遥感图像融合是遥感图像应用的一个重要处理步骤.随着遥感图像数据规模与融合算法计算复杂度的增大,遥感图像融合面临着处理速度的挑战.最近几年,GPU计算能力得到极大提升,面向通用计算的应用得到了快速发展.本文基于GPU编程模型和硬件特性,深入研究了遥感图像融合的并行加速算法,提出了适合融合执行流的并行映射模型.本文选取计算量大、计算精度高的IHS增强小波融合算法进行GPU并行设计,并针对主流的GPU平台在数据传输、循环优化、线程设计等方面进行了优化,最后在nVIDIA GTX 460 GPU上进行了实验.实验结果表明,本文设计的并行映射模型及优化策略能够很好地适用于遥感图像融合应用,最大加速比达到了114倍.研究表明,GPU通用计算技术在遥感图像处理领域具有广阔的应用前景.%Remote sensing image fusion is an important processing step of the application of remote sensing images. With the scale of remote sensing image data and complexity of fusion algorithm increasing, the remote sensing image fusion is facing a challenge on the processing speed. In recent years,the power of the computing of GPU has been greatly improved, which results that using it for the general-purpose computing has a rapid development. In this paper, based on GPU programming mode and its hardware features,the parallel accelerated algorithm of remote sensing image fusion is studied,and a parallel mapping model for the fusion execution stream is proposed. The IHS- and wavelet-based fusion algorithm with high accuracy and complexity of calculation is selected to design the parallel processing method on GPU, also some optimizations on data transfer, loop unrolling, thread setting,et al are done for the mainstream GPU hardware. Finally, the results of experiment on the GPU of nVIDIAGTX 460are given, which shows that our proposed parallel mapping model and the optimization strategy can be well applied to the field of remote sensing image fusion. In our experiment,the maximum speedup is up to 114X compared with the serial CPU program. This study also shows that the general computing technology of GPU has broad application prospects in the field of remote sensing image processing.
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