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ETD Abstract Container

Abstract Header

Classification for synthetic aperture radar ship image of the ship targets under the few-shot condition

Zhang, Siqi
http://rave.ohiolink.edu/etdc/view?acc_num=osu1681123944293789

Abstract Details

2023, Master of Actuarial and Quantitative Risk Management, Ohio State University, Mathematics.
Deep learning algorithms have shown outstanding results for the categorization of synthetic aperture radar (SAR) images. But because there is a conflict between the deep learning techniques' broad parameter space and the scanty labeled samples of ship targets, most deep learning models are unable to provide satisfying outcomes under the few-shot scenario. Classification for SAR ship images often faces the few-shot condition because they are sensitive to system parameters, so that the amount of effective SAR images is very low. To this end, this article proposes a method based on graph neural network (GNN). An end-to-end classification model was trained by this method. Moreover, our method extracts multi-scale features of the targets to make classification effects better. The dropout layers are added into the graph convolution layer to reduce the overfitting in few-shot classification. Finally, our model is applied to the mixed dataset of the simulated and real data to verify effectiveness of our method. The experimental findings presented in this research show that our method outperforms other cutting-edge approaches in terms of categorization rate.
Ai Ni (Committee Member)
Chunsheng Ban (Advisor)
27 p.
Artificial Intelligence; Computer Science
graph neural network (GNN), synthetic aperture radar (SAR), few-shot learning, ship target

Recommended Citations

Citations

  • Zhang, S. (2023). Classification for synthetic aperture radar ship image of the ship targets under the few-shot condition [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681123944293789

    APA Style (7th edition)

  • Zhang, Siqi. Classification for synthetic aperture radar ship image of the ship targets under the few-shot condition. 2023. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1681123944293789.

    MLA Style (8th edition)

  • Zhang, Siqi. "Classification for synthetic aperture radar ship image of the ship targets under the few-shot condition." Master's thesis, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681123944293789

    Chicago Manual of Style (17th edition)

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© 2023, some rights reserved.Creative Commons License Classification for synthetic aperture radar ship image of the ship targets under the few-shot condition by Siqi Zhang is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.