{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T20:23:06Z","timestamp":1779308586793,"version":"3.51.4"},"reference-count":67,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T00:00:00Z","timestamp":1723075200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Qinghai Province","award":["2024-ZJ-708"],"award-info":[{"award-number":["2024-ZJ-708"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pansharpening, a pivotal task in remote sensing, involves integrating low-resolution multispectral images with high-resolution panchromatic images to synthesize an image that is both high-resolution and retains multispectral information. These pansharpened images enhance precision in land cover classification, change detection, and environmental monitoring within remote sensing data analysis. While deep learning techniques have shown significant success in pansharpening, existing methods often face limitations in their evaluation, focusing on restricted satellite data sources, single scene types, and low-resolution images. This paper addresses this gap by introducing PanBench, a high-resolution multi-scene dataset containing all mainstream satellites and comprising 5898 pairs of samples. Each pair includes a four-channel (RGB + near-infrared) multispectral image of 256 \u00d7 256 pixels and a mono-channel panchromatic image of 1024 \u00d7 1024 pixels. To avoid irreversible loss of spectral information and achieve a high-fidelity synthesis, we propose a Cascaded Multiscale Fusion Network (CMFNet) for pansharpening. Multispectral images are progressively upsampled while panchromatic images are downsampled. Corresponding multispectral features and panchromatic features at the same scale are then fused in a cascaded manner to obtain more robust features. Extensive experiments validate the effectiveness of CMFNet.<\/jats:p>","DOI":"10.3390\/rs16162899","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T12:13:14Z","timestamp":1723119194000},"page":"2899","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Towards Robust Pansharpening: A Large-Scale High-Resolution Multi-Scene Dataset and Novel Approach"],"prefix":"10.3390","volume":"16","author":[{"given":"Shiying","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining 810016, China"},{"name":"Intelligent Computing and Application Laboratory of Qinghai Province, Qinghai University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuechao","family":"Zou","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6801-0510","authenticated-orcid":false,"given":"Junliang","family":"Xing","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengfei","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining 810016, China"},{"name":"Intelligent Computing and Application Laboratory of Qinghai Province, Qinghai University, Xining 810016, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pin","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Computer Technology and Application, Qinghai University, Xining 810016, China"},{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2020.12.014","article-title":"GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening","volume":"172","author":"Zhang","year":"2021","journal-title":"ISPRS-J. 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