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

The present work sought to instil metrology in existing hyperspectral texture feature extraction methods. Specifically, we propose distance-based expressions of graylevel cooccurrence matrix (GLCM), local binary pattern (LBP), and Gabor filtering directly computable for hyperspectral images without any pre- or post-processing. At the core of our proposition is Radical of Extended Mean Information for Discrimination (REID), a novel spectral distance with information-theoretic roots. Respecting the physics of spectrum as continuous function of wavelengths, REID is mathematically decomposable into spectral direction and spectral magnitude distances. The resulted feature calculations are fullband (utilizing all wavelengths), yet lightweight and fully interpretable. A similarity measure based on information theory is also justified. Their efficiency is demonstrated in the context of texture classification, content-based image retrieval, and cancer detection in which they consistently outperform existing computations based on dimensionally reduced space using PCA, ICA, and NMF. The propositions could be potentially integrated into machine/deep learning systems towards explainable AI (XAI).