A Metrological Framework For Hyperspectral Texture Analysis Using Relative Spectral Difference Occurrence Matrix

Rui Jian Chu , Noël Richard (1) , Faouzi Ghorbel (2) , Christine Fernandez-Maloigne (3) , Jon Yngve Hardeberg (4)

1 UP - Université de Poitiers = University of Poitiers
2 GRIFT - GRIFT - ENSI - TUNISIE
3 XLIM-ASALI - Synthèse et analyse d'images
4 The Norwegian Colour and Visual Computing Laboratory

A new hyperspectral texture descriptor, Relative Spectral Difference Occurrence Matrix (RSDOM) is proposed. Developed in a metrological framework, it simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. It is generic and adapted for any number of spectral band or range. As validation, a texture classification scheme is applied on HyTexiLa dataset using RSDOM. The obtained accuracy is excellent (95.6%), comparable to Opponent Band Local Binary Pattern (OBLBP) but at a much-reduced feature size (0.1% of OBLBP's).

Mots clés: Kullback-Leibler divergence; texture; non-uniformity; metrology; Kullback-Leibler; Index Terms-Texture

Domaines: Informatique [cs]; Vision par ordinateur et reconnaissance de formes [cs.CV]

Rui Jian Chu, Noël Richard, Faouzi Ghorbel, Christine Fernandez-Maloigne, Jon Yngve Hardeberg. A Metrological Framework For Hyperspectral Texture Analysis Using Relative Spectral Difference Occurrence Matrix. 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Sep 2019, Amsterdam, Netherlands. pp.1-5, ⟨10.1109/WHISPERS.2019.8921335⟩. ⟨hal-02481582⟩