Advances in hyper spectral imaging sensors makes it possible to obtain high dimensional and high resolution data which poses challenges for computational cost and time. As the inter-band resolution is high in hyper spectral images, the spectral dimension could be reduced without significant loss of useful information. This requires careful application of dimensionality reduction techniques to reduce the dimension but preserve the original spectral characteristics. It is observed in the literature that filtering techniques can address some of the challenges concerning dimensionality reduction in hyper spectral imaging. In this paper a dimensionality reduction method adopting Discrete Wavelet Transform and Distance Classifier Correlation filter has been proposed for hyper spectral imaging. The experimental results show that the combination of ID-DWT with DCCF outperforms the other methods in dimensionality reduction and classification of given data set.