A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture

Abstract

Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder-Decoder architecture for landslide recognition, called the Separable Channel Attention Network (SCANet). The SCANet consists of a Poolformer encoder and a Separable Channel Attention Feature Pyramid Network (SCA-FPN) decoder. Firstly, the Poolformer can extract global semantic information at different levels with the help of transformer architecture, and it greatly reduces computational complexity of the network by using pooling operations instead of a self-attention mechanism. Secondly, the SCA-FPN we designed can fuse multi-scale semantic information and complete pixel-level prediction of remote sensing images. Without bells and whistles, our proposed SCANet outperformed the mainstream semantic segmentation networks with fewer model parameters on our self-built landslide dataset. The mIoU scores of SCANet are 1.95% higher than ResNet50-Unet, especially.