{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:52:33Z","timestamp":1775145153538,"version":"3.50.1"},"reference-count":68,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T00:00:00Z","timestamp":1649721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015524","name":"China Three Gorges Corporation","doi-asserted-by":"publisher","award":["YMJ(XLD)(19)110"],"award-info":[{"award-number":["YMJ(XLD)(19)110"]}],"id":[{"id":"10.13039\/100015524","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2018YFC1505002"],"award-info":[{"award-number":["2018YFC1505002"]}],"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>At present, Synthetic Aperture Radar Interferometry (InSAR) has been an important technique for active landslides recognition in the geological survey field. However, the traditional interpretation method through human\u2013computer interaction highly relies on expert experience, which is time-consuming and subjective. To solve the problem, this study designed an end-to-end semantic segmentation network, called deep residual shrinkage U-Net (DRs-UNet), to automatically extract potential active landslides in InSAR imagery. The proposed model was inspired by the structure of U-Net and adopted a residual shrinkage building unit (RSBU) as the feature extraction block in its encoder part. The method of this study has three main advantages: (1) The RSBU in the encoder part incorporated with soft thresholding can reduce the influence of noise from InSAR images. (2) The residual connection of the RSBU makes the training of the network easier and accelerates the convergency process. (3) The feature fusion of the corresponding layers between the encoder and decoder effectively improves the classification accuracy. Two widely used networks, U-Net and SegNet, were trained under the same experiment environment to compare with the proposed method. The experiment results in the test set show that our method achieved the best performance; specifically, the F1 score is 1.48% and 4.1% higher than U-Net and SegNet, which indicates a better balance between precision and recall. Additionally, our method has the best IoU score of over 90%. Furthermore, we applied our network to a test area located in Zhongxinrong County along Jinsha River where landslides are highly evolved. The quantitative evaluation results prove that our method is effective for the automatic recognition of potential active landslide hazards from InSAR imagery.<\/jats:p>","DOI":"10.3390\/rs14081848","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T22:48:45Z","timestamp":1649803725000},"page":"1848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai\u2013Tibet Plateau"],"prefix":"10.3390","volume":"14","author":[{"given":"Ximing","family":"Chen","sequence":"first","affiliation":[{"name":"Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China"},{"name":"School of Geography and Information Engineering, China University of Geoscience (Wuhan), Wuhan 430074, China"}]},{"given":"Xin","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China"}]},{"given":"Zhenkai","family":"Zhou","sequence":"additional","affiliation":[{"name":"Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China"},{"name":"School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China"},{"name":"School of Geography and Information Engineering, China University of Geoscience (Wuhan), Wuhan 430074, China"}]},{"given":"Chuangchuang","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China"}]},{"given":"Kaiyu","family":"Ren","sequence":"additional","affiliation":[{"name":"Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing 100081, China"},{"name":"Key Laboratory of Active Tectonics and Geological Safety, Ministry of Natural Resources, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6166","DOI":"10.1109\/JSTARS.2020.3028855","article-title":"A new deep-learning-based approach for earthquake-triggered landslide detection from single-temporal RapidEye satellite imagery","volume":"13","author":"Yi","year":"2020","journal-title":"IEEE J. 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