{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:10:29Z","timestamp":1774030229195,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,4,21]],"date-time":"2017-04-21T00:00:00Z","timestamp":1492732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pan-sharpening aims to sharpen a low spatial resolution multispectral (MS) image by combining the spatial detail information extracted from a panchromatic (PAN) image. An effective pan-sharpening method should produce a high spatial resolution MS image while preserving more spectral information. Unlike traditional intensity-hue-saturation (IHS)- and principal component analysis (PCA)-based multiscale transform methods, a novel pan-sharpening framework based on the matting model (MM) and multiscale transform is presented in this paper. First, we use the intensity component (I) of the MS image as the alpha channel to generate the spectral foreground and background. Then, an appropriate multiscale transform is utilized to fuse the PAN image and the upsampled I component to obtain the fused high-resolution gray image. In the fusion, two preeminent fusion rules are proposed to fuse the low- and high-frequency coefficients in the transform domain. Finally, the high-resolution sharpened MS image is obtained by linearly compositing the fused gray image with the upsampled foreground and background images. The proposed framework is the first work in the pan-sharpening field. A large number of experiments were tested on various satellite datasets; the subjective visual and objective evaluation results indicate that the proposed method performs better than the IHS- and PCA-based frameworks, as well as other state-of-the-art pan-sharpening methods both in terms of spatial quality and spectral maintenance.<\/jats:p>","DOI":"10.3390\/rs9040391","type":"journal-article","created":{"date-parts":[[2017,4,21]],"date-time":"2017-04-21T10:59:30Z","timestamp":1492772370000},"page":"391","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["A Novel Pan-Sharpening Framework Based on Matting Model and Multiscale Transform"],"prefix":"10.3390","volume":"9","author":[{"given":"Yong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3537-979X","authenticated-orcid":false,"given":"Weiguo","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China"}]},{"given":"Shuying","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China"}]},{"given":"Pan","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Biomedical Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Yue","family":"Que","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,4,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6828","DOI":"10.3390\/rs70606828","article-title":"A new look at image fusion methods from a Bayesian perspective","volume":"7","author":"Zhang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1109\/TGRS.2016.2606324","article-title":"Sensitivity of pansharpening methods to temporal and instrumental changes between multispectral and panchromatic data sets","volume":"50","author":"Aiazzi","year":"2017","journal-title":"IEEE Trans. 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