{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:34:36Z","timestamp":1765233276514,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Program of Shaanxi Province of China","award":["2020JQ-600, 2020JM-415"],"award-info":[{"award-number":["2020JQ-600, 2020JM-415"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61801384, 61901371, 61901372, 61877050"],"award-info":[{"award-number":["61801384, 61901371, 61901372, 61877050"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Northwest University Paleontological Bioinformatics Innovation Team","award":["2019TD-012"],"award-info":[{"award-number":["2019TD-012"]}]},{"name":"Key Research and Development Program of Shaanxi Province of China","award":["2020KW-010, 2021KW-05"],"award-info":[{"award-number":["2020KW-010, 2021KW-05"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC1510503"],"award-info":[{"award-number":["2019YFC1510503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A novel super-resolution (SR) method is proposed in this paper to reconstruct high-resolution (HR) remote sensing images. Different scenes of remote sensing images have great disparities in structural complexity. Nevertheless, most existing SR methods ignore these differences, which increases the difficulty to train an SR network. Therefore, we first propose a preclassification strategy and adopt different SR networks to process the remote sensing images with different structural complexity. Furthermore, the main edge of low-resolution images are extracted as the shallow features and fused with the deep features extracted by the network to solve the blurry edge problem in remote sensing images. Finally, an edge loss function and a cycle consistent loss function are added to guide the training process to keep the edge details and main structures in a reconstructed image. A large number of comparative experiments on two typical remote sensing images datasets (WHURS and AID) illustrate that our approach achieves better performance than state-of-the-art approaches in both quantitative indicators and visual qualities. The peak signal-to-noise ratio (PSNR) value and the structural similarity (SSIM) value using the proposed method are improved by 0.5353 dB and 0.0262, respectively, over the average values of five typical deep learning methods on the \u00d74 AID testing set. Our method obtains satisfactory reconstructed images for the subsequent applications based on HR remote sensing images.<\/jats:p>","DOI":"10.3390\/rs14040925","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:58:03Z","timestamp":1644872283000},"page":"925","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Super-Resolution Network for Remote Sensing Images via Preclassification and Deep\u2013Shallow Features Fusion"],"prefix":"10.3390","volume":"14","author":[{"given":"Xiuchao","family":"Yue","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Xiaoxuan","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Wanxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Hang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1026-0060","authenticated-orcid":false,"given":"Lin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Jiayang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"given":"Mengwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4066-1802","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710127, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, Z., Zhang, T., and Ouyang, C. 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