Robust Abundance Estimation in Hyperspectral Images Using Spectral-Spatial Deep Learning
Keywords:
hyperspectral imaging, abundance estimation, spectral unmixing, deep learning, spectral-spatial features, robustness, remote sensing, model deployment, system governanceAbstract
Hyperspectral imaging captures detailed spectral signatures across hundreds of contiguous bands, enabling precise material identification and abundance estimation through spectral unmixing. Traditional unmixing methods often rely on linear mixing models and treat each pixel independently, neglecting the rich spatial context inherent in natural scenes. Recent advances in deep learning have introduced spectral-spatial architectures that simultaneously exploit spectral and spatial information, yielding significant improvements in abundance estimation accuracy. However, the deployment of these models in operational remote sensing systems introduces complex challenges related to robustness, computational efficiency, scalability, and fairness. This paper presents a systems-level examination of spectral-spatial deep learning frameworks for robust abundance estimation. We analyze structural trade-offs between convolutional networks, transformers, and hybrid attention mechanisms, focusing on their capacity to handle noise, illumination variability, atmospheric interference, and spectral variability across large-scale hyperspectral datasets. The discussion extends to infrastructure considerations, including real-time onboard processing, energy consumption, and model compression for satellite-borne platforms. Governance and policy implications are addressed, particularly concerning data biases in environmental monitoring and defense applications, as well as the ethical use of unmixing results in resource allocation and surveillance. Cross-domain comparisons with medical imaging and industrial quality control illustrate transferable lessons for robustness and sustainability. By integrating architectural analysis with deployment-centric perspectives, this paper provides a comprehensive roadmap for future research and practical implementation of robust spectral-spatial deep learning systems for hyperspectral abundance estimation.
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