Landslide mapping from polarimetric SAR images using deep learning and morphological model

Rapid and accurate mapping of landslides following triggering events such as intense rainfall is critical for emergency response, hazard assessment, and disaster mitigation, particularly in mountainous regions prone to frequent slope failures. The effectiveness of optical imagery in such contexts is often limited by persistent cloud cover and fog during the rainy season, whereas synthetic aperture radar (SAR) provides all-weather, day-and-night observations, making it a suitable alternative for time-sensitive mapping. This study presents a deep learning framework incorporating a morphological optimization model for precise landslide detection from a single post-event polarimetric SAR acquisition. The morphological optimization model is introduced to impose shape- and connectivity-aware constraints on SAR-derived candidates, suppressing isolated false alarms and improving boundary continuity under speckle and terrain-induced noise. The approach combines polarimetric parameter selection, a Deep Forest classifier, a majority voting scheme, and shape constraints to enhance boundary delineation. A rainfall-induced landslide in Bijie City, Guizhou Province, China, was selected as the case study, using quad-polarization ALOS-2 and dual-polarization Sentinel-1 imagery. The observed performance gap between ALOS-2 and Sentinel-1 is consistent with the stronger vegetation penetration and reduced volume-scattering sensitivity of L-band compared with C-band in humid mountainous terrain. The quad-pol ALOS-2 data achieved the highest overall accuracy (95.2%), followed by dual-pol ALOS-2 (89.8%), while Sentinel-1 provided effective regional-scale detection (76.3%). The results confirm the method’s potential for rapid, reliable landslide mapping in complex mountainous terrain, and its applicability to operational disaster assessment in regions with similar geomorphological and climatic conditions.