{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:11:40Z","timestamp":1775819500590,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T00:00:00Z","timestamp":1707264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFB3900900"],"award-info":[{"award-number":["2021YFB3900900"]}]},{"name":"National Key Research and Development Program of China","award":["2021C01031"],"award-info":[{"award-number":["2021C01031"]}]},{"name":"National Key Research and Development Program of China","award":["2022M720121"],"award-info":[{"award-number":["2022M720121"]}]},{"name":"Provincial Key R&amp;D Program of Zhejiang","award":["2021YFB3900900"],"award-info":[{"award-number":["2021YFB3900900"]}]},{"name":"Provincial Key R&amp;D Program of Zhejiang","award":["2021C01031"],"award-info":[{"award-number":["2021C01031"]}]},{"name":"Provincial Key R&amp;D Program of Zhejiang","award":["2022M720121"],"award-info":[{"award-number":["2022M720121"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021YFB3900900"],"award-info":[{"award-number":["2021YFB3900900"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021C01031"],"award-info":[{"award-number":["2021C01031"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022M720121"],"award-info":[{"award-number":["2022M720121"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Deep-time Digital Earth (DDE) Big Science Program","award":["2021YFB3900900"],"award-info":[{"award-number":["2021YFB3900900"]}]},{"name":"Deep-time Digital Earth (DDE) Big Science Program","award":["2021C01031"],"award-info":[{"award-number":["2021C01031"]}]},{"name":"Deep-time Digital Earth (DDE) Big Science Program","award":["2022M720121"],"award-info":[{"award-number":["2022M720121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Pan-sharpening is a fusion process that combines a low-spatial resolution, multi-spectral image that has rich spectral characteristics with a high-spatial resolution panchromatic (PAN) image that lacks spectral characteristics. Most previous learning-based approaches rely on the scale-shift assumption, which may not be applicable in the full-resolution domain. To solve this issue, we regard pan-sharpening as a multi-task problem and propose a Siamese network with Gradient-based Spatial Attention (GSA-SiamNet). GSA-SiamNet consists of four modules: a two-stream feature extraction module, a feature fusion module, a gradient-based spatial attention (GSA) module, and a progressive up-sampling module. In the GSA module, we use Laplacian and Sobel operators to extract gradient information from PAN images. Spatial attention factors, learned from the gradient prior, are multiplied during the feature fusion, up-sampling, and reconstruction stages. These factors help to keep high-frequency information on the feature map as well as suppress redundant information. We also design a multi-resolution loss function that guides the training process under the constraints of both reduced- and full-resolution domains. The experimental results on WorldView-3 satellite images obtained in Moscow and San Juan demonstrate that our proposed GSA-SiamNet is superior to traditional and other deep learning-based methods.<\/jats:p>","DOI":"10.3390\/rs16040616","type":"journal-article","created":{"date-parts":[[2024,2,7]],"date-time":"2024-02-07T03:47:09Z","timestamp":1707277629000},"page":"616","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GSA-SiamNet: A Siamese Network with Gradient-Based Spatial Attention for Pan-Sharpening of Multi-Spectral Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Yi","family":"Gao","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"}]},{"given":"Mengjiao","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 866 Yuhangtang Road, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9322-0149","authenticated-orcid":false,"given":"Sensen","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 866 Yuhangtang Road, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1475-8480","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 866 Yuhangtang Road, Hangzhou 310058, China"}]},{"given":"Zhenhong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, 866 Yuhangtang Road, Hangzhou 310058, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,7]]},"reference":[{"key":"ref_1","first-page":"657","article-title":"Understanding Image Fusion","volume":"70","author":"Zhang","year":"2004","journal-title":"Photogramm. 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