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Content Provider | IET Digital Library |
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Author | Tang, Jiaxin Zhang, Fan Yin, Qiang Hu, Wei |
Abstract | Synthetic aperture radar (SAR) target recognition can provide effective target category information and becomes the key part of SAR image application. Machine learning and deep learning are two main methods of target recognition. Normally, through the massive image features learning, the trained model can be used to infer the possible categories for various new target images, but the overfitting problem caused by limited data samples always makes the trained model unusable. In order to solve this case, the authors introduce a dual-input Siamese convolution neural network to the small samples oriented SAR target recognition. The training method looks like a kind of data enhancement method, but there are some differences between them. In the experiments, only 15 training samples are used to complete a three-class tank classification task. It means each category has just five samples while the number of corresponding testing data is 195. As a result, the recognition accuracy of the authors’ method outperforms the support vector machine, A-ConvNet, and 18-layers ResNet by 31, 13 and 16%, respectively. Siamese network has a good performance in small samples classification and the results prove the validity of the network. |
Starting Page | 6845 |
Ending Page | 6847 |
Page Count | 3 |
Volume Number | 2019 |
e-ISSN | 20513305 |
Issue Number | Issue 20, Oct (2019) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/joe/2019/20 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0566 |
Journal | The Journal of Engineering |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2019-07-11 |
Access Restriction | Open |
Rights License | Creative Commons Attribution -Non Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) |
Subject Keyword | Computer Vision And Image Processing Technique Convolutional Neural Nets Data Enhancement Method Data Samples Deep Learning Dual-input Siamese Convolution Neural Network Effective Target Category Information Electrical Engineering Computing Feature Extraction Image Classification Image Recognition Incomplete Training Datasets Knowledge Engineering Technique Learning in AI Machine Learning Massive Image Feature Neural Computing Technique Radar Computing Radar Equipment Radar Imaging Radar Target Recognition Sample Classification SAR Image Application SAR Target Recognition Synthetic Aperture Radar Synthetic Aperture Radar Target Recognition System And Application Target Image Testing Data Training Method |
Content Type | Text |
Resource Type | Article |
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