Decision level fusion in classification of hyperspectral data from urban areas

Abstract

Classification of hyperspectral data with high spatial resolution using both spatial and spectral approaches is discussed. The spatial approach is based on mathematical morphology. In the method, several principal components (PCs) from the hyperspectral data are used. From each of the PCs, a morphological profile is built. These profiles are used together in one extended morphological profile, which is then classified with a neural network. The spectral classification approach is based on maximum likelihood classification and nonparametric weighted feature extraction (NWFE). The results from the spectral and spatial modeling are finally fused together using several different fusion rules. Experimental results are given on a hyperspectral data from an urban area.