Application of Feature Selection and Classifier Ensembles for the Classification of Hyperspectral Data

The improved spectral resolution of modern hyperspectral sensors provides capabilities for discrimination of subtly different classes and objects. However, in order to obtain statistically reliable classification results, the number of required training samples increases exponentially as the number of spectral bands increases. However, in many situations, acquisition of the large number of training samples for the high-dimensional datasets may not be feasible. Multiple classifiers have been regarded as a promising solution for this problem. In this paper, creation of ensemble of classifiers based on feature selection has been evaluated and an effective strategy for generation of feature subsets has been proposed. The proposed method is based on generating multiple feature subsets by running feature selection algorithm several times, with the aim of discrimination of one class from the others each time. Each of the final subsets of features is selected so as to have the capability for discrimination of one of the classes. Each of these subsets is then passed to the maximum likelihood classifier. Finally a combination scheme is used to combine the outputs of individual classifiers. Practical examinations on the AVIRIS data for discrimination of different land cover classes demonstrate the effectiveness of the proposed strategy.