With the advantage of working in all weathers and all days, synthetic aperture radar (SAR) imaging systems have a great application value. As an efficient image generation and recognition model, generative adversarial networks (GANs) have been applied to SAR image analysis and achieved promising performance. However, the cost of labeling a large number of SAR images limits the performance of the developed approaches and aggravates the mode collapsing problem. This paper presents a novel approach namely Integrated GANs (I-GAN), which consists of a conditional GANs, an unconditional GANs and a classifier, to achieve semi-supervised generation and recognition simultaneously. The unconditional GANs assist the conditional GANs to increase the diversity of the generated images. A co-training method for the conditional GANs and the classifier is proposed to enrich the training samples. Since our model is capable of representing training images with rich characteristics, the classifier can achieve better recognition accuracy. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset proves that our method achieves better results in accuracy when labeled samples are insufficient, compared against other state-of-the-art techniques.
Bibliographical noteFunding Information:
This work was supported by the National Natural Science Foundation of China under Grant 61771027, Grant 61071139, Grant 61471019, Grant 61501011, and Grant 61171122. The work of A. Hussain was supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/M026981/1. The work of H. Zhou was supported in part by the U.K. EPSRC under Grant EP/N011074/1, in part by the Royal Society—Newton Advanced Fellowship under Grant NA160342, and in part by the European Union’s Horizon 2020 Research and Innovation Program under the Marie–Sklodowska–Curie Grant Agreement 720325.
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- Generative adversarial networks (GANs)
- Semi-supervised learning
- Synthetic aperture radar (SAR)
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)