Knowledge Commons of Institute of Automation,CAS
Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition | |
Huang, Xiayuan1; Yang, Qiao2; Qiao, Hong1 | |
发表期刊 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
ISSN | 1545-598X |
2020 | |
卷号 | 18期号:0页码:1-5 |
通讯作者 | Huang, Xiayuan(xiayuan.huang@ia.ac.cn) |
摘要 | This letter proposes a lightweight two-stream convolutional neural network (CNN) for synthetic aperture radar (SAR) target recognition. Specifically, the two-stream CNN first extracts low-level features by three alternating convolution layers and max-pooling layers. Then two streams are followed to extract local and global features. One stream uses global maximum pooling to extract local features with the greatest response; the other uses large-stride convolution kernels to extract global features. Finally, the two streams are combined for target recognition. Therefore, the two-stream CNN can learn rich multilevel features to achieve high recognition accuracy for SAR target recognition. Moreover, compared to other popular CNNs, the two-stream CNN is very lightweight. The experimental results on the moving and stationary target acquisition and recognition (MSTAR) data set demonstrate that the proposed method not only can improve the recognition accuracy but also reduce the number of parameters of the model dramatically. |
关键词 | Lightweight synthetic aperture radar (SAR) target recognition two-stream convolutional neural network (CNN) |
DOI | 10.1109/LGRS.2020.2983718 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[61802408] ; National Natural Science Foundation of China[91648205] ; National Natural Science Foundation of China[61627808] ; National Key Research and Development Program of China[2017YFB1300200] ; National Key Research and Development Program of China[2017YFB1300203] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32000000] |
项目资助者 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Science |
WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000633394400021 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40565 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Huang, Xiayuan |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Science and Technology |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Huang, Xiayuan,Yang, Qiao,Qiao, Hong. Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2020,18(0):1-5. |
APA | Huang, Xiayuan,Yang, Qiao,&Qiao, Hong.(2020).Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,18(0),1-5. |
MLA | Huang, Xiayuan,et al."Lightweight Two-Stream Convolutional Neural Network for SAR Target Recognition".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 18.0(2020):1-5. |
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