An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images

Wang Siyu; Gao Xin; Sun Hao; Zheng Xinwei; Sun Xian

doi:10.12000/JR17009

Volume 6 Issue 2

May 2017

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Article Navigation > Journal of Radars > 2017 > 6(2): 195-203

ZHANG Xuqi, ZHOU Bin, LIU Haiqi, et al. A scalable method for group target tracking using multisensor with limited field of views[J]. Journal of Radars, 2024, 13(6): 1220–1238. doi: 10.12000/JR24054

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Wang Siyu, Gao Xin, Sun Hao, Zheng Xinwei, Sun Xian. An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images[J]. Journal of Radars, 2017, 6(2): 195-203. doi: 10.12000/JR17009

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An Aircraft Detection Method Based on Convolutional Neural Networks in High-Resolution SAR Images

DOI: 10.12000/JR17009

Wang Siyu^{①,②
,},
Gao Xin^{①
,
,},
Sun Hao^{①
,},
Zheng Xinwei^{①
,},
Sun Xian^{①
,}

①.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
②.
University of Chinese Academy of Science, Beijing 100049, China

Funds: The National Natural Science Foundation of China (41501485)

Received Date: 2017-01-20
Rev Recd Date: 2017-03-05

Available Online: 2017-05-02

Publish Date: 2017-04-28

Abstract

Abstract

In the field of image processing using Synthetic Aperture Radar (SAR), aircraft detection is a challenging task. Conventional approaches always extract targets from the background of an image using image segmentation methods. Nevertheless, these methods mainly focus on pixel contrast and neglect the integrity of the target, which leads to locating the object inaccurately. In this study, we build a novel SAR aircraft detection framework. Compared to traditional methods, an improved saliency-based method is proposed to locate candidates coarsely and quickly in large scenes. This proposed method is verified to be more efficient compared with the sliding window method. Next, we design a convolutional neural network fitting in SAR images to accurately identify the candidates and obtain the final detection result. Moreover, to overcome the problem of limited available SAR data, we propose four data augmentation methods comprising translation, speckle noising, contrast enhancement, and small-angle rotation. Experimental results show that our framework achieves excellent performance on the high-resolution TerraSAR-X dataset.
- Synthetic Aperture Radar (SAR),
- Aircraft detection,
- Convolutional Neural Networks (CNNs),
- Data augmentation,
- Visual saliency

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References

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