提出了一种线性预测和多谱带查表相结合的高光谱图像无损压缩算法。首先, 根据高光谱图像谱带间具有强相关性的特点, 建立基于Yule-Walker方程的线性预测模型, 其中方程系数矩阵为非Toeplitz形式的对称矩阵, 需要使用改进的Levinson算法进行求解。其次, 针对校正后的高光谱图像具有稀疏直方图的特点, 提出了多谱带查表法, 对线性预测的结果进行修正, 去除这些图像中因校正引起的信息冗余; 而对未校正图像, 则不使用该步骤处理。最后, 使用熵编码器对预测误差进行编码。分别使用自适应算术编码和Golomb-Rice编码作为熵编码器进行了测试, 结果表明: 本文算法具有较高的压缩比, 压缩效果好于国际空间数据系统咨询委员会(CCSDS)的标准算法。

A lossless compression scheme consisting of a linear prediction and multiband lookup tables was proposed to compress the airborne hyperspectral imagery efficiently. Firstly, based on the Yule-Walker equation, a linear prediction model whose equation coefficient matrix is a non-Toeplitz type covariance matrix and it should be solved by an extension form of Levinson algorithm was established by exploiting the strong correlation of spectral bands of hyperspectral imagery. Then, a multiband lookup table algorithm was adopted to refine the prediction result based on the calibrated hyperspectral imagery containing a sparse histogram induced by calibration techniques. However, for the uncalibrated imagery, the multiband lookup tables could be neglected. Finally, the prediction residuals were sent to the entropy encoder. In the experiment, the Adaptive Arithmetic Code and Golomb-Rice Code were both tested as the entropy encoder. The experimental results show that the proposed scheme has a higher compression ratio and the compression effect is better than that of the standard from Consultative Committee for Space Data System(CCSDS).