结合多种分解策略的遥感影像去相关拉伸并行处理方法  

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

作　　者：杨景辉[1,2] 张继贤[1,2] 

机构地区：[1]武汉大学资源与环境科学学院,湖北武汉430079 [2]中国测绘科学研究院,北京100830

出　　处：《武汉大学学报（信息科学版）》2016年第3期402-407,共6页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(40901229);国家863计划(2011AA120401)~~

摘　　要：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。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 de- composition in the linear transformation. The whole procedure is parallelized. The parallel experi- ments of decorrelation stretching for two datasets, the airborne hyperspectral image OMIS and satel- lite 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 comput- ers with cores ranging from 12 to 16 by correctly configuring the number of cores and disks. Mean- while, 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.

关 键 词：遥感影像 去相关拉伸 并行计算 

分 类 号：P237[天文地球—摄影测量与遥感] TP751[天文地球—测绘科学与技术]