A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition.pdf (1.24 MB)

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

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posted on 15.02.2018 by Fei Gao, Zhenyu Yue, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou
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.
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This work was supported by the National Natural Science Foundation of China (61771027; 61071139; 61471019; 61171122; 61501011; 61671035), the Aeronautical Science Foundation of China (20142051022), and the Preresearch Project (9140A07040515HK01009). Dr. E. Yang is supported in part by the RSE-NNSFC Joint Project (2017–2019) (6161101383) with China University of Petroleum (Huadong). Dr. H. Zhou is supported by UK EPSRC under Grants EP/N508664/1, EP/R007187/1, and EP/N011074/1 and Royal Society-Newton Advanced Fellowship under Grant NA160342.

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Citation

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

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VoR (Version of Record)

Published in

Computational Intelligence and Neuroscience

Publisher

Hindawi Publishing Corporation

issn

1687-5265

eissn

1687-5273

Acceptance date

23/08/2017

Copyright date

2017

Available date

15/02/2018

Publisher version

https://www.hindawi.com/journals/cin/2017/3105053/

Language

en

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