{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T12:00:06Z","timestamp":1775736006650,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,19]],"date-time":"2020-02-19T00:00:00Z","timestamp":1582070400000},"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>Pansharpening is the process of fusing a low-resolution multispectral (LRMS) image with a high-resolution panchromatic (PAN) image. In the process of pansharpening, the LRMS image is often directly upsampled by a scale of 4, which may result in the loss of high-frequency details in the fused high-resolution multispectral (HRMS) image. To solve this problem, we put forward a novel progressive cascade deep residual network (PCDRN) with two residual subnetworks for pansharpening. The network adjusts the size of an MS image to the size of a PAN image twice and gradually fuses the LRMS image with the PAN image in a coarse-to-fine manner. To prevent an overly-smooth phenomenon and achieve high-quality fusion results, a multitask loss function is defined to train our network. Furthermore, to eliminate checkerboard artifacts in the fusion results, we employ a resize-convolution approach instead of transposed convolution for upsampling LRMS images. Experimental results on the Pl\u00e9iades and WorldView-3 datasets prove that PCDRN exhibits superior performance compared to other popular pansharpening methods in terms of quantitative and visual assessments.<\/jats:p>","DOI":"10.3390\/rs12040676","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"676","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["PCDRN: Progressive Cascade Deep Residual Network for Pansharpening"],"prefix":"10.3390","volume":"12","author":[{"given":"Yong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuying","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangyuan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330032, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.inffus.2016.03.003","article-title":"A review of remote sensing image fusion methods","volume":"32","author":"Ghassemian","year":"2016","journal-title":"Inf. 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