  Although Generative Adversarial Networks (GANs) are successfully applied to
diverse fields, training GANs on synthetic aperture radar (SAR) data is a
challenging task mostly due to speckle noise. On the one hands, in a learning
perspective of human's perception, it is natural to learn a task by using
various information from multiple sources. However, in the previous GAN works
on SAR target image generation, the information on target classes has only been
used. Due to the backscattering characteristics of SAR image signals, the
shapes and structures of SAR target images are strongly dependent on their pose
angles. Nevertheless, the pose angle information has not been incorporated into
such generative models for SAR target images. In this paper, we firstly propose
a novel GAN-based multi-task learning (MTL) method for SAR target image
generation, called PeaceGAN that uses both pose angle and target class
information, which makes it possible to produce SAR target images of desired
target classes at intended pose angles. For this, the PeaceGAN has two
additional structures, a pose estimator and an auxiliary classifier, at the
side of its discriminator to combine the pose and class information more
efficiently. In addition, the PeaceGAN is jointly learned in an end-to-end
manner as MTL with both pose angle and target class information, thus enhancing
the diversity and quality of generated SAR target images The extensive
experiments show that taking an advantage of both pose angle and target class
learning by the proposed pose estimator and auxiliary classifier can help the
PeaceGAN's generator effectively learn the distributions of SAR target images
in the MTL framework, so that it can better generate the SAR target images more
flexibly and faithfully at intended pose angles for desired target classes
compared to the recent state-of-the-art methods.
