2024/08/22 by Qiming Yang, Zixin Wang, Yang, Qiming +5 · 2 citations
Business, Management and Accounting · Earth and Planetary Sciences · #Computer Vision and Pattern Recognition (cs.CV) #Distributed #E-commerce and Technology Innovations #FOS: Computer and information sciences #Parallel #Remote Sensing and Land Use #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2408.12672
openalex publication_date 2024/08/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, although U-Net network has made significant progress in the field of image segmentation, it still faces performance bottlenecks in remote sensing image segmentation. In this paper, we innovatively propose to introduce SimAM and CBAM attention mechanism in U-Net, and the experimental results show that after adding SimAM and CBAM modules alone, the model improves 17.41% and 12.23% in MIoU, and the Mpa and Accuracy are also significantly improved. And after fusing the two,the model performance jumps up to 19.11% in MIoU, and the Mpa and Accuracy are also improved by 16.38% and 14.8% respectively, showing excellent segmentation accuracy and visual effect with strong generalization ability and robustness. This study opens up a new path for remote sensing image segmentation technology and has important reference value for algorithm selection and improvement.