Threshold Attention Network for Semantic Segmentation of Remote Sensing Images
To address the high computational complexity and redundancy inherent in self-attention mechanisms for remote sensing image semantic segmentation, this paper proposes a Threshold Attention Mechanism (TAM) and a lightweight, efficient network termed TANet. TANet comprises two key components: an Attention Feature Enhancement Module (AFEM) for shallow-layer global feature enhancement, and a Threshold Attention Pyramid Pooling module (TAPP) for deep-layer multi-scale contextual modeling. TAM dynamically filters salient attention regions via adaptive thresholding, preserving long-range dependency modeling while substantially reducing computational overhead. Evaluated on the ISPRS Vaihingen and Potsdam benchmarks, TANet achieves competitive accuracy against state-of-the-art methods, with ~22% faster inference speed and 40% lower GPU memory consumption—demonstrating a favorable balance among accuracy, efficiency, and deployment practicality.