We develop a spectral-spatial feature, Relative Spectral Difference Occurrence Matrix (RSDOM) for hyperspectral texture recognition. Inspired by Julesz's conjecture, the proposed feature is based on spectral difference approach and respects the metrological constraints. It simultaneously considers the distribution of spectra and their spatial arrangement in the hyperspectral image. The feature is generic and adapted for any number of spectral bands and range. We validate our proposition by applying a classification scheme on the HyTexiLa database. An accuracy comparable to local binary pattern approach is obtained, but at a much reduced cost due to the extremely small feature size.
Rui Jian Chu, Noël Richard, Christine Fernandez-Maloigne, Jon Yngve Hardeberg, "Relative Spectral Difference Occurrence Matrix: A Metrological Spectral-Spatial Feature for Hyperspectral Texture Analysis" in Proc. IS&T 27th Color and Imaging Conf., 2019, pp 386 - 392, https://doi.org/10.2352/issn.2169-2629.2019.27.69