{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T18:19:48Z","timestamp":1767896388693,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,10,27]],"date-time":"2016-10-27T00:00:00Z","timestamp":1477526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371413"],"award-info":[{"award-number":["41371413"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Program of the National Natural Science Foundation of China","award":["41331176"],"award-info":[{"award-number":["41331176"]}]},{"name":"TerraSAR-X AO project","award":["LAN2456"],"award-info":[{"award-number":["LAN2456"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared with optical sensors, Synthetic Aperture Radar (SAR) can provide important damage information due to its ability to map areas affected by earthquakes independently from weather conditions and solar illumination. In 2013, a new TerraSAR-X mode named staring spotlight (ST), whose azimuth resolution was improved to 0.24 m, was introduced for various applications. This data source made it possible to extract detailed information from individual buildings. In this paper, we present a new concept for individual building damage assessment using a post-event sub-meter very high resolution (VHR) SAR image and a building footprint map. With the building footprint map, the original footprints of buildings can be located in the SAR image. Based on the building imaging analysis of a building in the SAR image, the features in the building footprint can be extracted to identify standing and collapsed buildings. Three machine learning classifiers, including random forest (RF), support vector machine (SVM) and K-nearest neighbor (K-NN), are used in the experiments. The results show that the proposed method can obtain good overall accuracy, which is above 80% with the three classifiers. The efficiency of the proposed method is demonstrated based on samples of buildings using descending and ascending sub-meter VHR ST images, which were all acquired from the same area in old Beichuan County, China.<\/jats:p>","DOI":"10.3390\/rs8110887","type":"journal-article","created":{"date-parts":[[2016,10,27]],"date-time":"2016-10-27T10:17:52Z","timestamp":1477563472000},"page":"887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Earthquake-Induced Building Damage Detection with Post-Event Sub-Meter VHR TerraSAR-X Staring Spotlight Imagery"],"prefix":"10.3390","volume":"8","author":[{"given":"Lixia","family":"Gong","sequence":"first","affiliation":[{"name":"Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China"},{"name":"School of Civil Engineering and Geosciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK"}]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9280-8378","authenticated-orcid":false,"given":"Fan","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Jingfa","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0088-8148","authenticated-orcid":false,"given":"Hong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Crustal Dynamics, China Earthquake Administration, Beijing 100085, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4870","DOI":"10.3390\/rs6064870","article-title":"Rapid damage assessment by means of multi-temporal SAR-A comprehensive review and outlook to sentinel-1","volume":"6","author":"Plank","year":"2014","journal-title":"Remote Sens."},{"key":"ref_2","first-page":"195","article-title":"A survey of earthquake damage detection and assessment of building using SAR imagery","volume":"33","author":"Gong","year":"2013","journal-title":"J. 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