Low Complexity Improvement for Hyperspectral Asymmetrical Data Compression

Spatial and spectral decor relations are necessary for hyper spectral data compression. The two dimensional wavelet transform based spatial transform and the Karhunen-Loève transform (KLT) based spectral transform have been employed successfully for hyper spectral data compression. In this paper a hyper spectral asymmetrical data compression is proposed as an improvement of the low complexity version of the Karhunen-Loève transform following the energy distribution in the wavelet transform domain. In the improved low complexity KLT, the computation processing of the covariance matrix is carried out on a spectral data which is extracted from the region of high energy distribution. The new method highlights the physical difference between the spatial and spectral characteristics of hyper spectral data. Experimental results show that the new method has improved significantly, not only the computation time but also has a good performance for the compressed data.