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Computational Intelligence and Neuroscience
Volume 2017, Article ID 3105053, 8 pages
Research Article

A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

1Electronic Information Engineering, Beihang University, Beijing 100191, China
2Space Mechatronic Systems Technology Laboratory, Department of Design, Manufacture and Engineering Management, University of Strathclyde, Glasgow G1 1XJ, UK
3School of Electronics, Electrical Engineering and Computer Science, Queen’s University, Belfast BT7 1NN, UK

Correspondence should be addressed to Jun Wang; nc.ude.aaub@302jgnaw

Received 5 May 2017; Revised 9 August 2017; Accepted 23 August 2017; Published 1 October 2017

Academic Editor: George A. Papakostas

Copyright © 2017 Fei Gao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervised method is developed by adding a new regularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples.