Relative Spectral Difference Occurrence Matrix: A Metrologi- cal Spectral-Spatial Feature for Hyperspectral Texture Analysis

摘要

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 metro-logical 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.