FPGA Optimization for Hyperspectral Target Detection with Collaborative Representation

Currently, remote sensing image processing raises much higher requirements on the computing platform and processing speed. The high speed, low power, reconfigurable and radiation resistance features of Field Programmable Gate Arrays (FPGA) makes it become a better choice for real-time processing in hyperspectral imagery. In this paper, we have optimized the newly proposed hyperspectral target detection algorithm based on FPGA. The collaborative representation is a high-efficiency target detection (CRD) algorithm in hyperspectral imagery, which is directly based on the concept that the target pixels can be approximately represented by its spectral signatures, while the other cannot. Using the Sherman-Morrison formula to calculate the matrix inversion and the difficulty of implementing the overall CRD algorithm on the FPGA is reduced. The running speed of parallel programming is greatly promoted on the FPGA under the premise of reasonable resources. The experimental results demonstrate that the proposed system has significantly improved the processing time when compared to the pre-optimized system and the 3.40 GHz CPU.