Near lossless compression of hyperspectral images based on distributed source coding

Effective compression technique of on-board hyperspectral images has been an active topic in the field of hyperspectral remote sensintg. In order to solve the effective compression of on-board hyperspectral images, a new distributed near lossless compression algorithm based on multilevel coset codes is proposed. Due to the diverse importance of each band, a new adaptive rate allocation algorithm is proposed, which allocates rational rate for each band according to the size of weight factor defined for hyperspectral images subject to the target rate constraints. Multiband prediction is introduced for Slepian-Wolf lossless coding and an optimal quantization algorithm is presented under the correct reconstruction of Slepian-Wolf decoder, which minimizes the distortion of reconstructed hyperspectral images under the target rate. Then Slepian-Wolf encoder exploits the correlation of the quantized values to generate the final bit streams. Experimental results show that the proposed algorithm has both higher compression efficiency and lower encoder complexity than several existing classical algorithms.