PeaceGAN: A GAN-Based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier

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Although generative adversarial networks (GANs) are successfully applied to diverse fields, training GANs on synthetic aperture radar (SAR) data is a challenging task due to speckle noise. On the one hand, in a learning perspective of human perception, it is natural to learn a task by using information from multiple sources. However, in the previous GAN works on SAR image generation, information on target classes has only been used. Due to the backscattering characteristics of SAR signals, the structures of SAR images are strongly dependent on their pose angles. Nevertheless, the pose angle information has not been incorporated into GAN models for SAR images. In this paper, we propose a novel GAN-based multi-task learning (MTL) method for SAR target image generation, called PeaceGAN, that has two additional structures, a pose estimator and an auxiliary classifier, at the side of its discriminator in order to effectively combine the pose and class information via MTL. Extensive experiments showed that the proposed MTL framework can help the PeaceGAN's generator effectively learn the distributions of SAR images so that it can better generate the SAR target images more faithfully at intended pose angles for desired target classes in comparison with the recent state-of-the-art methods.</p>
Publisher
MDPI
Issue Date
2021-10
Language
English
Article Type
Article
Citation

REMOTE SENSING, v.13, pp.3939.1 - 3939.25

ISSN
2072-4292
DOI
10.3390/rs13193939
URI
http://hdl.handle.net/10203/288597
Appears in Collection
EE-Journal Papers(저널논문)
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