Endmember-Aware Feature Learning for Nonlinear Hyperspectral Unmixing

Abstract

Hyperspectral unmixing is a critical inverse problem in remote sensing that involves decomposing mixed pixels into a set of pure spectral signatures, known as endmembers, and their corresponding fractional abundances. Traditional unmixing approaches often assume a linear mixing model, but many real-world scenarios exhibit nonlinear interactions due to multiple scattering, intimate mixing, and complex surface geometries. This paper presents a comprehensive system-level analysis of endmember-aware feature learning frameworks designed to address the challenges of nonlinear hyperspectral unmixing. We argue that embedding endmember knowledge directly into the feature learning process enhances both interpretability and accuracy, while also introducing structural trade-offs that affect deployment, scalability, and governance. The discussion spans architectural design choices—such as the integration of spectral-spatial constraints, attention mechanisms, and state-space models—and examines how these choices influence robustness under varying illumination, atmospheric conditions, and sensor noise. Sustainability considerations are explored in terms of computational resource demands, energy efficiency, and model longevity across diverse observational platforms. Furthermore, we address fairness and policy implications, including the risk of spectral biases introduced by training data imbalances and the need for transparent unmixing pipelines in environmental monitoring and resource management. By synthesizing recent advances in deep learning with domain-specific constraints, we outline a forward-looking perspective on infrastructure governance and the responsible deployment of unmixing systems. The paper concludes with recommendations for future research directions that prioritize algorithmic transparency, cross-domain generalizability, and equitable access to high-quality Earth observation data.