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This is a Quantitative Study study.

Hyperspectral Texture Metrology Based on Distance Measures in an Information-Theoretic Framework

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Distance-based metrics enhance texture classification in hyperspectral imaging, suggesting new AI integration.

Distance measures enhance texture feature extraction for hyperspectral imaging, improving classification accuracy.
The study introduces Radical of Extended Mean Information for Discrimination, outperforming traditional PCA and ICA methods.
Feature calculations are efficient and interpretable, optimizing both spectral direction and magnitude distances.
This approach could inform machine learning systems, paving the way for enhanced explainable AI applications.

Chu et al. (2025) studied this question.

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