Research on Improved U-net Based Remote Sensing Image Segmentation Algorithm

TL;DR

This paper enhances U-Net for remote sensing image segmentation by integrating attention mechanisms SimAM and CBAM, significantly improving accuracy, robustness, and generalization over previous models.

Contribution

Introduces a novel combination of SimAM and CBAM attention modules into U-Net, leading to substantial performance improvements in remote sensing image segmentation.

Findings

Model MIoU improved by up to 19.11%

Accuracy increased by approximately 14.8%

Enhanced segmentation robustness and generalization

Abstract

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.