DOCNet: Dual-Domain Optimized Class-Aware Network for Remote Sensing Image Segmentation

The spatial attention mechanism has been frequently employed for the semantic segmentation of remote sensing images, given its renowned capability to model long-range dependencies. As remote sensing images often exhibit intricate backgrounds, significant intraclass variability, and a foreground-background imbalance, spatial attention mechanism-based methods somehow tend to introduce an extensive amount of background context through intensive affinity operations, causing unsatisfactory segmentation outcomes. While several class-aware methods attempt to attenuate the interference of background context by generating class representations as representative features, they still encounter challenges related to independent correlation calculation and single-confidence scale class representations. We introduce a dual-domain optimized class-aware network designed to address these challenges. In the semantic domain, we use category confidence as a scaling criterion to derive class representations at multiple confidence scales, effectively modeling pixel-class relationships. In the spatial domain, we leverage pixel-class relationships and their consensus to enhance relevant correlations while suppressing erroneous ones. Experimental results on three datasets demonstrate that the proposed method surpasses previous state-of-the-art ones for remote sensing image segmentation. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/xwmaxwma/rssegmentation</uri> .