A novel active semisupervised convolutional neural network algorithm for SAR image recognition

Gao, Fei and Yue, Zhenyu and Wang, Jun and Sun, Jinping and Yang, Erfu and Zhou, Huiyu (2017) A novel active semisupervised convolutional neural network algorithm for SAR image recognition. Computational Intelligence and Neuroscience, 2017. pp. 1-8. 3105053. ISSN 1687-5273

[img] Text (Gao-etal-CIN2017-A-novel-active-semisupervised-convolutional-neural-network)
Gao_etal_CIN2017_A_novel_active_semisupervised_convolutional_neural_network.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (1MB)

    Abstract

    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.