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Convolutional neural network using generated data for SAR ATR with limited samples
Author(s): Longjian Cong; Lei Gao; Hui Zhang; Peng Sun
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Paper Abstract

Being able to adapt all weather at all times, it has been a hot research topic that using Synthetic Aperture Radar(SAR) for remote sensing. Despite all the well-known advantages of SAR, it is hard to extract features because of its unique imaging methodology, and this challenge attracts the research interest of traditional Automatic Target Recognition(ATR) methods. With the development of deep learning technologies, convolutional neural networks(CNNs) give us another way out to detect and recognize targets, when a huge number of samples are available, but this premise is often not hold, when it comes to monitoring a specific type of ships. In this paper, we propose a method to enhance the performance of Faster R-CNN with limited samples to detect and recognize ships in SAR images.

Paper Details

Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091P (8 March 2018); doi: 10.1117/12.2292997
Show Author Affiliations
Longjian Cong, Beijing Aerospace Automatic Control Institute (China)
National Key Lab. of Science and Technology on Aerospace Intelligence Control (China)
Lei Gao, Beijing Aerospace Automatic Control Institute (China)
National Key Lab. of Science and Technology on Aerospace Intelligence Control (China)
Hui Zhang, Beijing Aerospace Automatic Control Institute (China)
National Key Lab. of Science and Technology on Aerospace Intelligence Control (China)
Peng Sun, Beijing Aerospace Automatic Control Institute (China)
National Key Lab. of Science and Technology on Aerospace Intelligence Control (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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