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

Jihyong Oh,Munchurl Kim

doi:10.3390/rs13193939

Jihyong Oh, Munchurl Kim

Open Access

https://doi.org/10.3390/rs13193939

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Abstract

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.

Highlights

Synthetic aperture radar (SAR) is commonly utilized for surveillance systems [1,2,3,4].Since SAR has a compelling characteristic of a penetration, SAR images can be obtained, regardless of any weather condition, whether the time is night or daytime or the weather is sunny or cloudy, unlike an optical remote sensing
The Moving and Stationary Target Acquisition and Recognition (MSTAR) public dataset was utilized for experiments of the SAR target image generation
Was gathered by the Sandia National Laboratory (SNL) SAR sensor platform that was supported by Defense Advanced Research Projects Agency (DARPA) and Air Force Research

Summary

Introduction

Synthetic aperture radar (SAR) is commonly utilized for surveillance systems [1,2,3,4].Since SAR has a compelling characteristic of a penetration, SAR images can be obtained, regardless of any weather condition, whether the time is night or daytime or the weather is sunny or cloudy, unlike an optical remote sensing. It is hard to train the CNNs for SAR-related tasks due to the lack of available SAR images that should be obtained by radar attached to air vehicles and labeled manually with considerable time consumption [8,14]. For this reason, there is a need for generative models that can generate abundant SAR data such as “Big Data” for diverse SAR tasks

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