TY - GEN
T1 - FPGA optimization for hyperspectral target detection with collaborative representation
AU - Yang, Peidi
AU - Li, Wei
AU - Li, Xuebin
AU - Gao, Lianru
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/8
Y1 - 2018/10/8
N2 - 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 GHzCPU.
AB - 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 GHzCPU.
KW - Collaborative-representation-based detector (CRD)
KW - Field Programmable gate arrays (FPGAs)
KW - Hyperspectralimaging
KW - Real-time processing
KW - Target and anomaly detection
UR - http://www.scopus.com/inward/record.url?scp=85056507007&partnerID=8YFLogxK
U2 - 10.1109/PRRS.2018.8486378
DO - 10.1109/PRRS.2018.8486378
M3 - Conference contribution
AN - SCOPUS:85056507007
T3 - 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018
BT - 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 10th IAPR Workshop on Pattern Recognition in Remote Sensing, PRRS 2018
Y2 - 19 August 2018 through 20 August 2018
ER -