MTPANet: a multiscale transformer with position-aware attention for remote sensing image segmentation

Remote sensing images exhibit the characteristics of large intra-class variances and small inter-class variances, making remote sensing image segmentation a distinctive semantic segmentation task. Currently, the mainstream approach for remote sensing image segmentation is to combine CNN and transformer to enhance the network's feature extraction capability. However, simply concatenating CNN and transformer modules does not effectively leverage the information extraction advantages of each module. In this paper, we propose a U-shaped architecture that utilizes a multi-scale transformer with position-aware attention for remote sensing image segmentation (MTPANet). We specifically design a novel dual-branch self-attention module to extract features. The key feature of this module is the separate incorporation of position-aware attention and multi-scale components into both the CNN and transformer, followed by their fusion. This approach enhances the network's global attention capability and local perception ability, effectively addressing the inter-class and intra-class variability issues. Lastly, we validate the effectiveness and robustness of MTPANet on publicly available datasets, achieving precise segmentation of remote sensing images.