The Comparison of deep learning recognition methods based on SAR image

被引：0
|
作者：
Zhai, Jia [1 ]
Zhu, Sha [2 ]
Chen, Feng [1 ]
Xie, Xiaodan [3 ]
Zhu, Yong [1 ]
Yin, Hongcheng [1 ]
机构：
[1] Sci & Technol Electromagnet Scattering Lab, Beijing, Peoples R China
[2] Beijing Inst Remote Sensing Informat, Beijing, Peoples R China
[3] Sci & Technol Opt Radiat Lab, Beijing, Peoples R China
来源：
CONFERENCE PROCEEDINGS OF 2019 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, COMMUNICATIONS AND COMPUTING (IEEE ICSPCC 2019) | 2019年
关键词：
Synthetic Aperture Radar (SAR); target recognition; principle component analysis; stacked autoencoder; convolutional neural network;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Aiming at the problem of Synthetic Aperture Radar (SAR) target recognition, a new deep learning method is proposed. The Stacked Auto Encoder (SAE) network and the convolutional Neural Network (CNN) have remarkable performance. Through the implementation and comparison of these methods, it is shown that the proposed deep learning recognition method has strong adaptability to different situations and has robustness to attitude angle, background and noise.
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页数：3
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