Decision Fusion in Kernel-Induced Spaces for Hyperspectral Image Classification

Abstract

The one-against-one (OAO) strategy is commonly employed with classifiers-such as support vector machines-which inherently provide binary two-class classification in order to handle multiple classes. This OAO strategy is introduced for the classification of hyperspectral imagery using discriminant analysis within kernel-induced feature spaces, producing a pair of algorithms-kernel discriminant analysis and kernel local Fisher discriminant analysis-for dimensionality reduction, which are followed by a quadratic Gaussian maximum-likelihood-estimation classifier. In the proposed approach, a multiclass problem is broken down into all possible binary classifiers, and various decision-fusion rules are considered for merging results from this classifier ensemble. Experimental results using several hyperspectral data sets demonstrate the benefits of the proposed approach-in addition to improved classification performance, the resulting classifier framework requires reduced memory for estimating kernel matrices.