基于GPU和分块技术的巨幅影像快速傅里叶变换算法研究

快速傅里叶变换(FFT)是遥感影像处理的基础方法，随着高光谱、高空间和高时间分辨率遥感影像获取能力的提升，如何利用快速傅里叶变换技术快速有效地处理巨幅遥感影像是当前遥感影像处理技术中的重要环节和研究热点。傅里叶变换算法FFT是基本的图像处理算法之一，该算法可进行遥感影像的条带噪声去除、影像压缩和影像配准处理等多种用途。 CUFFT函数库是NVIDIA公司提供的基于GPU的FFT算法库， FFTW是由MIT科学实验室计算机组在PC平台上开发的基于CPU的FFT算法，是目前在基于CPU的运行速度最快的FFT算法函数库，这两种实现共有的问题是当可用内存或显存的容量小于图像容量时，就会出现内存或显存溢出。针对这种问题，提出了一种基于GPU和分块技术的巨幅遥感影像快速傅里叶变换（huge remote fast Fourier transform, HRFFT）算法。通过对CUDA的CUFFT函数库中的FFT算法进行改进，解决了巨幅图像内存或显存溢出的问题，并结合HJ-1A卫星的CCD影像，通过实验与其他算法进行了对比，证明了该方法的合理性。在实际应用中，利用本文提出的HRFFT算法，改善了影像处理的效果，提高了遥感影像的质量，同时加快了影像处理的速度，节省了计算时间，取得了较好的效果。

Abstract

Fast Fourier transforms (FFT) is a basic approach to remote sensing image processing. With the improvement of capacity of remote sensing image capture with the features of hyperspectrum, high spatial resolution and high temporal resolution, how to use FFT technology to efficiently process huge remote sensing image becomes the critical step and research hot spot of current image processing technology. FFT algorithm, one of the basic algorithms of image processing, can be used for stripe noise removal, image compression, image registration, etc. in processing remote sensing image. CUFFT function library is the FFT algorithm library based on CPU and FFTW. FFTW is a FFT algorithm developed based on CPU in PC platform, and is currently the fastest CPU based FFT algorithm function library. However there is a common problem that once the available memory or memory is less than the capacity of image, there will be out of memory or memory overflow when using the above two methods to realize image FFT arithmetic. To address this problem, a CPU and partitioning technology based Huge Remote Fast Fourier Transform (HRFFT) algorithm is proposed in this paper. By improving the FFT algorithm in CUFFT function library, the problem of out of memory and memory overflow is solved. Moreover, this method is proved rational by experiment combined with the CCD image of HJ-1A satellite. When applied to practical image processing, it improves effect of the image processing, speeds up the processing, which saves the time of computation and achieves sound result.

参考文献

[1] Ling Y R, Ehlers M, Usery E L, et al. ISPRS Journal of Photogrammetry & Remote Sensing, 2007， (61): 381.

[2] Cooley J W， Tukey J W. Math. Comput., 1965， 19: 297.

[3] Singh K, Walters J P, Hestness J, et al. FFTW and Complex Ambiguity Function performance on the Maestro Processor. Aerospace Conference, 2011 IEEE. 5-12 March 2011. 1.

[4] Pairman D, Belliss S E, Cuff J, et al. Detection and mapping of irrigated farmland in Canterbury, New Zealand. Geoscience and Remote Sensing Symposium (IGARSS), 2011, IEEE International. 24-29 July 2011. 696.

[5] Wu Yue, Jia Weile, Wang Lin. GPU Tuning for First-Principle Electronic Structure Simulations.Springer Berlin Heidelberg. 2013. 235.

[6] Nukada A, Ogata Y, Endo T, et. Bandwidth Intensive 3-D FFT Kernel for GPUs using CUDA. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing, IEEE Press, 2008. 1.

[7] Song Jianghong, Zhao Zhongming, Wang Gang. Journal of Computer-Aided Design & Computer Graphics, 2005, 17(7): 1517.

[8] Kenneth Moreland， Edward Angel. The FFT on GPU. In Proceedings of Graphics Hardware, San Diego, 2003， 117(17)： 112.

[9] Xiao Jiang, Hu Keliang, Deng Yuanyong. Computer Engineering， 2009， 35(10): 7.

[10] Steven M de Jong， Freek D van der Meer. Remote Sensing Image Analysis. P. O. Box 17, 3300AA Dordrecht. The Netherlands. 2006. 26.

[11] Govindaraju N K, Lloyd B, Dotsenko Y, et al. High Performance Discrete Fourier Transforms on Graphics Processors. In Proceedings of the 2008 ACM/IEEE Conference on Supercomputing,. IEEE Press, 2008. 1.

[12] Chen Y, Cui X， Mei H. Large-scale FFT on GPU Clusters. 24th International Conference on Supercomputing (ICS’10), ACM， 2010. 315.

[13] Wang Long, Jia Weile, Chi Xuebin, et al. Large Scale Plane Wave Pseudopotential Density Functional Theory Calculations on GPU Clusters，Supercomputing Conference 2011.

[14] Ogata Y, Endo T, Maruyama N, et al. An Efficient, Model-Based CPU-GPU Heterogeneous FFT Library. In Parallel and Distributed Processing, 2008. IPDPS 2008. IEEE International Symposium on. April 2008. 1.

[15] Gac N S， Mancini, M Desvignes， et al. EURASIP Journal on Embedded Systems-Special Issue on Design and Architectures for Signal and Image Processing， 2008, Article 5.

杨雪, 李学友, 李家国, 马骏, 张力, 杨健, 杜全叶. 基于GPU和分块技术的巨幅影像快速傅里叶变换算法研究[J]. 光谱学与光谱分析, 2014, 34(2): 498. YANG Xue, LI Xue-you, LI Jia-guo, MA Jun, ZHANG Li, YANG Jan, DU Quan-ye. Research on Fast Fourier Transforms Algorithm of Huge Remote Sensing Image Technology with GPU and Partitioning Technology[J]. Spectroscopy and Spectral Analysis, 2014, 34(2): 498.

基于GPU和分块技术的巨幅影像快速傅里叶变换算法研究

关于本站 Cookie 的使用提示

全站搜索

基于GPU和分块技术的巨幅影像快速傅里叶变换算法研究

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索