Remote Sensing Applications and Decision Support

Using compute unified device architecture-enabled graphic processing unit to accelerate fast Fourier transform-based regression Kriging interpolation on a MODIS land surface temperature image

[+] Author Affiliations
Hongda Hu

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, 129 Luoyu Road, Wuhan 430079, Hubei, China

Hong Shu

Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, 129 Luoyu Road, Wuhan 430079, Hubei, China

Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, Hubei, China

Zhiyong Hu

University of West Florida, Department of Environmental Studies, Pensacola 32514, Florida, United States

Jianhui Xu

Guangzhou Institute of Geography, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou 510070, Guangdong, China

J. Appl. Remote Sens. 10(2), 026036 (Jun 23, 2016). doi:10.1117/1.JRS.10.026036
History: Received March 24, 2016; Accepted June 1, 2016
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Abstract.  Kriging interpolation provides the best linear unbiased estimation for unobserved locations, but its heavy computation limits the manageable problem size in practice. To address this issue, an efficient interpolation procedure incorporating the fast Fourier transform (FFT) was developed. Extending this efficient approach, we propose an FFT-based parallel algorithm to accelerate regression Kriging interpolation on an NVIDIA® compute unified device architecture (CUDA)-enabled graphic processing unit (GPU). A high-performance cuFFT library in the CUDA toolkit was introduced to execute computation-intensive FFTs on the GPU, and three time-consuming processes were redesigned as kernel functions and executed on the CUDA cores. A MODIS land surface temperature 8-day image tile at a resolution of 1 km was resampled to create experimental datasets at eight different output resolutions. These datasets were used as the interpolation grids with different sizes in a comparative experiment. Experimental results show that speedup of the FFT-based regression Kriging interpolation accelerated by GPU can exceed 1000 when processing datasets with large grid sizes, as compared to the traditional Kriging interpolation running on the CPU. These results demonstrate that the combination of FFT methods and GPU-based parallel computing techniques greatly improves the computational performance without loss of precision.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Hongda Hu ; Hong Shu ; Zhiyong Hu and Jianhui Xu
"Using compute unified device architecture-enabled graphic processing unit to accelerate fast Fourier transform-based regression Kriging interpolation on a MODIS land surface temperature image", J. Appl. Remote Sens. 10(2), 026036 (Jun 23, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.026036


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