方留杨, 王密, 潘俊. CPU和GPU协同的多光谱影像快速波段配准方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(7): 1000-1007. DOI: 10.13203/j.whugis20160218
引用本文: 方留杨, 王密, 潘俊. CPU和GPU协同的多光谱影像快速波段配准方法[J]. 武汉大学学报 ( 信息科学版), 2018, 43(7): 1000-1007. DOI: 10.13203/j.whugis20160218
FANG Liuyang, WANG Mi, PAN Jun. CPU/GPU Cooperative Fast Band Registration Method for Multispectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1000-1007. DOI: 10.13203/j.whugis20160218
Citation: FANG Liuyang, WANG Mi, PAN Jun. CPU/GPU Cooperative Fast Band Registration Method for Multispectral Imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(7): 1000-1007. DOI: 10.13203/j.whugis20160218

CPU和GPU协同的多光谱影像快速波段配准方法

CPU/GPU Cooperative Fast Band Registration Method for Multispectral Imagery

  • 摘要: 随着遥感影像数据量的飞速增长,传统的串行波段配准方法已无法满足大数据多光谱影像的实时配准需求。针对该问题,提出了一种CPU和GPU协同的多光谱影像快速波段配准方法。首先进行计算量和并行度分析,将同名点匹配和微分纠正映射至GPU执行,仿射变换系数拟合仍驻留在CPU执行。其次通过核函数任务映射和基本设置,使算法步骤在GPU上可执行,并设计了3种性能优化方法(访存优化、指令优化、传输计算堆叠),进一步提高了波段配准的执行效率。在NVIDIA Tesla M2050 GPU和Intel Xeon E5650 CPU组成的实验平台上,对遥感26号卫星多光谱影像的实验表明,使用该方法加速后的波段配准执行时间仅为3.25 s,与传统串行方法相比,加速比达到了32.32倍,可以满足大数据多光谱影像的近实时配准需求。

     

    Abstract: With the rapid increase of data size of remote sensing images, the traditional serial band re-gistration method cannot meet the demand for real-time processing of big-data multispectral images. Therefore, a CPU/GPU cooperative fast band registration method for multispectral imagery is proposed in this paper. Firstly, the computational amount and degree of parallelism are analyzed; point matching and differential rectification are ported to GPU to execute while the affine transformation parameter is still calculated on CPU. Secondly, kernel task assignment and basic settings are made to ensure the two above GPU steps executable. Moreover, three performance optimization methods, including memory access optimization, instruction optimization and transmission/computation overlap, are designed to further improve the efficiency of band registration. The experimental results based on NVIDIA Tesla M2050 GPU and Intel Xeon E5650 CPU show that the running time of YG-26 multispectral image band registration is only 3.25 s with our method, which got a speedup ratio of 32.32 compared with the traditional CPU serial method. The proposed method can provide quasi-real-time processing capability for multispectral imagery with big data size.

     

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