Wavelet-Based Principal Components Analysis Feature Extraction Method for Hyperspectral Images

A new wavelet-based principal components analysis(PCA) feature extraction method is proposed for hyperspectral dimensionality reduction.Wavelet decomposition can reduce hyperspectral data in the spectral domain for each pixel.This may not only reduce the data volume,but also preserve the distinction among spectral signatures that is useful for most pixel-based classifiers.PCA can provide more local spatial information among neighborhood class pixels than wavelet decomposition.Experimental results show that the hybrid method can prove the effectiveness and accuracy in classifying the hyperspectral data.