Benefits of textural characterization for the classification of hyperspectral images

Several spatial features are compared for the spatial/spectral classification of hyperspectral data. These features are extracted from texture spectra, co-occurrence matrices and morphological profiles. First, a PCA (Principal Components Analysis) is carried out on the hyperspectral image and textural features are computed on the first principal components. These textural features are concatenated together with spectral features (the principal components previously used) and the resulting image vector is then classified using SVM (Support Vector Machines) and a gaussian mixture algorithm. In the latter case, a hierarchical classification is used as a post-processing in order to reach a desired number of classes.