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

GAO Fei; MA Fei; LUO Xiling; WANG Jun; SUN Jinping

doi:10.1049/cje.2017.04.012

Volume 26 Issue 3

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Article Navigation > Chinese Journal of Electronics > 2017 > 26(3): 647-653

GAO Fei, MA Fei, LUO Xiling, et al., “Hierarchical Feature-Based Detection Method for SAR Targets Under Complex Environment,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 647-653, 2017, doi: 10.1049/cje.2017.04.012

Citation:

GAO Fei, MA Fei, LUO Xiling, et al., “Hierarchical Feature-Based Detection Method for SAR Targets Under Complex Environment,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 647-653, 2017, doi: 10.1049/cje.2017.04.012

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

doi: 10.1049/cje.2017.04.012

School of Electronic and Information Engineering, Beihang University, Beijing 100191, China

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.

More Information

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

Abstract

Abstract

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.
- Synthetic aperture radar (SAR),
- Targets detection,
- Human visual system (HVS),
- Complex background,
- Hierarchical model and X (HMAX)

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References(14)

References

L.M. Novak, S.D. Halversen, G.J. Owirka, et al., "Effects of polarization and resolution on SAR ATR", IEEE Transactions on Aerospace and Electronic Systems, Vol.33, No.3, pp.102-116, 1997.

M. Di Bisceglie and C. Galdi, "CFAR detection of extended objects in high-resolution SAR image", IEEE Transactions on Geoscience and Remote Sensing, Vol.43, No.4, pp.833-43, 2005

D. Wang, "Knowledge based object detection using SAR images", IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol.12, pp.269-272, 1987.

Z.L. Chen, B.J. Zou, X. Gao, H.L. Shen and X.Y. Zhang, "Fusion visual attention and low-level features in images for region of interest extraction", Chinese Journal of Electronics, Vol.22, No.2, pp.287-290, 2013.

J. Harel, C. Koch and P. Perona, "Graph-based visual saliency", Advances in Neural Information Processing Systems, pp.545-552, 2006.

A.Z. Hu, R. Zhang, D. Yin, et al., "Perceptual quality assessment of SAR image compression based on image content partition and neural network", Chinese Journal of Electronics, Vol.22, No.3, pp.543-548, 2013.

Q. Yu, "Rapid feed forward computation by temporal encoding and learning with spiking neurons", IEEE Transactions on Neural Networks and Learning Systems, Vol.24, No.10, pp.1539-1552, 2013.

X.Y. Deng and Y.D. Ma, "PCNN model analysis and its automatic parameters determination in image segmentation and edge detection", Chinese Journal of Electronics, Vol.23, No.1, pp.97-103, 2014.

Y. Yu, B. Wang and L. Zhang, "Hebbian-based neural networks for bottom-up visual attention and its applications to ship detection in SAR images", Neurocomputing, Vol.74, No.11, pp.2008-2017, 2011.

D.H. Hubel and T.N. Wiesel, "Ferrier lecture:Functional architecture of macaque monkey visual cortex", Proceedings of the Royal Society of London, Series B, Biological Sciences, pp.1-59, 1977.

M. Riesenhuber and T. Poggio, "Hierarchical models of object recognition in cortex", Nature neuroscience, Vol.2, No.11, pp.7, 1999.

T. Serre, L. Wolf, S. Bileschi, et al., "Robust object recognition with cortex-like mechanisms", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.29, No.3, pp.411-426, 2007.

J. Mutch and D.G. Lowe, "Object class recognition and localization using sparse features with limited receptive fields", International Journal of Computer Vision, Vol.80, No.1, pp.45-57, 2008.

H. Li, Y. Wei, L. Li, et al., "Hierarchical feature extraction with local neural response for image recognition", IEEE Transactions on Cybernetics, Vol.43, No.2, pp.412-424, 2013.

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