结合多种分解策略的遥感影像去相关拉伸并行处理方法

提出了一种结合多种分解策略的遥感影像去相关拉伸并行处理方法,该方法根据不同步骤的特点采用不同任务分解策略:计算波段统计信息采用按波段进行任务分解,计算协方差矩阵采用按波段对进行任务分解,进行线性变换采用按数据块进行任务分解,实现了全过程的并行处理。在两台分别安装Windows 7和Linux操作系统的多核计算机下进行了OMIS机载高光谱影像和ASTER卫星影像的去相关拉伸并行处理实验,通过合理配置CPU核数和磁盘系统等,常用的12 ~ 16核计算机可取得最高约8倍的整体加速比。同时分析了影响整体加速性能的因素,给出了多核计算机用于遥感影像去相关拉伸并行处理的使用建议。

The Parallel Decorrelation Stretching with Multiple Decomposition Tactics for Remotely Sensed Imagery

This paper presents a parallel processing method of decorrelation stretching with multiple decomposition tactics for remotely sensed imagery. The method adopts different decomposition tactics for different steps in the whole procedure with band-based decomposition in the statistics of image bands, twin-band-based decomposition in the computation of the covariance matrix, and tile-based decomposition in the linear transformation. The whole procedure is parallelized. The parallel experiments of decorrelation stretching for two datasets, the airborne hyperspectral image OMIS and satellite image ASTER, are carried out on two multi-core computers respectively with Windows 7 and Linux operating systems. The results show that it can achieve whole-speedup up to eight on computers with cores ranging from 12 to 16 by correctly configuring the number of cores and disks. Meanwhile, the factors impacting the whole-speedup are analyzed, and usage suggestions for decorrelation stretching for remotely sensed imagery on the multi-core computer are proposed.