Open Access

ARTICLE

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

by Liguo Zhang^1,2, Zilin Tian¹, Yan Zhang^3,*, 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:

Journal of New Media 2022, 4(2), 59-71. https://doi.org/10.32604/jnm.2022.029360

Received 01 March 2022; Accepted 11 April 2022; Issue published 13 June 2022

Abstract

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.

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Keywords

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