{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:32:46Z","timestamp":1775745166567,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T00:00:00Z","timestamp":1716940800000},"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>Super-resolution (SR) for satellite remote sensing images has been recognized as crucial and has found widespread applications across various scenarios. Previous SR methods were usually built upon Convolutional Neural Networks and Transformers, which suffer from either limited receptive fields or a lack of prior assumptions. To address these issues, we propose ESatSR, a novel SR method based on state space models. We utilize the 2D Selective Scan to obtain an enhanced capability in modeling long-range dependencies, which contributes to a wide receptive field. A Spatial Context Interaction Module (SCIM) and an Enhanced Image Reconstruction Module (EIRM) are introduced to combine image-related prior knowledge into our model, therefore guiding the process of feature extraction and reconstruction. Tailored for remote sensing images, the interaction of multi-scale spatial context and image features is leveraged to enhance the network\u2019s capability in capturing features of small targets. Comprehensive experiments show that ESatSR demonstrates state-of-the-art performance on both OLI2MSI and RSSCN7 datasets, with the highest PSNRs of 42.11 dB and 31.42 dB, respectively. Extensive ablation studies illustrate the effectiveness of our module design.<\/jats:p>","DOI":"10.3390\/rs16111956","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T03:45:08Z","timestamp":1717040708000},"page":"1956","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["ESatSR: Enhancing Super-Resolution for Satellite Remote Sensing Images with State Space Model and Spatial Context"],"prefix":"10.3390","volume":"16","author":[{"given":"Yinxiao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"},{"name":"Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0165-6071","authenticated-orcid":false,"given":"Wei","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering, Sichuan University, Chengdu 610065, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9354-6093","authenticated-orcid":false,"given":"Fang","family":"Xie","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Baojun","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China"},{"name":"Innovation Academy for Microsatellites, Chinese Academy of Sciences, Shanghai 201210, China"},{"name":"School of Optoelectronics, University of Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9012","DOI":"10.1109\/JSTARS.2021.3108777","article-title":"Unsupervised hyperspectral image change detection via deep learning self-generated credible labels","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. 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