{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T01:46:55Z","timestamp":1769824015141,"version":"3.49.0"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42071411"],"award-info":[{"award-number":["42071411"]}]},{"name":"National Natural Science Foundation of China","award":["XDA23090203"],"award-info":[{"award-number":["XDA23090203"]}]},{"name":"National Natural Science Foundation of China","award":["2019QZKK0902"],"award-info":[{"award-number":["2019QZKK0902"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFG0200"],"award-info":[{"award-number":["2022YFG0200"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["42071411"],"award-info":[{"award-number":["42071411"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA23090203"],"award-info":[{"award-number":["XDA23090203"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2019QZKK0902"],"award-info":[{"award-number":["2019QZKK0902"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2022YFG0200"],"award-info":[{"award-number":["2022YFG0200"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["42071411"],"award-info":[{"award-number":["42071411"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["XDA23090203"],"award-info":[{"award-number":["XDA23090203"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["2019QZKK0902"],"award-info":[{"award-number":["2019QZKK0902"]}]},{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program","award":["2022YFG0200"],"award-info":[{"award-number":["2022YFG0200"]}]},{"name":"Key Research and Development Program of Sichuan Province","award":["42071411"],"award-info":[{"award-number":["42071411"]}]},{"name":"Key Research and Development Program of Sichuan Province","award":["XDA23090203"],"award-info":[{"award-number":["XDA23090203"]}]},{"name":"Key Research and Development Program of Sichuan Province","award":["2019QZKK0902"],"award-info":[{"award-number":["2019QZKK0902"]}]},{"name":"Key Research and Development Program of Sichuan Province","award":["2022YFG0200"],"award-info":[{"award-number":["2022YFG0200"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring.<\/jats:p>","DOI":"10.3390\/rs15184498","type":"journal-article","created":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T05:08:21Z","timestamp":1694581701000},"page":"4498","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Super-Resolution Network for High-Resolution Reconstruction of Landslide Main Bodies in Remote Sensing Imagery Using Coordinated Attention Mechanisms and Deep Residual Blocks"],"prefix":"10.3390","volume":"15","author":[{"given":"Huajun","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Chengming","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Yuzhan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Rong","family":"Tang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]},{"given":"Ruilong","family":"Wei","sequence":"additional","affiliation":[{"name":"Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Geophysics, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s10346-013-0436-y","article-title":"The Varnes Classification of Landslide Types, an Update","volume":"11","author":"Hungr","year":"2014","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1007\/s10346-015-0674-2","article-title":"Sediment Failure Types, Preconditions and Triggering Factors in the Gulf of Cadiz","volume":"14","author":"Leynaud","year":"2017","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, L., Qiu, H., Zhou, W., Zhu, Y., Liu, Z., Ma, S., Yang, D., and Tang, B. 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