基于GPU和分块技术的巨幅影像快速傅里叶变换算法研究

doi:10.3964/j.issn.1000-0593(2014)02-0498-07

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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.

Key words：GPU;HRFFT;FFT;Remote sensing image

Received: 2013-04-16 Accepted: 2013-07-15

ZTFLH:

TP751

Corresponding Authors: LI Xue-you, LI Jia-guo E-mail: jacoli@126.com; lixueyou321@126.com

Cite this article:

YANG Xue,LI Xue-you,LI Jia-guo, et al. Research on Fast Fourier Transforms Algorithm of Huge Remote Sensing Image Technology with GPU and Partitioning Technolog[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34(02): 498-504.

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2014)02-0498-07 OR https://www.gpxygpfx.com/EN/Y2014/V34/I02/498

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