Dynamic Linear Classifier System for Hyperspectral Image Classification for Land Cover Mapping

Abstract

Exploitation of the spectral capabilities of modern hyperspectral image demands efficient preprocessing and analyses methods. Analysts' choice of classifier and dimensionality reduction (DR) method and the harmony between them determine the accuracy of image classification. Multiple classifier system (MCS) has the potential to combine the relative advantages of several classifiers into a single image classification exercise for the hyperspectral image classification. In this paper, we propose an algorithmic extension of the MCS, named as dynamic classifier system (DCS), which exploits the context-based image and information class characteristics represented by multiple DR methods for hyperspectral image classification for land cover mapping. The proposed DCS algorithm pairs up optimal combinations of classifiers and DR methods specific to the hyperspectral image and performs image classifications based only on the identified combinations. Further, the impact of various trainable and nontrainable combination functions on the performance of the proposed DCS has been assessed. Image classifications were carried out on five multi-site airborne hyperspectral images using the proposed DCS and were compared with the MCS and SVM based supervised image classifications with and without DR. The results indicate the potential of the proposed DCS algorithm to increase the classification accuracy considerably over that of MCS or SVM supervised image classifications.