A Comparative Study of Deep Learning Methods for Hyperspectral Unmixing

Abstract

Hyperspectral imaging captures hundreds of contiguous spectral bands per pixel, enabling detailed material identification in remote sensing, mineralogy, agriculture, and environmental monitoring. However, the limited spatial resolution of sensors results in mixed pixels where multiple materials contribute to a single spectrum. Hyperspectral unmixing, the process of decomposing mixed pixels into constituent endmembers and their fractional abundances, is a fundamental inverse problem that has been addressed through numerous computational approaches. In recent years, deep learning methods have emerged as powerful alternatives to classical geometric and statistical techniques, offering nonlinear unmixing capabilities and data-driven feature extraction. This paper presents a comparative study of deep learning architectures for hyperspectral unmixing, focusing on system-level trade-offs rather than purely algorithmic performance metrics. We examine autoencoder-based frameworks, convolutional neural networks, recurrent models, and emerging state-space representations, analyzing their structural assumptions, training data requirements, computational costs, and generalization capacity. Particular attention is given to deployment infrastructure, scalability to large hyperspectral datasets, robustness to noise and spectral variability, and the implications for fairness and governance in operational settings. Through a critical synthesis of recent advances, including weak-signal representation learning frameworks, we highlight the tension between model complexity and interpretability, the challenges of training data scarcity, and the need for reproducible benchmarks. The study concludes with recommendations for designing robust, efficient, and ethically deployable deep unmixing systems that align with real-world infrastructure constraints.