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Abstract
With the number of channels in the hundreds instead of in the tens hyperspectral imagery possesses much richer spectral information than multispectral imagery .The increased dimensionality of such hyperspectral data provides a challenge to the current technique for analyzing data.

Conventional classification methods my not be used without dimension reduction preprocessing. Supervised classification techniques use labelled samples in order to train the classifier. usually the number of such samples is limited and as the number of bands available increases this limitation becomes sever and can become dominate over the projected added value of having the additional bands available.

So because of these problems dimension reduction has become a significant part of hyperspectral image interpretation.

Some techniques are used for dimension reduction such as principle component analysis and minimum noise fraction but these techniques are not useful for dimension reduction for example PCA is computationally expensive and dose not eliminate anomalies that can be seen at one arbitrary band. And MNF has the similar problems.

Spectral data reduction using automatic wavelet decomposition could be useful because it preserves the distinction among spectral signatures. It is also computed in automatic fashion and can filter data anomalies this is due to the intrinsic properties of wavelet transform that preserve high and low frequency feature therefore preserving peaks and valleys found in typical spectra. Compared to PCA and MNF, for the same level of data reduction we show that automatic wavelet reduction has more separate classes and yields better or comparable classification accuracy, while achieving substantial computational saving.