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
  • [41] A Method of Fusion Recognition Based on the Characteristic of Target and Incomplete Data
    Li Jun-wu
    Yu Zhi-fu
    PROCEEDINGS OF 2012 IEEE 11TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) VOLS 1-3, 2012, : 1813 - 1816
  • [42] SAR Target Recognition Using Only Simulated Data for Training by Hierarchically Combining CNN and Image Similarity
    Zhang, Chen
    Wang, Yinghua
    Liu, Hongwei
    Sun, Yuanshuang
    Hu, Liping
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [43] A Satellite-Borne SAR Target Recognition Method Based on Supplementary Feature Fusion
    Liu Sikai
    Yang Jun
    2018 IEEE 3RD INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA), 2018, : 326 - 330
  • [44] Method for Image Quality Evaluation of Satellite-based SAR Data
    Arai, Kohei
    Mikamo, Michihiro
    Onishi, Shunsuke
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (07) : 119 - 126
  • [45] THE SEN1-2 DATASET FOR DEEP LEARNING IN SAR-OPTICAL DATA FUSION
    Schmitt, M.
    Hughes, L. H.
    Zhu, X. X.
    ISPRS TC I MID-TERM SYMPOSIUM INNOVATIVE SENSING - FROM SENSORS TO METHODS AND APPLICATIONS, 2018, 4-1 : 141 - 146
  • [46] SAR Target Recognition Using cGAN-Based SAR-to-Optical Image Translation
    Sun, Yuchuang
    Jiang, Wen
    Yang, Jiyao
    Li, Wangzhe
    REMOTE SENSING, 2022, 14 (08)
  • [47] Deep Network for SAR Target Recognition Based on Attribute Scattering Center Convolutional Kernel Modulation
    Yi L.
    Lan D.
    Ke’er Z.
    Yuang D.
    Journal of Radars, 2024, 13 (02) : 443 - 456
  • [48] Bidirectional Interaction Fusion Network Based on EC-Maps and SAR Images for SAR Target Recognition
    Hui, Xuemeng
    Liu, Zhunga
    Wang, Longfei
    Zhang, Zuowei
    Yao, Shun
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2025, 74
  • [49] Integrated Physically Interpretable Model for SAR Target Recognition: Unified Fusion of Electromagnetic and Deep Features
    Liao, Leiyao
    Hong, Zishuo
    Liu, Ziwei
    Zhang, Gengxin
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2025, 22
  • [50] A vibration recognition method based on deep learning and signal processing
    Cheng Z.-G.
    Liao W.-J.
    Chen X.-Y.
    Lu X.-Z.
    Gongcheng Lixue/Engineering Mechanics, 2021, 38 (04): : 230 - 246
12345