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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. For example, the identification of each ground surface pixel by its corresponding spectral signature is still difficult because of the immense volume of data. Conventional classification methods may not be used without dimension reduction preprocessing. This is due to the curse of dimensionality, which refers to the fact that the sample size needed to estimate a function of several variables to a given degree of accuracy grows exponentially with the number of variables. Principal component analysis (PCA) has been the technique of choice for dimension reduction. However, PCA is computationally expensive and does not eliminate anomalies that can be seen at one arbitrary band. Spectral data reduction using automatic wavelet decomposition could be useful. This is because it preserves the distinctions among spectral signatures. It is also computed in automatic fashion and can filter data anomalies. This is due to the intrinsic properties of wavelet transforms that preserves high- and low-frequency features, therefore preserving peaks and valleys found in typical spectra. Compared to PCA, for the same level of data reduction, we show that automatic wavelet reduction yields better or comparable classification accuracy for hyperspectral data, while achieving substantial computational savings.