{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T11:30:57Z","timestamp":1775302257212,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"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":["2020YFA0711403"],"award-info":[{"award-number":["2020YFA0711403"]}]},{"name":"National Key Research and Development Program of China","award":["SAST2019048"],"award-info":[{"award-number":["SAST2019048"]}]},{"name":"Shanghai Aerospace Science and Technology Innovation Fund","award":["2020YFA0711403"],"award-info":[{"award-number":["2020YFA0711403"]}]},{"name":"Shanghai Aerospace Science and Technology Innovation Fund","award":["SAST2019048"],"award-info":[{"award-number":["SAST2019048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In the field of remote sensing image processing, pansharpening technology stands as a critical advancement. This technology aims to enhance multispectral images that possess low resolution by integrating them with high-spatial-resolution panchromatic images, ultimately producing multispectral images with high resolution that are abundant in both spatial and spectral details. Thus, there remains potential for improving the quality of both the spectral and spatial domains of the fused images based on deep-learning-based pansharpening methods. This work proposes a new method for the task of pansharpening: the Multi-Stage Progressive Pansharpening Network with Detail Injection with Redundancy Reduction Mechanism (MSPPN-DIRRM). This network is divided into three levels, each of which is optimized for the extraction of spectral and spatial data at different scales. Particular spectral feature and spatial detail extraction modules are used at each stage. Moreover, a new image reconstruction module named the DRRM is introduced in this work; it eliminates both spatial and channel redundancy and improves the fusion quality. The effectiveness of the proposed model is further supported by experimental results using both simulated data and real data from the QuickBird, GaoFen1, and WorldView2 satellites; these results show that the proposed model outperforms deep-learning-based methods in both visual and quantitative assessments. Among various evaluation metrics, performance improves by 0.92\u201318.7% compared to the latest methods.<\/jats:p>","DOI":"10.3390\/s24186039","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T04:59:54Z","timestamp":1726721994000},"page":"6039","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Multi-Stage Progressive Pansharpening Network Based on Detail Injection with Redundancy Reduction"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4887-638X","authenticated-orcid":false,"given":"Xincan","family":"Wen","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1785-4024","authenticated-orcid":false,"given":"Hongbing","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China"},{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7354-7494","authenticated-orcid":false,"given":"Liangliang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119011","DOI":"10.1016\/j.foreco.2021.119011","article-title":"Diagnosing pristine pine forest development through pansharpened-surface-reflectance Landsat image derived aboveground biomass productivity","volume":"487","author":"Doyog","year":"2021","journal-title":"For. 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