Publication Abstract
Non-Uniform Random Feature Selection and Kernel Density Scoring with SVM Based Ensemble Classification for Hyperspectral Image Analysis
Samiappan, S., Prasad, S., & Bruce, L.M. (2013). Non-Uniform Random Feature Selection and Kernel Density Scoring with SVM Based Ensemble Classification for Hyperspectral Image Analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.. 6(02), 792-800.
Abstract
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 VectorMachines (SVMs) andMulti-Classifier
Systems (MCS) have become popular tools for HSI analysis.
Random Feature Selection (RFS) forMCS 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 SVMas 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.