Compression of hyperspectral data using vector quantisation and the discrete cosine transform

Mean-normalised vector quantization (M-NVQ) has been demonstrated to be the preferred vector quantization technique for application to the lossless compression of hyperspectral data. This work optimises M-NVQ parameters for application to lossy compression and slight improvement is shown to be gained by the implementation of spatial and spectral discrete cosine transform (DCT) techniques for coding of the M-NVQ residuals. Much more efficient compression is shown to be obtained by optimising the M-NVQ and DCT techniques simultaneously, rather than sequentially. Optimised spatial M-NVQ/spectral DCT is shown to produce compression ratios of between 1.5 and 2.5 times better than those obtained by the spatial M-NVQ technique alone. Compression ratios of up to 43:1 are achieved without significant loss in classification accuracy.