Improved U-Net Network Segmentation Method for Remote Sensing Image

Semantic segmentation and extraction based on remote sensing images has important theory and significance. Deep learning has become one of the mainstream methods to extract information from remote sensing images. In this paper, based on the improvement of U-Net network structure, we combine ASPP and skip connection. Improve the residual module to improve the information extraction method. The main improvements of this paper are: 1 Based on the U-Net network structure, we use the multi-scale feature detection capabilities of Pyramid to introduce. The ASPP module and the residual structure are improved, paying more attention to semantic and detail informatization, overcoming the limitations of U-Net in small target detection; 2 We have improved the U-Net network, using skip connections to get more layers of information. Experiments show that the model proposed in this paper has significantly higher MPA and MIOU than the U-Net model on both the VOC dataset and the Vaihingen dataset. It means that ARU-Net can extract information better.