TY - GEN
T1 - Intelligent search optimized edge potential function (EPF) approach to synthetic aperture radar (SAR) scene matching
AU - Wang, Yifei
AU - Yin, Jihao
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/9/16
Y1 - 2014/9/16
N2 - Research on synthetic aperture radar (SAR) scene matching in the aircraft end-guidance has a significant value for both research and real-world application. The conventional scene matching methods, however, suffer many disadvantages such as heavy computation burden and low convergence rate so that these methods cannot meet the requirement of end-guidance system in terms of fast and real-time data processing. Furthermore, there are complex noises in the SAR image, which also compromise the effectiveness of using the conventional scene matching methods. To address the above issues, in this paper, the intelligent optimization method, Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration, has been introduced to tackle the SAR scene matching problem. We first establish the effective similarity measurement function for target edge feature matching through introducing the edge potential function (EPF) model. Then, a new method, ADEQFS-EPF, has been proposed for SAR scene matching. In ADEQFS-EPF, the previous studied theoretical model, ADEQFS, is combined with EPF model. We also employed three recent proposed evolutionary algorithms to compare against the proposed method on optical and SAR datasets. The experiments based on Matlab simulation have verified the effectiveness of the application of ADEQFS and EPF model to the field of SAR scene matching.
AB - Research on synthetic aperture radar (SAR) scene matching in the aircraft end-guidance has a significant value for both research and real-world application. The conventional scene matching methods, however, suffer many disadvantages such as heavy computation burden and low convergence rate so that these methods cannot meet the requirement of end-guidance system in terms of fast and real-time data processing. Furthermore, there are complex noises in the SAR image, which also compromise the effectiveness of using the conventional scene matching methods. To address the above issues, in this paper, the intelligent optimization method, Free Search with Adaptive Differential Evolution Exploitation and Quantum-Inspired Exploration, has been introduced to tackle the SAR scene matching problem. We first establish the effective similarity measurement function for target edge feature matching through introducing the edge potential function (EPF) model. Then, a new method, ADEQFS-EPF, has been proposed for SAR scene matching. In ADEQFS-EPF, the previous studied theoretical model, ADEQFS, is combined with EPF model. We also employed three recent proposed evolutionary algorithms to compare against the proposed method on optical and SAR datasets. The experiments based on Matlab simulation have verified the effectiveness of the application of ADEQFS and EPF model to the field of SAR scene matching.
UR - https://www.scopus.com/pages/publications/84908572268
U2 - 10.1109/CEC.2014.6900397
DO - 10.1109/CEC.2014.6900397
M3 - 会议稿件
AN - SCOPUS:84908572268
T3 - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
SP - 2124
EP - 2131
BT - Proceedings of the 2014 IEEE Congress on Evolutionary Computation, CEC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE Congress on Evolutionary Computation, CEC 2014
Y2 - 6 July 2014 through 11 July 2014
ER -