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University","award":["ZYN2023018"],"award-info":[{"award-number":["ZYN2023018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Achieving a balance between spectral resolution and spatial resolution in multi-spectral remote sensing images is challenging due to physical constraints. Consequently, pan-sharpening technology was developed to address this challenge. While significant progress was recently achieved in deep-learning-based pan-sharpening techniques, most existing deep learning approaches face two primary limitations: (1) convolutional neural networks (CNNs) struggle with long-range dependency issues, and (2) significant detail loss during deep network training. Moreover, despite these methods\u2019 pan-sharpening capabilities, their generalization to full-sized raw images remains problematic due to scaling disparities, rendering them less practical. To tackle these issues, we introduce in this study a multi-spectral remote sensing image fusion network, termed TAMINet, which leverages a two-stream coordinate attention mechanism and multi-detail injection. Initially, a two-stream feature extractor augmented with the coordinate attention (CA) block is employed to derive modal-specific features from low-resolution multi-spectral (LRMS) images and panchromatic (PAN) images. This is followed by feature-domain fusion and pan-sharpening image reconstruction. Crucially, a multi-detail injection approach is incorporated during fusion and reconstruction, ensuring the reintroduction of details lost earlier in the process, which minimizes high-frequency detail loss. Finally, a novel hybrid loss function is proposed that incorporates spatial loss, spectral loss, and an additional loss component to enhance performance. The proposed methodology\u2019s effectiveness was validated through experiments on WorldView-2 satellite images, IKONOS, and QuickBird, benchmarked against current state-of-the-art techniques. Experimental findings reveal that TAMINet significantly elevates the pan-sharpening performance for large-scale images, underscoring its potential to enhance multi-spectral remote sensing image quality.<\/jats:p>","DOI":"10.3390\/rs16010075","type":"journal-article","created":{"date-parts":[[2023,12,24]],"date-time":"2023-12-24T20:48:37Z","timestamp":1703450917000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Pan-Sharpening Network of Multi-Spectral Remote Sensing Images Using Two-Stream Attention Feature Extractor and Multi-Detail Injection (TAMINet)"],"prefix":"10.3390","volume":"16","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mathematics, Southwest Minzu University, Chengdu 610041, China"},{"name":"School of Information and Business Management, Chengdu Neusoft University, Chengdu 611844, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5740-8179","authenticated-orcid":false,"given":"Jiaqing","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Minzu University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaoping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Minzu University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1186-7289","authenticated-orcid":false,"given":"Ying","family":"Tan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer System, State Ethnic Affairs Commission, College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shicheng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Big Data and Artificial Intelligence, Chengdu Technological University, Chengdu 611730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoguang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Minzu University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mathematics, Southwest Minzu University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guibing","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computer System, State Ethnic Affairs Commission, College of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, China"},{"name":"School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s12665-011-1112-y","article-title":"Application of Hyperspectral Remote Sensing for Environment Monitoring in Mining Areas","volume":"65","author":"Zhang","year":"2012","journal-title":"Environ. 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