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

2025-05-16

AI Summary

Hyperspectral image (HSI) representation suffers from a tripartite non-uniformity conflict among spectral dependency, spatial continuity, and feature efficiency; conventional homogeneous modeling paradigms thus yield suboptimal performance and representation bias. To address this, we propose FairHyp—a fairness-oriented framework that formally introduces *structural fairness* as an intrinsic requirement for HSI modeling, enabling dimension-specific learning while preserving global consistency. Our key innovations include: (i) a Runge–Kutta–based spatial adapter for adaptive spatial modeling, (ii) sparse-aware multi-receptive-field convolution for efficient spatial-spectral trade-offs, and (iii) a bidirectional Mamba-based spectral state-space model for long-range spectral dependency capture. Through end-to-end joint optimization, FairHyp achieves new state-of-the-art performance across four fundamental tasks—classification, denoising, super-resolution, and inpainting. Moreover, it demonstrates strong cross-imaging-condition robustness, empirically validating non-uniformity as a universal structural deficiency inherent to HSI data.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Feature Construction/Reformulation
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

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.

Problem

• Addressing hyperspectral image non-uniformity in spectral, spatial, and feature dimensions
• Overcoming biased representations from unified processing paradigms in HSI models
• Resolving conflicting behaviors in spectral dependencies and spatial continuity

Innovation

• Runge-Kutta-inspired spatial variability adapter
• Multi-receptive field convolution with sparse-aware refinement
• Bidirectional Mamba scanning for spectral dependencies