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

Fei Gao, Zhenyu Yue, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
40 Downloads (Pure)


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.

Original languageEnglish
Article number3105053
Pages (from-to)1-8
Number of pages8
JournalComputational Intelligence and Neuroscience
Publication statusPublished - 1 Oct 2017


  • convolutional neural networks
  • synthetic aperture radar
  • machine learning
  • image recognition


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