The efficient compression of onboard hyperspectral images has been a difficult problem which needs to be resolved urgently. Low encoding complexity and excellent error resilience are provided by distributed source coding, which has wide applied foreground in the field of hyperspectral images compression. For the problem of onboard compression for hyperspectral images, a distributed near lossless compression algorithm based on multi- level coset codes is proposed. According to the procedure of Slepian- Wolf lossless coding based on multi- level coset codes, an optimal quantization scheme for distributed near lossless compression of hyperspectral images is presented, which makes the distortion of hyperspectral images minimum under the given target bit-rates. Slepian-Wolf lossless coding is performed on the quantized values, which realizes the distributed near lossless compression of hyperspectral images. Experimental results show that the proposed algorithm can obtain both high near lossless compression performance and low encoding complexity compared with those existed classical algorithms.