【摘要】 针对图像校正引起的高光谱图像的数据相关性,本文基于三级谱间预测和后向像素搜素(IP3-BPS)两阶预测提出了一种应用自适应预测器排序的三阶预测高光谱图像无损压缩算法。首先,根据高光谱图像相邻波段的相关系数大小进行自适应波段分组。然后,对谱间相关系数大于0.9的分组,利用校正引起的数据相关性和高光谱图像波段缩放因子分别给出一种递归双向像素搜索和一种自适应预测器排序技术;新形成的三阶预测算法将递归双向像素搜索和后向像素搜索作为最后两阶预测的预测器,并自适应调整两者的排序以获得更优的预测值。对机载可见光/红外成像光谱仪(AVIRIS′97)高光谱图像进行压缩的实验结果表明,提出的算法的平均比特率达到3.85bpp,优于其它无损压缩算法0.07~1.28bpp。该算法在计算复杂度较低的情况下,是一种高效的高光谱图像无损压缩方法。更多还原

【Abstract】 On the basis of third-order predictor and backward pixel search technology(IP3-BPS),a lossless compression third-order predictor algorithm using three-stage prediction with adaptive predictor reordering was proposed to overcome the calibration-induced data correlation of hyperspectral images.Firstly,hyperspectral images were divided into groups adaptively according to the correlation factor between adjacent bands.Then using the calibration-induced data correlation and the band scaling factor,a recursive Bidirectional Pixel Search(RBPS)method and an adaptive band grouping method were proposed,respectively,for these groups with spectral correlation factor more than 0.9.The proposed algorithm takes the recursive bidirectional pixel search and the backward pixel search as thelast two predictors,and adjusts adaptively their orders to achieve better prediction values.The experiments on the images from an Airborne Visible/Infrared Imaging Spectrometer(AVIRIS 1997)were performed.It shows that the average bit-rate of the proposed algorithm is 3.85bpp,0.07-1.28bpp higher than those of other lossless compression algorithms.It is an effective lossless compression method for hyperspectral images in low computational complexity.更多还原