Weak Component Preservation in Deep Hyperspectral Unmixing Models
Keywords:
hyperspectral unmixing, deep learning, weak signal preservation, system architecture, attention mechanisms, state-space models, socio-technical infrastructure, fairness, Earth observationAbstract
Hyperspectral unmixing is a critical inverse problem in remote sensing and Earth observation, where mixed pixel spectra are decomposed into a set of pure spectral signatures and their corresponding fractional abundances. Recent advances in deep learning have significantly improved unmixing accuracy, yet a persistent blind spot remains: the systematic loss of weak spectral components that carry high-value information for environmental monitoring, mineral exploration, and agricultural assessment. This paper examines the problem of weak component preservation in deep hyperspectral unmixing models from a systems-level perspective. We argue that the architectural design of neural unmixing networks, particularly the competing objectives of reconstruction fidelity, sparsity enforcement, and representation compression, creates inherent trade-offs that marginalize low-amplitude or low-frequency endmembers. Through an interdisciplinary lens combining signal processing, machine learning infrastructure, and socio-technical governance, we analyze how architectural choices, training paradigms, and deployment constraints collectively influence the detectability of weak components. We further explore the implications of weak component loss for downstream decision-making, policy compliance, and equitable access to Earth observation data. A case illustration using state-space models and attention mechanisms demonstrates emerging strategies for preserving weak signals while maintaining overall unmixing performance. The paper concludes with a set of architectural and governance recommendations for designing hyperspectral unmixing systems that are both robust and inclusive of weak spectral information.
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