Landslide extraction model for remote sensing images based on improved DeepLabv3+

In order to realize the accurate recognition of landslides in remote sensing images, an improved DeepLabv3+ landslide extraction model is proposed in this paper. (1) Hybrid Module and Attention Module based CSPNet (HA-CSPNet) is constructed as the backbone feature extraction network to enhance the feature information of small landslides and suppress the interference of irrelevant background information. (2) Combining the advantages of Residual Structure and Dense Connected Module, Residual-dense ASPP is designed to focus landslide features at different scales, enhance feature reuse and prevent gradient vanishing. The experimental results show that the landslide extraction model proposed in this paper is practical. It can improve the accuracy of landslide semantic segmentation.