基于FPGA的高光谱异常目标检测RXD算法加速方案

高光谱图像在带来丰富光谱信息的同时,其数据量大和维数高的特性也使得各种目标检测算法进行处理时往往产生庞大的运算量,所以采用可以实现高光谱异常目标检测算法的高速处理方案显得尤为迫切和重要。考虑到现场可编程门阵列（FPGA）强大的并行计算能力和极具灵活的设计方式,针对高光谱异常目标检测RXD算法中协方差矩阵及其逆的计算量过大的问题,以分块并行和正交三角（QR）分解为主要加速思想,利用高层次综合（HLS）工具对算法进行优化,提出了RXD算法在FPGA平台上的加速方案。实验结果表明,所提出的基于FPGA平台的加速方案可以在保持算法检测性能的同时达到相较于CPU实现7.04倍的加速,验证了加速方案的正确有效性。

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

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.