Description

Abstract:

A fusion-classification system based upon a wavelet-coefficient correlation matrix (WCM) used as a feature grouping is proposed. 3-D DWT coefficients   are extracted as spectral-spatial features for classification. Afterwards, classification (LDA-MLE, LFDA-GMM, and SVM-radial basis function (RBF)) along with decision fusion in the form of majority voting (MV) is then invoked to combine these results into a single classification decision per pixel. A windowed 3-D discrete wavelet transform is first combined with a feature grouping−a wavelet-coefficient correlation matrix (WCM)−to extract and select spectral-spatial features from the hyperspectral image dataset. The adjacent wavelet-coefficient subspaces (from the WCM) are intelligently grouped such that correlated coefficients are assigned to the same group. Afterwards, a multiclassifier decision-fusion approach is employed for the final classification. The performance of the proposed classification system is assessed with various classifiers, including maximum-likelihood estimation, Gaussian mixture models, and support vector machines. Experimental results show that with the proposed fusion system, independent of the classifier adopted, the proposed classification system substantially outperforms the popular single-classifier classification paradigm under small-sample-size conditions and noisy environments.