Abstract
High-resolution synthetic aperture radar (SAR) can provide a rich information source for target detection and greatly increase the types and number of target characteristics. How to efficiently extract the target of interest from large amounts of SAR images is the main research issue. Inspired by the biological visual systems, researchers have put forward a variety of biologically inspired visual models for target detection, such as classical saliency map and HMAX. But these methods only model the retina or visual cortex in the visual system, which limit their ability to extract and integrate targets characteristics; thus, their detection accuracy and efficiency can be easily disturbed in complex environment. Based on the analysis of retina and visual cortex in biological visual systems, a progressive enhancement detection method for SAR targets is proposed in this paper. The detection process is divided into RET, PVC, and AVC three stages which simulate the information processing chain of retina, primary and advanced visual cortex, respectively. RET stage is responsible for eliminating the redundant information of input SAR image, enhancing inputs’ features, and transforming them to excitation signals. PVC stage obtains primary features through the competition mechanism between the neurons and the combination of characteristics, and then completes the rough detection. In the AVC stage, the neurons with more receptive field compound more precise advanced features, completing the final fine detection. The experimental results obtained in this study show that the proposed approach has better detection results in comparison with the traditional methods in complex scenes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Novak LM, Halversen SD, Owirka GJ, et al. Effects of polarization and resolution on SAR ATR. Aerosp Electron Syst. 1997;33(1):102–16.
Di Bisceglie M, Galdi C. CFAR detection of extended objects in high-resolution SAR images. Geosci Remote Sens. 2005;43(4):833–43.
Bai X, Zhou F, Jin T. Enhancement of dim small target through modified top-hat transformation under the condition of heavy clutter. Signal Process. 2010;90(5):1643–54.
Deng X, Ma Y. PCNN model analysis and its automatic parameters determination in image segmentation and edge detection. Chin J Electron. 2014;23(1):97–103.
Francis C. Astonishing hypothesis: the scientific search for the soul. New York: Simon and Schuster; 1995.
Rodieck RW. Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Res. 1965;5(12):583–601.
Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis. IEEE Trans Pattern Anal Mach Intell. 1998;20(11):1254–9.
Zhao J, Sun S, Liu X, et al. A novel biologically inspired visual saliency model. Cognit Comput. 2014;6(4):841–8.
Hubel DH, Wiesel TN. Ferrier lecture: Functional architecture of macaque monkey visual cortex. Proc R Soc Lond B Biol Sci. 1977;1130:1–59.
Jones JP, Palmer LA. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol. 1987;58(6):1233–58.
Amoon M, Rezai-rad G, Daliri MR. PSO-based optimal selection of zernike moments for target discrimination in high-resolution SAR imagery. J Indian Soc Remote Sens. 2014;42(3):483–93.
Tu Z, Zheng A, Yang E, et al. A biologically inspired vision-based approach for detecting multiple moving objects in complex outdoor scenes. Cognit Comput. 2015;7(5):1–13.
Ho-Phuoc T, Guyader N, Guérin-Dugué A. A functional and statistical bottom-up saliency model to reveal the relative contributions of low-level visual guiding factors. Cognit Comput. 2010;2(4):344–59.
Poggio MRAT. Hierarchical models of object recognition in cortex. Nat Neurosci. 1999;2(11):7.
Serre T, Wolf L, Bileschi S, et al. Robust object recognition with cortex-like mechanisms. Pattern Anal Mach Intell. 2007;29(3):411–26.
Mutch J, Lowe DG. Multiclass object recognition with sparse, localized features computer vision and pattern recognition. In: 2006 IEEE computer society conference on IEEE. 2006;1:11–18.
Zhang L, Tjondronegoro D. Facial expression recognition using facial movement features. Affect Comput. 2011;2(4):219–29.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 61071139, 61471019, 61171122), the Aeronautical Science Foundation of China (No. 20142051022), the Foundation of ATR Key Lab. It is also supported by the Royal Society of Edinburgh (RSE) and the National Natural Science Foundation of China (NNSFC) under the RSE-NNSFC joint projects (2012–2015) [Grant Numbers 61211130309 and 61211130210] with Beihang University and Anhui University, China, respectively. It was also supported in part by the “Sino-UK Higher Education Research Partnership for Ph.D. Studies” Joint Project (2013–2015) funded by the British Council China and the China Scholarship Council (CSC).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Fei Gao, Fei Ma, Yaotian Zhang, Jun Wang, Jinping Sun, Erfu Yang and Amir Hussain declare that they have no conflict of interest.
Informed Consent
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.
Human and Animal Rights
This article does not contain any studies with human or animal subjects performed by the any of the authors.
Rights and permissions
About this article
Cite this article
Gao, F., Ma, F., Zhang, Y. et al. Biologically Inspired Progressive Enhancement Target Detection from Heavy Cluttered SAR Images. Cogn Comput 8, 955–966 (2016). https://doi.org/10.1007/s12559-016-9405-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-016-9405-9