Reduction and estimation of hyperspectral imagery using dual tree wavelet filter bank analysis with an orthogonal subspace projection approach

Abstract

Hyperspectral imagery provides richer information about materials than multispectral imagery. The new larger data volumes from hyperspectral sensors present a challenge for traditional processing techniques. Principal component analysis (PCA) has been the technique of choice for dimension reduction. Spectral data reduction and estimation using wavelet filter bank with perfect reconstruction can be considered for better results.