8 March 2018 Convolutional neural network using generated data for SAR ATR with limited samples
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091P (2018) https://doi.org/10.1117/12.2292997
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
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.
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Longjian Cong, Lei Gao, Hui Zhang, Peng Sun, "Convolutional neural network using generated data for SAR ATR with limited samples", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091P (8 March 2018); doi: 10.1117/12.2292997; https://doi.org/10.1117/12.2292997
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