A multiclassifier and decision fusion system for hyperspectral image classification

In this paper, a windowed three-dimensional discrete wavelet transform (3DDWT) is employed to extract spectral-spatial features for hyperspectral image classification; these features quantify local orientation and scale characteristics. Since single subband (i.e., the "LLL" subband) is deficient in exploiting useful information, all eight subbands (LLL, LLH, LHL, LHH, HLL, HLH, HHL, HHH) from a single-level, dyadic 3D DWT are first fused to overcome the small-sample-size problem. The studies reported in this paper are conducted within the context of multi-classifiers and decision fusion systems that are designed to handle the high-dimensional 3D DWT feature spaces. Two decision fusion rules-majority voting (MV) and logarithmic opinion pool (LOGP) are employed and studied for the final classification of hyperspectral dataset. Experimental results show that the proposed fusion algorithms substantially outperform traditional single-classifier methods (LDA-MLE, LFDA-GMM, and SVM-RBF) and a single classifier algorithm based on the windowed 3D DWT structure (3D DWT-LFDA-GMM).