Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity

• URL: http://arxiv.org/abs/2505.11267v1
• Date: Fri, 16 May 2025 14:00:11 GMT
• Title: Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity
• Authors: Wuzhou Quan, Mingqiang Wei, Jinhui Tang,
• Abstract summary: Hyperspectral image (HSI) representation is fundamentally challenged by pervasive non-uniformity.<n>We propose FairHyp, a fairness-directed framework that disentangles and resolves the threefold non-uniformity.<n>Our findings redefine fairness as a structural necessity in HSI modeling and offer a new paradigm for balancing adaptability, efficiency, and fidelity.
• Score: 42.8098014428052
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hyperspectral image (HSI) representation is fundamentally challenged by pervasive non-uniformity, where spectral dependencies, spatial continuity, and feature efficiency exhibit complex and often conflicting behaviors. Most existing models rely on a unified processing paradigm that assumes homogeneity across dimensions, leading to suboptimal performance and biased representations. To address this, we propose FairHyp, a fairness-directed framework that explicitly disentangles and resolves the threefold non-uniformity through cooperative yet specialized modules. We introduce a Runge-Kutta-inspired spatial variability adapter to restore spatial coherence under resolution discrepancies, a multi-receptive field convolution module with sparse-aware refinement to enhance discriminative features while respecting inherent sparsity, and a spectral-context state space model that captures stable and long-range spectral dependencies via bidirectional Mamba scanning and statistical aggregation. Unlike one-size-fits-all solutions, FairHyp achieves dimension-specific adaptation while preserving global consistency and mutual reinforcement. This design is grounded in the view that non-uniformity arises from the intrinsic structure of HSI representations, rather than any particular task setting. To validate this, we apply FairHyp across four representative tasks including classification, denoising, super-resolution, and inpaintin, demonstrating its effectiveness in modeling a shared structural flaw. Extensive experiments show that FairHyp consistently outperforms state-of-the-art methods under varied imaging conditions. Our findings redefine fairness as a structural necessity in HSI modeling and offer a new paradigm for balancing adaptability, efficiency, and fidelity in high-dimensional vision tasks.

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