Deep Spectral Representation Learning for Hyperspectral Unmixing and Abundance Estimation
Keywords:
hyperspectral unmixing, abundance estimation, deep representation learning, spectral variability, autoencoders, state-space models, system architecture, infrastructure, robustness, fairness, sustainabilityAbstract
Hyperspectral imaging captures hundreds of narrow spectral bands per pixel, enabling detailed material characterization across environmental monitoring, agriculture, defense, and mineral exploration. However, the spatial resolution of such sensors is often limited, leading to mixed pixels that contain spectral signatures from multiple materials. Hyperspectral unmixing and abundance estimation aim to decompose each mixed pixel into a set of endmember spectra and their corresponding fractional abundances. Traditional approaches rely on linear mixing models, geometric methods, or sparse regression, yet they struggle with spectral variability, noise, and the nonlinear interactions present in real scenes. Deep spectral representation learning has emerged as a powerful paradigm that leverages neural networks to learn robust, nonlinear feature embeddings directly from the data, often yielding superior unmixing accuracy and generalization. This paper presents a comprehensive systems-level analysis of deep spectral representation learning for hyperspectral unmixing and abundance estimation. We examine architectural innovations such as autoencoders, convolutional networks, transformers, and state-space models, emphasizing structural trade-offs between model capacity, interpretability, and computational efficiency. We discuss infrastructure requirements for training and deploying these models on large-scale hyperspectral datasets, including data governance, standardization, and computational resource allocation. Robustness to sensor noise, atmospheric effects, and spectral variability is analyzed from a fairness and policy perspective, as unmixing results can directly impact land-use decisions, resource allocation, and environmental justice. Sustainability considerations, including energy consumption of deep learning pipelines and the life cycle of hyperspectral missions, are addressed. The paper further explores governance frameworks for ensuring transparency and reproducibility in abundance estimation, particularly in high-stakes applications such as disaster response and mineral rights disputes. By integrating technical, operational, and societal dimensions, this work provides a roadmap for the responsible deployment of deep spectral unmixing systems in real-world socio-technical infrastructures.
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