Building Damage Assessment Using Intensity SAR Data with Different Incidence Angles and Longtime Interval
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- Ge Pinglan
- Graduate School of Engineering, The University of Tokyo
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- Gokon Hideomi
- Institute of Industrial Science, The University of Tokyo
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- Meguro Kimiro
- Institute of Industrial Science, The University of Tokyo
書誌事項
- 公開日
- 2019-03-28
- 資源種別
- journal article
- DOI
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- 10.20965/jdr.2019.p0456
- 公開者
- 富士技術出版株式会社
この論文をさがす
説明
<p>When carrying out change detection for building damage assessment using synthetic aperture radar (SAR) intensity images, it is desirable that the observation conditions of the images are similar and acquisition time is close to the earthquake occurrence time. In this way, the influence of the radar operating system and ground temporal changes can be minimized, facilitating high-accuracy assessment results. However, in practice, especially in poor developing areas, it is difficult to obtain ideal images owing to limited pre-event data archives. In the 2015 Gorkha, Nepal earthquake, the TerraSAR-X satellite captured the influenced Sankhu area before and after the earthquake on May 30, 2010 and May 13, 2015, respectively. The pre-event data was obtained in an ascending path with an incidence angle of 41°, whereas the post-event data was obtained in a descending path with an incidence angle of 33°. To apply the obtained data that had different observation conditions and longtime intervals for building damage assessment, two ways were considered and studied. On one hand, the feasibility of change detection considering these factors was investigated. Pixel statistic characteristics were analyzed in twelve test areas to check the influence of temporal changes, and building footprints were buffered considering two different incidence angles. On the other hand, the reliability of classification based on only post-event data was studied. The results showed good classification performance of some texture parameters, such as the “range value” and “standard deviation,” which are worthy of further study. Moreover, the classification results obtained using the post-event data achieved similar accuracy to that using both the pre- and post-event data, preliminarily indicating the research value of post-event data-based building damage detection as it can solve the pre-event data limitation problem once and for all.</p>
収録刊行物
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- Journal of Disaster Research
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Journal of Disaster Research 14 (3), 456-465, 2019-03-28
富士技術出版株式会社
