Hierarchical extraction of landslides from multiresolution remotely sensed optical images

Abstract : The automated detection and mapping of landslides from Very High Resolution (VHR) images present several challenges related to the heterogeneity of landslide sizes, shapes and soil surface characteristics. However, a common geomorphological characteristic of landslides is to be organized with a series of embedded and scaled features. These properties motivated the use of a multiresolution image analysis approach for their detection. In this work, we propose a hybrid segmentation/classification region-based method, devoted to this specific issue. The method, which uses images of the same area at various spatial resolutions (Medium to Very High Resolution), relies on a recently introduced top-down hierarchical framework. In the specific context of landslide analysis, two main novelties are introduced to enrich this framework. The first novelty consists of using non-spectral information, obtained from Digital Terrain Model (DTM), as a priori knowledge for the guidance of the segmentation/classification process. The second novelty consists of using a new domain adaptation strategy, that allows to reduce the expert’s interaction when handling large image datasets. Experiments performed on satellite images acquired over terrains affected by landslides demonstrate the efficiency of the proposed method with different hierarchical levels of detail addressing various operational needs.
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Camille Kurtz, André Stumpf, Jean-Philippe Malet, Pierre Gançarski, Anne Puissant, et al.. Hierarchical extraction of landslides from multiresolution remotely sensed optical images. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2014, 87, pp.122-136. ⟨10.1016/j.isprsjprs.2013.11.003⟩. ⟨hal-01694400v1⟩

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