Compression can significantly decrease hyper spectral images to relatively manageable sizes

thereby facilitating their efficient transmission and storage in aground station. Dictionary learning and sparse representation perform well in natural image compression. This study focuses on the properties of hyper spectral images and presents an efficient compression algorithm based on wavelets and dictionary learning for hyper spectral images.First

multi-scale training samples are built through wavelet decomposition; the training samples of each scale are sent to the K-SVD dictionary learning model to obtainmulti-scale dictionaries by joint training

whichsimultaneouslycalculates the errors and update the multi-scale dictionary. Second

statistical analysis is performed on the used dictionary atoms in local optimal bands in the process of sparse coding

and a frequency selection factor is introduced. The statistical information and frequency selection factor are then used to decrease on-used or rarely used atoms in the dictionary. Other bands can be sparsely and easily represented using the simplified dictionary. Finally

the simplified dictionary is directly entropy coded

and the DC component is entropy coded after 4-neighborhood prediction and differential pulse code modulation. The indices of the coefficients of each scale are rearranged according to the numerical value and are separately entropy coded after DPCM. The sparse coefficients are also rearranged according to the sequential changing of indices and are entropy coded together after adaptive quantization. Results show that the proposed scheme outperforms the traditional spatial and other multi-scale dictionary learning algorithms. It is also much better than3D-SPIHT in terms of bit rate distortion performance. As JPEG2000（ Part 2） largely benefits from the embedded block coding with optimized truncation strategy

it can achieve a better performance than our scheme. However

our proposed is much faster than JPEG2000（ Part 2）. This study designed a novel hyper spectral image compressor based on wavelets and dictionary learning.Experimental results reveal that the proposed scheme outperforms the 3D-SPIHT and most compression algorithms. It is also much faster than the state-of-art compression standard JPEG2000（ Part 2）. This compression algorithm can be improved further in the process of rearrangement before entropy coding. Our experiments prove that dictionary learning and sparse representation theories have great potential in hyper pectral image compression and interpretation applications. This study can motivate future research in this field.