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Articles | Volume XLIII-B3-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1181–1187, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1181-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1181–1187, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1181-2022
 
31 May 2022
31 May 2022

APPLICATION OF SAR TIME-SERIES AND DEEP LEARNING FOR ESTIMATING LANDSLIDE OCCURRENCE TIME

W. Wang1,2, M. Motagh1,2, S. Plank3, A. Orynbaikyzy3, and S. Roessner1 W. Wang et al.
  • 1GFZ German Research Center for Geosciences, Potsdam, Germany
  • 2Leibniz University Hannover, Hannover, Germany
  • 3German Aerospace Center (DLR), Oberpfaffenhofen, Germany

Keywords: Landslide, Deep Learning, SAR, Anomaly Detection, Unsupervised Learning

Abstract. The time series of normalized difference vegetation index (NDVI) and interferometric coherence extracted from optical and Synthetic Aperture Radar (SAR) images, respectively, have strong responses to sudden landslide failures in vegetated regions, which is expressed by a sudden increase or decrease in the values of NDVI and coherence. Compared with optical sensors, SAR sensors are not affected by cloud and daylight conditions and can detect the occurrence time of failure in near real-time. The purpose of this paper is to automatically determine the time of failure occurrence using time series coherence values. We propose, based on some existing anomaly detection algorithms, a deep neural network-based anomaly detection strategy that combines supervised and unsupervised learning without a priori knowledge about failure time. Our experiment using July 21, 2020 Shaziba landslide in China shows that in comparison to widely used unsupervised methodology, the use of our algorithm leads to a more accurate detection of the timing of the landslide failure.