SAR target recognition method of MSTAR data set based on multi-feature fusion

被引:2
|
作者
Shi, Ji [1 ]
机构
[1] China Aerosp Sci & Ind Corp, Beijing Inst Remote Sensing Equipment, Res Inst 2, Beijing, Peoples R China
来源
2022 INTERNATIONAL CONFERENCE ON BIG DATA, INFORMATION AND COMPUTER NETWORK (BDICN 2022) | 2022年
关键词
component; synthetic aperture radar; target recognition; multi-feature fusion; feature extraction;
D O I
10.1109/BDICN55575.2022.00120
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
To solve the problem of low recognition rate of synthetic aperture radar (SAR) target based on feature recognition, a target recognition method of SAR image based on multi-feature fusion is proposed, which combines Hu moment, Harris corner point and Gabor feature. The three kinds of features describe the target's geometric shape feature, corner feature and image texture feature respectively, which can improve the accuracy of SAR target recognition from the aspect of feature extraction. Based on the MSTAR data set, the experiment is carried out under standard and extended operating conditions. The results show that the proposed method can effectively overcome the deficiency of insufficient single feature description information and improve the SAR target recognition rate to a certain extent.
引用
收藏
页码:626 / 632
页数:7
相关论文
共 50 条
  • [41] Human Action Recognition Based on Skeleton Information and Multi-Feature Fusion
    Wang, Li
    Su, Bo
    Liu, Qunpo
    Gao, Ruxin
    Zhang, Jianjun
    Wang, Guodong
    ELECTRONICS, 2023, 12 (17)
  • [42] Research on plant leaf recognition method based on multi-feature fusion in different partition blocks
    Lv, Zhimin
    Zhang, Zhibin
    DIGITAL SIGNAL PROCESSING, 2023, 134
  • [43] Optical and SAR images Combined Mangrove Index based on multi-feature fusion
    Huang, Ke
    Yang, Gang
    Yuan, Yi
    Sun, Weiwei
    Meng, Xiangchao
    Ge, Yong
    SCIENCE OF REMOTE SENSING, 2022, 5
  • [44] Research on Radar Intelligent Recognition System of Space Targets Based on Multi-feature Fusion
    Lin, Xinghan
    2024 3RD INTERNATIONAL CONFERENCE ON ROBOTICS, ARTIFICIAL INTELLIGENCE AND INTELLIGENT CONTROL, RAIIC 2024, 2024, : 250 - 253
  • [45] Unknown Traffic Recognition Based on Multi-Feature Fusion and Incremental Learning
    Liu, Junyi
    Wang, Jiarong
    Yan, Tian
    Qi, Fazhi
    Chen, Gang
    APPLIED SCIENCES-BASEL, 2023, 13 (13):
  • [46] A GLASS IMAGE CLASSIFICATION METHOD BASED ON MULTI-FEATURE FUSION
    Zhang, Liang
    Wen, Jing
    Xu, Sheng-Zhou
    Xing, Hao-Yang
    Zhu, Yu
    Chen, Heng-Xin
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON WAVELET ANALYSIS AND PATTERN RECOGNITION (ICWAPR), 2016, : 7 - 11
  • [47] Automatic parts selection method based on multi-feature fusion
    Chen H.
    Luo H.
    Hui B.
    Chang Z.
    Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 2021, 50 (08):
  • [48] Malicious URL Recognition Based on Multi-feature Fusion and Machine Learning
    Ma, Changyou
    Wu, Aimin
    Ma, Wenzhuo
    Chen, Ke
    Liu, Yun
    Liang, Xiaoning
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 3014 - 3019
  • [49] An Image Patch Matching Method Based on Multi-feature Fusion
    Yu, Xiangru
    Guo, Yukun
    Li, Jinping
    Cai, Fudong
    2017 10TH INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, BIOMEDICAL ENGINEERING AND INFORMATICS (CISP-BMEI), 2017,
  • [50] Multi-Layer Model Based on Multi-Scale and Multi-Feature Fusion for SAR Images
    Zhai, Aobo
    Wen, Xianbin
    Xu, Haixia
    Yuan, Liming
    Meng, Qingxia
    REMOTE SENSING, 2017, 9 (10)