Joint Endmember Representation and Abundance Mapping for Hyperspectral Image Analysis
Keywords:
Hyperspectral unmixing, endmember representation, abundance mapping, joint learning, socio-technical infrastructure, robustness, policy implicationsAbstract
Hyperspectral image analysis relies on the accurate decomposition of mixed pixels into constituent spectral endmembers and their corresponding fractional abundances. Traditional approaches treat endmember extraction and abundance estimation as sequential, often independent, tasks, which can lead to information loss and suboptimal reconstruction. This paper proposes a systemic perspective on joint endmember representation and abundance mapping, arguing that a unified learning framework can more effectively capture the intrinsic spectral–spatial structure of hyperspectral data. We examine architectural trade-offs between end-to-end neural architectures and hybrid physics-informed models, emphasizing the role of representation bottlenecks, reconstruction fidelity, and spatial regularization. Infrastructure considerations for deploying such models on satellite and airborne platforms are discussed, including computational constraints, energy efficiency, and real-time inference requirements. Robustness to noise, spectral variability, and missing bands is analyzed through the lens of adversarial training and attention mechanisms. Fairness and policy implications arise when abundance maps inform land-use classification, resource allocation, or environmental monitoring; we highlight risks of bias propagation and the need for transparent governance frameworks. The paper further explores sustainability challenges related to large-scale training data requirements and model carbon footprint. By integrating insights from signal processing, machine learning, and socio-technical systems, we advocate for joint representation frameworks that are not only accurate but also deployable, equitable, and resilient. A case study on weak-signal unmixing illustrates the importance of handling rare materials and fine-scale abundance reconstruction. The conclusion outlines future research directions toward standardized benchmarks, interpretable architectures, and participatory model validation.
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