Non-Uniform Random Feature Selection and Kernel Density Scoring With SVM Based Ensemble Classification for Hyperspectral Image Analysis

Traditional statistical classification approaches often fail to yield adequate results with Hyperspectral imagery (HSI) because of the high dimensional nature of the data, multimodal class distribution and limited ground truth samples for training. Over the last decade, Support Vector Machines (SVMs) and Multi-Classifier Systems (MCS) have become popular tools for HSI analysis. Random Feature Selection (RFS) for MCS is a popular approach to produce higher classification accuracies. In this study, we present a Non-Uniform Random Feature Selection (NU-RFS) within a MCS framework using SVM as the base classifier. We propose a method to fuse the output of individual classifiers using scores derived from kernel density estimation. This study demonstrates the improvement in classification accuracies by comparing the proposed approach to conventional analysis algorithms and by assessing the sensitivity of the proposed approach to the number of training samples. These results are compared with that of uniform RFS and regular SVM classifiers. We demonstrate the superiority of Non-Uniform based RFS system with respect to overall accuracy, user accuracies, producer accuracies and sensitivity to number of training samples.