Johannesburg 2000, South Africa"},{"name":"Faculty of Engineering and Science, University of Agder, 4630 Kristiansand, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8439-362X","authenticated-orcid":false,"given":"Brian","family":"Brisco","sequence":"additional","affiliation":[{"name":"The Canada Center for Mapping and Earth Observation, Ottawa, ON K1S 5K2, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9495-4010","authenticated-orcid":false,"given":"Meisam","family":"Amani","sequence":"additional","affiliation":[{"name":"Wood Environment & Infrastructure Solutions, Ottawa, ON K2E 7L5, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/0034-4257(91)90062-B","article-title":"Radiometric Rectification: Toward a Common Radiometric Response among Multidate, Multisensor Images","volume":"35","author":"Hall","year":"1991","journal-title":"Remote Sens. 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