Decision fusion for hyperspectral image classification based on minimum-distance classifiers in thewavelet domain

A decision-fusion approach is introduced for hyperspectral data classification based on minimum-distance classifiers in the wavelet domain. In the proposed approach, multi-scale features of each hyperspectral pixel are extracted by implementing a redundant discrete wavelet transformation on the spectral signature. Following this, a pair of minimum distance classifiers-a local mean-based nonparametric classifirer and a nearest regularization subspace-are applied on wavelet coefficients at each scale. Classification results are finally merged in a multi-classifier decision-fusion system. Experimental results using real hyperspectral data demonstrate the benefits of the proposed approach-in addition to improved classification performance compared to a traditional single classifier, the resulting classifier framework is effective even for low signal-to-noise-ratio images.