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
相关论文
共 50 条
  • [31] SAR-PeGA: A Generation Method of Adversarial Examples for SAR Image Target Recognition Network
    Xia, Weijie
    Liu, Zhe
    Li, Yi
    IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 2023, 59 (02) : 1910 - 1920
  • [32] SAR image recognition based on improved R-FCN
    Zhou X.
    Zhang Z.
    Lu Y.
    Wang Q.
    Wang K.
    Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2022, 44 (04): : 1202 - 1209
  • [33] Deep Learning for Target recognition from SAR images
    El Housseini, Ali
    Toumi, Abdelmalek
    Khenchaf, Ali
    2017 SEMINAR ON DETECTION SYSTEMS ARCHITECTURES AND TECHNOLOGIES (DAT), 2017,
  • [34] SAR-BagNet: An Ante-hoc Interpretable Recognition Model Based on Deep Network for SAR Image
    Li, Peng
    Feng, Cunqian
    Hu, Xiaowei
    Tang, Zixiang
    REMOTE SENSING, 2022, 14 (09)
  • [35] Impact of SAR Image Quantization Method on Target Recognition With Neural Networks
    Li, Kangwei
    Wang, Di
    An, Daoxiang
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 308 - 320
  • [36] SAR Target Classification Based on Radar Image Luminance Analysis by Deep Learning
    Zhu, Hongliang
    Wang, Weiye
    Leung, Rocky
    IEEE SENSORS LETTERS, 2020, 4 (03)
  • [37] Shearlet Transform-Based Novel Method for Multimodality Medical Image Fusion Using Deep Learning
    Mergin, Ancy
    Premi, M. S. Godwin
    INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS, 2023, 22 (01)
  • [38] An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
    Yuan, Ye
    Wu, Yanxia
    Fu, Yan
    Wu, Yulei
    Zhang, Lidan
    Jiang, Yan
    REMOTE SENSING, 2021, 13 (18)
  • [39] A target recognition method based on feature level data fusion
    Chen, WJ
    Dou, LH
    Chen, J
    PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4, 2002, : 2094 - 2098
  • [40] Target Recognition Based on Fuzzy Dempster Data Fusion Method
    Deng, Yong
    Su, Xiaoyan
    Wang, Dong
    Li, Qi
    DEFENCE SCIENCE JOURNAL, 2010, 60 (05) : 525 - 530
12345