A Metrological Framework for Hyperspectral Texture Analysis Using Relative Spectral Difference Occurrence Matrix
Peer reviewed, Journal article
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Original versionWorkshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing. 2019, 2019-September . 10.1109/WHISPERS.2019.8921335
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).