GAO Fei, MA Fei, LUO Xiling, WANG Jun, SUN Jinping. Hierarchical Feature-Based Detection Method for SAR Targets Under Complex Environment[J]. Chinese Journal of Electronics, 2017, 26(3): 647-653. doi: 10.1049/cje.2017.04.012
Citation: GAO Fei, MA Fei, LUO Xiling, WANG Jun, SUN Jinping. Hierarchical Feature-Based Detection Method for SAR Targets Under Complex Environment[J]. Chinese Journal of Electronics, 2017, 26(3): 647-653. doi: 10.1049/cje.2017.04.012

Hierarchical Feature-Based Detection Method for SAR Targets Under Complex Environment

doi: 10.1049/cje.2017.04.012
Funds:  This work is supported by the National Natural Science Foundation of China (No.61071139, No.61471019, No.61671035), the Aeronautical Science Foundation of China (No.20142051022), and the Foundation of ATR Key Lab.
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  • Corresponding author: LUO Xiling (corresponding author) was born in 1974. He received the B.E. degree in electronics and information engineering of Beihang University. His research interests include computer vision, air traffic management, etc. (Email:luoxiling@buaa.edu.cn)
  • Received Date: 2015-01-12
  • Rev Recd Date: 2015-03-25
  • Publish Date: 2017-05-10
  • A reliable target detection method for Synthetic aperture radar (SAR) images is needed urgently with the wide application of SAR systems. The performance of conventional detection algorithms, such as Constant false alarm rate (CFAR), degrade significantly in low SCR or complex regions while the Human visual system (HVS) can identify targets of interest without knowing characteristics of the background even in complicated environment. The combination of HVS with the SAR-ATR system may effectively achieve real-time multi-target detection in complex occlusion scenes. A new effective target detection algorithm is put forward using hierarchical characteristics of targets. Inspired by different roles of the retina and visual cortex in the HVS, this detection algorithm is divided into coarse detection and fine detection stage. Two kinds of features based on the correlation between target features and suspicious targets, namely overall feature and refined feature, are used in these two stages respectively to extract real targets. Experimental results verify its correctness and effectiveness in complex environment.
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