On the characterization of hyperspectral texture

Many tools have been proposed in the literature for texture characterization of images. Some of them are based on statistical properties, others on fractal measures and some more on multiresolution analysis. Those methods have been proposed in a scalar point of view to be applied on mono-band images. They are not suited to the hyperspectral context where the spectral signature of each pixel has to be considered as a vector. Hyperspectral texture characterization is studied in this paper by extending the wavelet transform to suit hyperspectral images. A dimensionality reduction step is first applied on hyperspectral data with nonlinear transform, then a multiresolution analysis is performed on a limited number of spectral bands. The texture characterization by itself is based on the accurate modeling of the marginal distribution of the wavelet coefficients using Generalized Gaussian Density (GGD) and similarity measurement of GGDS by computing the Kullback-Leibler (KL) divergence. The results are presented with real hyperspectral images in a supervised and non-supervised texture segmentation.