Reg-SA–UNet++: A Lightweight Landslide Detection Network Based on Single-Temporal Images Captured Postlandslide
Chaoyang Niu, Ouyang Gao, Wanjie Lu, Wei Liu, Tao Lai
Abstract
Landslide detection based on remote sensing images is an effective method for rapidly and accurately detecting landslide regions, which can aid in disaster prevention and mitigation. Landslide detection methods based on semantic segmentation can be used to delineate the scope of landslides while detecting their location. Most existing models use multi-temporal or geological data to improve accuracy. However, the large amount of data introduces additional parameters, consuming significant computing resources. Therefore, this study proposes Reg-SA–UNet++, a model for landslide detection, which uses a single-temporal image captured post-landslide. Reg-SA–UNet++ is based on UNet++ with the following modifications: deep supervised pruning is removed for fewer model parameters and increased detection accuracy; RegNet is employed to replace the convolutional blocks in the encoding process to reduce the number of parameters and improve feature acquisition and attention modules are added at the connection of the convolutional blocks of each layer to strengthen the model's attention to landslide features. The overall accuracy and F1 score of the Reg-SA–UNet++ model for the constructed landslide dataset (93.37% and 92.41%, respectively) and landslide mapping (97.09% and 96.10%, respectively) verify the effectiveness of the proposed model in detecting landslides from remote sensing images.