Multiscale framework for rapid change analysis from SAR image time series: Case study of flood monitoring in the central coast regions of Vietnam
2021
Lê, Thu Trang | Froger, Jean-Luc | Ho Tong Minh, Dinh | Laboratoire Magmas et Volcans (LMV) ; Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA) | Hanoi University of Mining and Geology (HUMG) | Laboratoire Magmas et Volcans (LMV-ENSMSE) ; École des Mines de Saint-Étienne (Mines Saint-Étienne MSE) ; Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-SPIN-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Magmas et Volcans (LMV) ; Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Clermont Auvergne (UCA) | Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement (LGL-TPE) ; École normale supérieure de Lyon (ENS de Lyon) ; Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL) ; Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS) | Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | IDEX-ISITE initiative 16-IDEX-0001 (CAP 20-25) | ANR-16-IDEX-0001,CAP 20-25,CAP 20-25(2016)
International audience
Mostrar más [+] Menos [-]Inglés. Recently, the frequency of natural and environmental disasters has increased significantly, causing constant changes on the Earth's surface. Synthetic Aperture Radar (SAR) data have been proved to be useful for operational change monitoring tasks. The multiscale framework presented in this paper aims at detecting and analyzing changes using SAR image time series composed of large-size images. Spatio-temporal changes are initially detected at the subimage scale analysis stage to determine regions and image acquisition dates related to the change occurrence. Detailed changes are then identified at the pixel scale analysis stage between selected acquisitions at each recognized region. This framework was used for flood monitoring over a large area along the central coast of Vietnam (from Thua Thien Hue province to Quang Nam province). We exploited a Sentinel-1 image time series acquired during two rainy seasons and typhoon seasons in the Western Pacific (from September to December of the two years 2017 and 2018). The proposed framework detected flooded areas with a high overall accuracy of 90.4% and could analyze different types of changes that occurred in this time series, i.e. dirac, periodic, chaotic changes, and temporal stability.
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