{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:58:33Z","timestamp":1774493913734,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T00:00:00Z","timestamp":1682553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004613","name":"China Geological Survey","doi-asserted-by":"publisher","award":["DD20211365"],"award-info":[{"award-number":["DD20211365"]}],"id":[{"id":"10.13039\/501100004613","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>SAR interferometry (InSAR) has emerged in the big-data era, particularly benefitting from the acquisition capability and open-data policy of ESA\u2019s Sentinel-1 SAR mission. A large number of Sentinel-1 SAR images have been acquired and archived, allowing for the generation of thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications actually aim at monitoring localized deformation sparsely distributed in the interferogram. Thus, it is not effective to apply the time-series InSAR analysis to the whole image and identify the deformed targets from the derived velocity map. Here, we present a strategy facilitated by the deep learning networks to firstly detect the localized deformation and then carry out the time-series analysis on small interferogram patches with deformation signals. Specifically, we report following-up studies of our proposed deep learning networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. In the applications of mining-induced subsidence monitoring and slow-moving landslide detection, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and unwrapping errors. The presented detection-first strategy introduces deep learning to the time-series InSAR processing chain and makes the mission of operationally monitoring localized deformation feasible and efficient for the large-scale InSAR.<\/jats:p>","DOI":"10.3390\/rs15092310","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T01:33:40Z","timestamp":1682645620000},"page":"2310","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3729-0139","authenticated-orcid":false,"given":"Teng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Earth and Space Sciences, Peking University, Beijing 100871, China"},{"name":"Center for Habitable Intelligent Planet, Institute of Artificial Intelligence, Peking University, Beijing 100871, China"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth and Space Sciences, Peking University, Beijing 100871, China"}]},{"given":"Zhipeng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2011.09.030","article-title":"Sentinel 1 SAR interferometry applications: The outlook for sub millimeter measurements","volume":"120","author":"Rucci","year":"2012","journal-title":"Remote. 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