Lossy hyperspectral image compression based on intra-band prediction and inter-band fractal encoding

Recently, hyperspectral image compression has become an urgent issue for remote sensing applications. A lossy hyperspectral image compression scheme based on intra-band prediction and inter-band fractal encoding is put forward in this paper. The hyperspectral image is firstly partitioned into several groups of bands (GOBs). Intra-band prediction is applied to the first band in each GOB, exploiting spatial correlation, while inter-band fractal encoding with a local search algorithm is applied to the other bands in each GOB, making use of the local similarity between two adjacent bands. The fractal parameters are signed Exp-Golomb entropy encoded. To improve the decoded quality, the prediction error and fractal residual are further transformed, quantized, and entropy encoded. Experimental results illustrate that the proposed scheme can obtain a better compression performance with low complexity compared with other well-known methods. In addition, the effect of compression on SVM (Support Vector Machine) classification is presented.