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).

Texture characterization from the metrological point of view is addressed in order to establish a physically relevant and directly interpretable feature. In this regard, a generic formulation is proposed to simultaneously capture the spectral and spatial complexity in hyperspectral images. The feature, named relative spectral difference occurrence matrix (RSDOM) is thus constructed in a multireference, multidirectional, and multiscale context. As validation, its performance is assessed in three versatile tasks. In texture classification on HyTexiLa, content-based image retrieval (CBIR) on ICONES-HSI, and land cover classification on Salinas, RSDOM registers 98.5% accuracy, 80.3% precision (for the top 10 retrieved images), and 96.0% accuracy (after post-processing) respectively, outcompeting GLCM, Gabor filter, LBP, SVM, CCF, CNN, and GCN. Analysis shows the advantage of RSDOM in terms of feature size (a mere 126, 30, and 20 scalars using GMM in order of the three tasks) as well as metrological validity in texture representation regardless of the spectral range, resolution, and number of bands.

A new hyperspectral texture descriptor, Relocated Spectral Difference Occurrence Matrix (rSDOM) is proposed. It assesses the distribution of spectral difference in a given neighborhood. For metrological purposes, rSDOM employs Kullback-Leibler pseudo-divergence (KLPD) for spectral difference calculation. It is generic and adapted for any spectral range and number of band. As validation, a texture classification scheme based on nearest neighbor classifier is applied on HyTexiLa dataset using rSDOM. The performance is close to Opponent Band Local Binary Pattern (OBLBP) with classification accuracy of 94.7%, but at a much-reduced feature size (0.24% of OBLBP's) and computational complexity.