A Deep Learning Fusion Recognition Method Based On SAR Image Data

被引：16
|
作者：
Zhai Jia [1 ]
Dong Guangchang [2 ]
Chen Feng [2 ]
Xie Xiaodan [2 ]
Qi Chengming [3 ]
Li Lin [4 ]
机构：
[1] Sci & Technol Electromagnet Scattering Lab, Beijing 100000, Peoples R China
[2] Sci & Technol Opt Radiat Lab, Beijing 100000, Peoples R China
[3] Beijing Union Univ, Beijing 100000, Peoples R China
[4] Fourth Acad China Aerosp Sience & Ind Corp, Beijing 100000, Peoples R China
来源：
2018 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS | 2019年 / 147卷
关键词：
synthetic aperture radar (SAR); target recognition; principle component analysis(PCA); stacked autoencoder (SAE); convolutional neural network(CNN);
D O I：
10.1016/j.procs.2019.01.229
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In view of the research status and existing problems of synthetic aperture radar (SAR) target recognition, a new method of deep learning fusion recognition is proposed. Firstly, the 1-D features extracted with principle component analysis(PCA) are used as the input of the stacked autoencoder(SAE) network to extract deep features, which achieves target recognition based on 1-D PCA feature data. Then, the SAR target images are used as the input of convolutional neural network(CNN) to extract deep features, which achieves target recognition based on 2-D SAR image feature data. Finally, a deep learning recognition algorithm of decision-level and feature-level fusion is proposed for the different kinds of SAR image feature data. The experiment analysis shows that the proposed method of deep learning fusion recognition in this paper is adaptive and robust to the attitude angle, background and noise. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码：533 / 541
页数：9
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