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• W3131444645 abstract "Synthetic aperture radar (SAR) polarimetry has demonstrated high efficiency in the detection of landslides in vegetated mountainous areas. In such places, post-landslide soil layers appear to correspond to the typical surface scattering mechanism, which is significantly different from the volume scattering behaviour of the surrounding vegetation. However, a landslide in the complex surroundings of various landforms, involving naked hillslopes, construction fields, bare farmlands, and other such aspects, may not be accurately identified owing to the occurrence of surface scattering behaviours. In order to detect landslides using SAR polarimetry without the limitation of vegetated mountainous areas, we propose a novel method of combining change detection (CD) and an analytic hierarchy process (AHP) based on the Yamaguchi decomposition (YD) to identify landslides while ensuring fewer false alarms. In particular, CD is applied to a pair of pre- and post-event datasets to determine the regions modified by landslides or human activities, and the AHP is performed over the post-event dataset to identify the suspect landslide region characterised by the surface scattering mechanism. Finally, the two results are fused by a logical operation to identify the actual landslide by removing the non-modified surface scattering regions. A case study of the Shenzhen landslide in complex surroundings was considered to verify the performance of the proposed method (CD-AHP). The results indicate that the method could clearly define the main body of the Shenzhen landslide from the city suburbs with a small number of false alarms. Therefore, this method provides a considerable perspective for landslide detection in complex surroundings using SAR polarimetry." @default.
• W3131444645 created "2021-03-01" @default.
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• W3131444645 date "2021-04-01" @default.
• W3131444645 modified "2023-10-18" @default.
• W3131444645 title "Using a fully polarimetric SAR to detect landslide in complex surroundings: Case study of 2015 Shenzhen landslide" @default.
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• W3131444645 doi "https://doi.org/10.1016/j.isprsjprs.2021.01.022" @default.
• W3131444645 hasPublicationYear "2021" @default.
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