Semantic Segmentation of High-Resolution Remote Sensing Images Based on Sparse Self-Attention and Feature Alignment

Abstract

Semantic segmentation of high-resolution remote sensing images (HRSI) is significant, yet challenging. Recently, several research works have utilized the self-attention operation to capture global dependencies. HRSI have complex scenes and rich details, and the implementation of self-attention on a whole image will introduce redundant information and interfere with semantic segmentation. The detail recovery of HRSI is another challenging aspect of semantic segmentation. Several networks use up-sampling, skip-connections, parallel structure, and enhanced edge features to obtain more precise results. However, the above methods ignore the misalignment of features with different resolutions, which affects the accuracy of the segmentation results. To resolve these problems, this paper proposes a semantic segmentation network based on sparse self-attention and feature alignment (SAANet). Specifically, the sparse position self-attention module (SPAM) divides, rearranges, and resorts the feature maps in the position dimension and performs position attention operations (PAM) in rearranged and restored sub-regions, respectively. Meanwhile, the proposed sparse channel self-attention module (SCAM) groups, rearranges, and resorts the feature maps in the channel dimension and performs channel attention operations (CAM) in the rearranged and restored sub-channels, respectively. SPAM and SCAM effectively model long-range context information and interdependencies between channels, while reducing the introduction of redundant information. Finally, the feature alignment module (FAM) utilizes convolutions to obtain a learnable offset map and aligns feature maps with different resolutions, helping to recover details and refine feature representations. Extensive experiments conducted on the ISPRS Vaihingen, Potsdam, and LoveDA datasets demonstrate that the proposed method precedes general semantic segmentation- and self-attention-based networks.