UNSUPERVISED CLASSIFIER SELECTION APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Generating accurate and robust classification maps from hyperspectral imagery (HSI) depends on the choice of the classifiers and input data sources. Choosing the appropriate classifier for a problem at hand is a tedious task. Multiple classifier system (MCS) combines the relative merits of various classifiers to generate robust classification maps. However, the presence of inaccurate classifiers may degrade the classification performance of MCS. In this paper, we propose an unsupervised classifier selection strategy to select an appropriate subset of accurate classifiers for the multiple classifier combination from a large pool of classifiers. The experimental results with two HSI show that the proposed classifier selection method overcomes the impact of inaccurate classifiers and significantly increases the classification accuracy.