Volume 44 Issue 12
Dec.  2018
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ZHENG Yuxuan, LI Yunsong, SHI Yanzi, et al. Acceleration scheme of RXD algorithm based on FPGA for hyperspectral anomaly target detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2556-2567. doi: 10.13700/j.bh.1001-5965.2018.0344(in Chinese)
Citation: ZHENG Yuxuan, LI Yunsong, SHI Yanzi, et al. Acceleration scheme of RXD algorithm based on FPGA for hyperspectral anomaly target detection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(12): 2556-2567. doi: 10.13700/j.bh.1001-5965.2018.0344(in Chinese)

Acceleration scheme of RXD algorithm based on FPGA for hyperspectral anomaly target detection

doi: 10.13700/j.bh.1001-5965.2018.0344
Funds:

National Natural Science Foundation of China 61502367

National Natural Science Foundation of China 61501346

National Natural Science Foundation of China 61701360

National Natural Science Foundation of China 61571345

National Natural Science Foundation of China 91538101

111 Project B08038

Yangtze River Scholar Bonus Schemes of China CJT160102

More Information
  • Corresponding author: LI Yunsong, E-mail: ysli@mail.xidian.edu.cn
  • Received Date: 11 Jun 2018
  • Accepted Date: 13 Jul 2018
  • Publish Date: 20 Dec 2018
  • Hyperspectral images bring abundant spectral information, but their large size and high dimensionality also lead to huge calculation. Therefore, it is particularly urgent to develop a high-speed processing scheme for anomaly target detection algorithms. Considering that the field programmable gate arrays (FPGA) are of powerful parallel capability and highly flexible design, aiming at the problem that the computation of the covariance matrix and its inverse is too large in the Reed-Xiaoli Detector (RXD) algorithm, we propose an acceleration scheme of block parallel and QR decomposition for the RXD algorithm based on the FPGA platform, which is optimized by high level synthesis (HLS). Experimental results show that the processing speed of FPGA-based acceleration scheme proposed in this paper is 7.04 times faster than that of CPU-based implementations with the detection performance preserved simultaneously, which verifies that the proposed acceleration scheme is correct and effective.

     

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