A Gaussian approach to subspace based classification of hyperspectral images

Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods deal with this difficulty by assuming that feature vectors lie in a low-dimensional subspace. Based on the fact that a class in a hyperspectral image may be composed of a number of different groups of materials and mixture of spectral features, we suggest to estimate several lower dimensional random subspaces for the samples within each class. For subspace learning and classification, we propose to exploit the union of random subspaces in a Gaussian Mixture Model. Experimental results, conducted on two real hyperspectral data sets, indicate that the proposed method provides competitive classification results in comparison with other state-of-the-art approaches.