Ground cover classification using remotely sensed hyperspectral data is a challenging pattern recognition problem. The small (and expensive to collect) training sample sizes exacerbate the curse-of-dimensionality problem that already exists with such high dimensional feature spaces. However, Support Vector Machine (SVM) classifiers have been demonstrated to be better at handling such situations compared to other statistical classifiers. Recently, multi-classifier systems and a uniform random feature selection have proved to be very effective for hyperspectral image classification. In this paper, a support vector machines based multi-classifier system with non-uniform (spectrally-constrained) random feature selection is presented. We propose two approaches to perform such a non-uniform random-feature selection. Experimental results with the AVIRIS Indian Pines hyperspectral data demonstrate that the proposed approach outperforms regular random feature selection based on a uniform distribution.
['Sathishkumar Samiappan', 'Saurabh Prasad', 'Lori Mann Bruce']
Automated hyperspectral imagery analysis via support vector machines based multi-classifier system with non-uniform random feature selection