Advanced Hyperspectral Scene Interpretation Through PCA-Enhanced Residual Learning in Complex Spectral Domains

Hyperspectral Imaging (HSI) is an effective remote sensing method that records extensive spectral data across hundreds of contiguous bands and enables detailed analysis of land cover and materials. The large dimensions, spectral redundancy, and lack of labeled data, however, are important limitation...

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Автори:	Ahmad Almadhor, Abdullah Al Hejaili, Abdullah Alqahtani, Nejib Ghazouani, Belgacem Bouallegue, Vincent Karovic, Sidra Abbas
Формат:	Artigo
Мова:	Inglês
Опубліковано:	IEEE 2026-01-01
Серія:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Предмети:	Hyperspectral image classification (HSIC) model interpretability Local Interpretable Model-Agnostic Explanation (LIME) principal component analysis (PCA) residual convolutional neural networks (ResNet) deep learning
Онлайн доступ:	https://ieeexplore.ieee.org/document/11373109/
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Advanced Hyperspectral Scene Interpretation Through PCA-Enhanced Residual Learning in Complex Spectral Domains

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