A Euclidean Affinity-Augmented Hyperbolic Neural Network for Semantic Segmentation of Remote Sensing Images

Li X, Xu F, Liu F, Lyu X, Gao H, Zhou J, Kaup A (2025)

Publication Type: Journal article

Publication year: 2025

Journal

IEEE Transactions on Geoscience and Remote Sensing Institute of Electrical and Electronics Engineers (IEEE)

DOI: 10.1109/TGRS.2025.3594760

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.

Authors with CRIS profile

Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung (LMS)

Involved external institutions

Hohai University CN China (CN) Griffith University AU Australia (AU)

How to cite

APA:

Li, X., Xu, F., Liu, F., Lyu, X., Gao, H., Zhou, J., & Kaup, A. (2025). A Euclidean Affinity-Augmented Hyperbolic Neural Network for Semantic Segmentation of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2025.3594760

MLA:

Li, Xin, et al. "A Euclidean Affinity-Augmented Hyperbolic Neural Network for Semantic Segmentation of Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing (2025).

BibTeX: Download