Automatic identification of active landslides over wide areas from time-series InSAR measurements using Faster RCNN
Citation
Cai, J., Zhang, L., Dong, J., Guo, J., Wang, Y., Liao, M. (2023). Automatic identification of active landslides over wide areas from time-series InSAR measurements using Faster RCNN. International Journal of Applied Earth Observation and Geoinformation, 124: 103516. Link to paper
Abstract
The method performs InSAR analysis to produce a surface displacement velocity map of the target region and then employs an improved Faster RCNN based on attended ResNet-34 and Feature Pyramid Networks (FPN) to detect active landslides from the velocity map. The study focused on Guizhou province in southwest China, where researchers processed 1168 scenes of Sentinel-1 images and 473 scenes of PALSAR-2 images to derive the surface displacement and identified 1627 active landslides, including 326 manually labeled landslides and 1301 landslides automatically detected by Faster RCNN. The improved Faster RCNN achieved good recall, precision, F1 score, and average precision (AP) at 91.49%, 91.33%, 0.914, and 0.940, respectively.