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Optimization of Convolutional Neural Network Target Recognition Algorithm
Guo, Chen; Jiang, Yuanyuan
Department空间技术部
Source Publication14TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING (WICOM 2018)
2018
Volume306
Pages426-433
Language英语
ISSN2475-8841
ISBN978-1-6059-5578-0
AbstractThis paper proposes an optimized convolutional neural network target recognition algorithm for the problem of low recognition rate of synthetic aperture radar (SAR) target training, under the condition of insufficient tag data, translation, rotation and complexity. In order to overcome the shortage of tag data, the convolutional neural network is initialized with a feature set, obtained by principal component analysis (PCA) unsupervised training. In order to improve the training speed while avoiding overfitting, Rectified Linear Unit (ReLU) function is used as the activation function. In order to enhance robustness and reduce the effect of down sampling on feature representation, this work uses a maximum probability sampling method and normalizes the local contrast of feature after convolution layers. The experimental result shows that, compared with traditional convolutional neural network, this approach achieves a higher recognition rate for SAR target and better robustness to various image deformation and complex background.
KeywordConvolutional neural networks Deep learning principal component analysis
Conference Name14th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM)
Conference DateSEP 18-20, 2018
Conference PlaceKunming, PEOPLES R CHINA
Indexed ByCPCI
Document Type会议论文
Identifierhttp://ir.nssc.ac.cn/handle/122/6680
Collection空间技术部
Recommended Citation
GB/T 7714
Guo, Chen,Jiang, Yuanyuan. Optimization of Convolutional Neural Network Target Recognition Algorithm[C],2018:426-433.
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