Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.