Wavelet-based dimension reduction for hyperspectral image classification

For dimension reduction of hyperspectral imagery, we propose a modification to Principal Component Analysis (PCA), Karhunen-Loeve Transform, by choosing a set of basis vectors corresponding to the proposed transformation to be not only orthonormal but also wavelets. Although the eigenvectors of the covariance matrix of PCA minimize the mean square error over all other choices of orthonormal basis vectors, we will show that the proposed set of wavelet basis vectors have several desirable properties. After reducing the dimensionality of the data, we perform a supervised classification of the original and reduced data sets, compare the results, and assess the merits of such transformation.

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