Semi-supervised generative adversarial nets with multiple generators for SAR image recognition

Fei Gao*, Fei Ma, Jun Wang, Jinping Sun, Erfu Yang, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
7 Downloads (Pure)

Abstract

As an important model of deep learning, semi-supervised learning models are based on Generative Adversarial Nets (GANs) and have achieved a competitive performance on standard optical images. However, the training of GANs becomes unstable when they are applied to SAR images, which reduces the feature extraction capability of the discriminator in GANs. This paper presents a new semi-supervised GANs with Multiple generators and a classifier (MCGAN). This model improves the stability of training for SAR images by employing multiple generators. A multi-classifier is introduced to the new GANs to utilize the labeled images during the training of the GANs, which shares the low level layers with the discriminator. Then, the layers of the trained discriminator and the classifier construct the recognition network for SAR images after having been finely tuned using a small number of the labeled images. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) databases show that the proposed recognition network achieves a better and more stable recognition performance than several traditional semi-supervised methods as well as other GANs-based semi-supervised methods.

Original languageEnglish
Article number2706
JournalSensors (Switzerland)
Volume18
Issue number8
DOIs
Publication statusPublished - 17 Aug 2018

Bibliographical note

Funding Information:
Funding: This research was funded by the National Natural Science Foundation of China, grant number 61771027, 61071139, 61471019, 61501011, and 61171122. E. Yang is supported in part under the RSE-NNSFC Joint Project (2017-2019), grant number 6161101383 with China University of Petroleum (Huadong). H. Zhou is supported by UK EPSRC, grant number EP/N508664/1, EP/R007187/1 and EP/N011074/1, and Royal Society-Newton Advanced Fellowship, grant number NA160342.

Publisher Copyright:
© 2018 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

Keywords

  • Deep learning
  • Generative adversarial networks (GANs)
  • Semi-supervised recognition
  • Synthetic aperture radar (SAR)

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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