Adaptive Reconstruction-Based Learning for Hyperspectral Unmixing
Keywords:
hyperspectral unmixing, adaptive reconstruction, deep learning, attention fusion, state-space models, scalability, robustness, fairnessAbstract
Hyperspectral unmixing is a critical inverse problem in remote sensing, tasked with decomposing mixed pixel spectra into pure material signatures and their corresponding abundance fractions. Traditional unmixing algorithms, while effective under controlled conditions, often falter when confronted with spectral variability, nonlinear mixing effects, noise, and limited training data. Recent advances in deep learning have introduced reconstruction-based frameworks that learn robust representations directly from data, yet many such models lack adaptability to changing acquisition conditions or sensor characteristics. This paper presents a comprehensive analysis of adaptive reconstruction-based learning for hyperspectral unmixing, focusing on system-level architectural choices, trade-offs between model complexity and generalizability, and the integration of attention mechanisms and state-space models. We examine how adaptive reconstruction strategies, including variational autoencoders, gated abundance reconstruction, and weak-signal attention fusion, can enhance the fidelity and interpretability of unmixing results. The discussion extends beyond algorithmic performance to address structural considerations such as scalability for large-scale deployments, computational sustainability, robustness to noise and endmember variability, and fairness implications in resource allocation and environmental monitoring. Through cross-domain case illustrations spanning agriculture, mineral exploration, and urban planning, we highlight the practical impact of adaptive reconstruction methods. The paper concludes with forward-looking perspectives on integrating physics-informed priors, foundation models, and uncertainty quantification to support reliable and equitable hyperspectral analysis in real-world socio-technical systems.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
