CPU-GPU协同计算的遥感仿真图像MTF退化并行算法  

A CPU-GPU collaboration based computing parallel algorithm for MTF degradation of remote sensing simulation images

作　　者：赵瑞斌[1] 赵生慧[1] 胡新礼[2] 

机构地区：[1]滁州学院计算机与信息工程学院,安徽滁州239000 [2]中国科学院遥感与数字地球研究所,北京100101

出　　处：《计算机工程与科学》2015年第7期1258-1264,共7页Computer Engineering & Science

基　　金：安徽省高校自然科学基金重点项目(KJ2014A184);安徽省高校自然科学基金资助项目(KJ2013B183);安徽省自然科学基金资助项目(1408085MF126)

摘　　要：在遥感图像仿真中,为了定量模拟并分析平台抖动、探测器电子特性、大气衰减等因素对遥感成像质量的影响,需要有效计算遥感系统的调制传递函数MTF,并将其快速作用到仿真图像上。然而,由于遥感仿真图像的大数据量特性以及MTF退化包含多个计算密集型算法,使得计算效率成为一个瓶颈问题。为此,根据已有研究提出的MTF计算模型,分析了遥感仿真图像MTF退化的一般流程及主要环节的算法复杂度。在此基础上,提出了一种CPU-GPU协同计算的遥感仿真图像MTF退化并行算法。实验结果表明,该并行算法有效地发挥了GPU并行计算能力,并明显提高了MTF退化处理效率。In order to quantitatively simulate and analyze the impact on the quality of the remote sensing system from factors such as platform jitter,electronic properties,and atmospheric attenuation,it is necessary to compute the modulate transfer function （MTF） of the remote sensing system and operate it in simulation images. However, because of the characteristics of big data in remote sensing image simu- lation and a number of intensive algorithms involved in the computing of MTF degradation, calculating efficiency becomes the bottleneck problem. Thus, according to the existing MTF calculation model, we analyze the general process of the MTF degradation of remote sensing simulated images and the com- plexity of the main steps in the algorithm. Based on this, we propose a CPU-GPU collaboration based computing parallel algorithm for the MTF degradation of remote sensing simulation images. Experimen- tal results show that the algorithm can make full use of the parallel computing capacity of GPUs and im- prove the computation efficiency of the MTF degradation.

关 键 词：遥感仿真图像 MTF退化 并行计算 GPU CUDA 

分 类 号：TP751.1[自动化与计算机技术—检测技术与自动化装置]