Hybridizing Euclidean and Hyperbolic Similarities for Attentively Refining Representations in Semantic Segmentation of Remote Sensing Images

Abstract

Attention mechanisms have revolutionized the semantic segmentation network in interpreting remotely sensed images (RSIs) due to their amazing ability in establishing contextual dependencies. Nevertheless, due to the complex scenes and diverse objects in RSIs, a variety of details and correlations are not available in Euclidean space. Therefore, a similarity-hybrid attention module (SHAM) is devised to attentively learn the hyperbolic and Euclidean attention maps between any two positions, followed by a weighted element-wise summation. The hybrid attention maps posses latent geometric properties of both Euclidean and hyperboloid. Taking commonly-used fully convolutional network (FCN) as baseline, HAENet that embeds SHAM, is presented. Experiments on ISPRS Potsdam and DeepGlobe benchmarks reveal its superiority to comparative methods. In addition, the ablation study validates the effectiveness of SHAM compared to other attention modules.