最佳递归双向预测的高光谱图像无损压缩

摘要

为了解决海量高光谱遥感图像对传输和存储带来的压力,提出一种基于最佳递归双向预测的高光谱图像无损压缩算法.首先根据高光谱图像各波段的谱间相关系数,选择相应的压缩方式.谱间相关系数＜0.9的波段使用bzip2模式进行压缩.谱间相关系数＞0.9的波段,则对参考波段进行单波段最佳前向预测,非参考波段采用最佳递归双向预测,并对预测残差采用JPEG-LS模式压缩.实验结果表明,对AVIRIS高光谱图像进行压缩,该算法的平均压缩比达到3.217,优于其他无损压缩算法0.09～1.374.该方法运算速度快,压缩效果好,很有应用前景.%To solve the transmission and storage problems resulting from massive hyperspectral remote sensing data, a lossless compression algorithm based on the optimal recursive bidirectional prediction for hyperspectral images is presented. Different compression models for each band are chosen according to their spectral correlation factors. If the spectral correlation factor is less than 0.9, the bzip2 compression model is chosen. Otherwise, the single band optimal previous prediction is performed on the reference band and the optimal recursive bidirectional prediction is performed on the nonreference band.Furthermore, the residual images are coded by JPEGLS. The algorithms designed in this paper has been applied to the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data,the result shows that the average compression ratio is 3.217, which is 0.09-1.374 higher than those from other lossless compression algorithms. This method is fast and works efficiently, so it can be widely used in practice.