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Deep Feature Bayesian Classifier for SAR Target Recognition with Small Training Set

Liguo Zhang1,2, Zilin Tian1, Yan Zhang3,*, Tong Shuai4, Shuo Liang4, Zhuofei Wu5

1 College of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China
2 Xidian University, Xi’an, 710000, China
3 China Industrial Control Systems Cyber Emergency Response Team, Beijing, 100040, China
4 The 54th Research Institute of CETC, Shijiazhuang, 130100, China
5 University of Bologna, Bologna, 00013, Italy

* Corresponding Author: Yan Zhang. Email: email

Journal of New Media 2022, 4(2), 59-71.


In recent years, deep learning algorithms have been popular in recognizing targets in synthetic aperture radar (SAR) images. However, due to the problem of overfitting, the performance of these models tends to worsen when just a small number of training data are available. In order to solve the problems of overfitting and an unsatisfied performance of the network model in the small sample remote sensing image target recognition, in this paper, we uses a deep residual network to autonomously acquire image features and proposes the Deep Feature Bayesian Classifier model (RBnet) for SAR image target recognition. In the RBnet, a Bayesian classifier is used to improve the effect of SAR image target recognition and improve the accuracy when the training data is limited. The experimental results on MSTAR dataset show that the RBnet can fully exploit effective information in limited samples and recognize the target of the SAR images more accurately. Compared with other state-of-the-art methods, our method offers significant recognition accuracy improvements under limited training data. Noted that the RBnet is moderately difficult to implement and has the value of popularization and application in engineering application scenarios in the field of small-sample remote sensing target recognition and recognition.


Cite This Article

L. Zhang, Z. Tian, Y. Zhang, T. Shuai, S. Liang et al., "Deep feature bayesian classifier for sar target recognition with small training set," Journal of New Media, vol. 4, no.2, pp. 59–71, 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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