A Euclidean Affinity-Augmented Hyperbolic Neural Network for Semantic Segmentation of Remote Sensing Images
Xin Li, Feng Xu, Fan Liu, Xin Lyu, Hongmin Gao, Jun Zhou, André Kaup
Abstract
Semantic segmentation of remote sensing images (RSIs) plays a pivotal role in advancing geospatial analyses and applications across diverse fields, such as urban planning and environmental monitoring. Traditional learning paradigms predominantly utilize Euclidean spaces for feature extraction. This approach can introduce spatial distortions when representing objects, as Euclidean architectures typically focus on locality and are optimized for grid data, not always yielding optimal geometrical representations for data structured in non-Euclidean spaces. To address these problems, we propose EAAHNet, the first fully hyperbolic neural network designed for semantic segmentation of RSIs. EAAHNet employs the Lorentz model to reformalize conventional Euclidean-based neural network operations, ensuring the preservation of hyperbolic properties. Furthermore, to account for the inherently Euclidean nature of ground objects, we propose a Euclidean affinity-augmented hyperbolic attention module (EAAHAM) that enriches contextual dependencies through an attention fusion manner. This enhancement significantly improves the network’s capacity to discern pixel-wise semantics. Extensive experiments conducted on the ISPRS Vaihingen, ISPRS Potsdam, and LoveDA datasets demonstrate EAAHNet’s superior performance over several state-of-the-art methods. Additionally, the ablation study verifies the impacts of EAAHAM.