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Open AccessFeature PaperArticle

A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images

1
Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
2
Graduate School of Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
3
Department of Architecture and Building Engineering, Tokyo Institute of Technology, Yokohama 226-8502, Japan
4
International Research Institute of Disaster Science, Tohoku University, Aoba-Ku, Sendai 980-8752, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 561; https://doi.org/10.3390/rs12030561
Received: 10 January 2020 / Revised: 3 February 2020 / Accepted: 5 February 2020 / Published: 8 February 2020
The rapid and accurate mapping of large-scale landslides and other mass movement disasters is crucial for prompt disaster response efforts and immediate recovery planning. As such, remote sensing information, especially from synthetic aperture radar (SAR) sensors, has significant advantages over cloud-covered optical imagery and conventional field survey campaigns. In this work, we introduced an integrated pixel-object image analysis framework for landslide recognition using SAR data. The robustness of our proposed methodology was demonstrated by mapping two different source-induced landslide events, namely, the debris flows following the torrential rainfall that fell over Hiroshima, Japan, in early July 2018 and the coseismic landslide that followed the 2018 Mw6.7 Hokkaido earthquake. For both events, only a pair of SAR images acquired before and after each disaster by the Advanced Land Observing Satellite-2 (ALOS-2) was used. Additional information, such as digital elevation model (DEM) and land cover information, was employed only to constrain the damage detected in the affected areas. We verified the accuracy of our method by comparing it with the available reference data. The detection results showed an acceptable correlation with the reference data in terms of the locations of damage. Numerical evaluations indicated that our methodology could detect landslides with an accuracy exceeding 80%. In addition, the kappa coefficients for the Hiroshima and Hokkaido events were 0.30 and 0.47, respectively. View Full-Text
Keywords: landslide damage detection; the 2018 torrential rain event in hiroshima; Japan; the 2018 Mw6.7 hokkaido earthquake; synthetic aperture radar (SAR) intensity imagery landslide damage detection; the 2018 torrential rain event in hiroshima; Japan; the 2018 Mw6.7 hokkaido earthquake; synthetic aperture radar (SAR) intensity imagery
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Adriano, B.; Yokoya, N.; Miura, H.; Matsuoka, M.; Koshimura, S. A Semiautomatic Pixel-Object Method for Detecting Landslides Using Multitemporal ALOS-2 Intensity Images. Remote Sens. 2020, 12, 561.

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