Using deep learning to detect small targets in infrared oversampling images

doi:10.21629/JSEE.2018.05.07

Journal of Systems Engineering and Electronics ›› 2018, Vol. 29 ›› Issue (5): 947-952.doi: 10.21629/JSEE.2018.05.07

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Using deep learning to detect small targets in infrared oversampling images

Liangkui LIN*(), Shaoyou WANG(), Zhongxing TANG()

Shanghai Institute of Satellite Engineering, Shanghai 201109, China

Received:2017-11-17 Online:2018-10-26 Published:2018-11-14
Contact: Liangkui LIN E-mail:gxckk1980@sina.com;wong926@sina.com;yujishenlan@163.com
About author:LIN Liangkui was born in 1980. He received his Ph.D. degree in communication and information system from National University of Defense Technology in 2011. He is currently an engineer in the Shanghai Institute of Satellite Engineering. His research interests include machine learning, multisensor multi-target tracking, and image processing. E-mail: gxckk1980@sina.com|WANG Shaoyou was born in 1982. He received his M.S. degree in optical engineering from Harbin Institute of Technology in 2009. He is currently a senior engineer in the Shanghai Institute of Satellite Engineering. His research interests include design and analysis of space optical sensor, and infrared information processing. E-mail: wong926@sina.com|TANG Zhongxing was born in 1985. He received his Ph.D. degree in spacecraft design from Beihang University in 2014. He is currently an engineer in the Shanghai Institute of Satellite Engineering. His research interests include spacecraft dynamics and control, imaging task planning and scheduling. E-mail: yujishenlan@163.com
Supported by:
the National Key Research and Development Program of China(2016YFB0500901);the Natural Science Foundation of Shanghai(18ZR1437200);the Satellite Mapping Technology and Application National Key Laboratory of Geographical Information Bureau(KLSMTA-201709);This work was supported by the National Key Research and Development Program of China (2016YFB0500901), the Natural Science Foundation of Shanghai (18ZR1437200), and the Satellite Mapping Technology and Application National Key Laboratory of Geographical Information Bureau (KLSMTA-201709)

Abstract

Abstract:

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network (CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3 – 4 orders of magnitude, and has more powerful target detection performance.

Key words: infrared small target detection, oversampling, deep learning, convolutional neural network (CNN)

Liangkui LIN, Shaoyou WANG, Zhongxing TANG. Using deep learning to detect small targets in infrared oversampling images[J]. Journal of Systems Engineering and Electronics, 2018, 29(5): 947-952.

Figures/Tables 7

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References 26

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Target	GSCR				BSF
Target	Max-Median	Top-Hat	LRSMD	CNN	Max-Median	Top-Hat	LRSMD	CNN
T1	1.706 5	5.089 0	6.605 0	1.215 9×10⁴	3.462 1	6.492 7	8.988 9	1.807 9×10⁴
T2	0.851 0	3.504 6	6.889 9	1.318 4×10⁴	2.490 2	4.958 8	8.930 2	1.791 7×10⁴
T3	1.446 1	4.370 7	6.284 0	7.438 4×10³	2.754 0	4.690 9	7.555 7	9.627 1×10³
T4	4.271 1	10.213 8	7.261 6	1.126 5×10⁴	6.005 5	10.640 0	10.819 0	1.532 3×10⁴
T5	2.264 4	9.900 9	9.097 2	1.081 2×103⁴	5.754 5	13.115 2	14.231 6	1.202 0×10³

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Using deep learning to detect small targets in infrared oversampling images

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