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Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

Linshan Shen1, Ye Tian1,*, Liguo Zhang1,2, Guisheng Yin1, Tong Shuai3, Shuo Liang3, Zhuofei Wu4

1 Harbin Engineering University, College of Computer Science and Technology, Harbin, 150001, China
2 Xidian University, Xi’an, 710000, China
3 The 54th Research Institute of CETC, Shijiazhuang, 050000, China
4 University of Bologna, Bologna, 40100, Italy

* Corresponding Author: Ye Tian. Email: email

Computers, Materials & Continua 2022, 73(1), 465-476.


The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing. However, the existing semi-supervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution, and its performance is mainly due to the two being in the same distribution state. When there is out-of-class data in unlabeled data, its performance will be affected. In practical applications, it is difficult to ensure that unlabeled data does not contain out-of-category data, especially in the field of Synthetic Aperture Radar (SAR) image recognition. In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model, this paper proposes a semi-supervised learning method of threshold filtering. In the training process, through the two selections of data by the model, unlabeled data outside the category is filtered out to optimize the performance of the model. Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and compared with existing several state-of-the-art semi-supervised classification approaches, the superiority of our method was confirmed, especially when the unlabeled data contained a large amount of out-of-category data.


Cite This Article

APA Style
Shen, L., Tian, Y., Zhang, L., Yin, G., Shuai, T. et al. (2022). Threshold filtering semi-supervised learning method for SAR target recognition. Computers, Materials & Continua, 73(1), 465-476.
Vancouver Style
Shen L, Tian Y, Zhang L, Yin G, Shuai T, Liang S, et al. Threshold filtering semi-supervised learning method for SAR target recognition. Comput Mater Contin. 2022;73(1):465-476
IEEE Style
L. Shen et al., "Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition," Comput. Mater. Contin., vol. 73, no. 1, pp. 465-476. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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