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. 2024 Jan 20;24(2):670.
doi: 10.3390/s24020670.

SAR Image Generation Method Using DH-GAN for Automatic Target Recognition

Snyoll Oghim  1 , Youngjae Kim  1 , Hyochoong Bang  1 , Deoksu Lim  2 , Junyoung Ko  2
Affiliations

SAR Image Generation Method Using DH-GAN for Automatic Target Recognition

Snyoll Oghim et al. Sensors (Basel). .

Abstract

In recent years, target recognition technology for synthetic aperture radar (SAR) images has witnessed significant advancements, particularly with the development of convolutional neural networks (CNNs). However, acquiring SAR images requires significant resources, both in terms of time and cost. Moreover, due to the inherent properties of radar sensors, SAR images are often marred by speckle noise, a form of high-frequency noise. To address this issue, we introduce a Generative Adversarial Network (GAN) with a dual discriminator and high-frequency pass filter, named DH-GAN, specifically designed for generating simulated images. DH-GAN produces images that emulate the high-frequency characteristics of real SAR images. Through power spectral density (PSD) analysis and experiments, we demonstrate the validity of the DH-GAN approach. The experimental results show that not only do the SAR image generated using DH-GAN closely resemble the high-frequency component of real SAR images, but the proficiency of CNNs in target recognition, when trained with these simulated images, is also notably enhanced.

Keywords: automatic target recognition; convolutional neural networks; generative adversarial networks; synthetic aperture radar.

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Conflict of interest statement

Authors Deoksu Lim and Junyoung Ko were employed by the company Hanwha Systems. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Basic structure of a GAN.
Figure 2
Figure 2
The structure of DH-GAN.
Figure 3
Figure 3
The network structure of the generator.
Figure 4
Figure 4
The network structure of the two discriminators.
Figure 5
Figure 5
The result of DH-GAN, NSGAN, and LSGAN at epoch 10.
Figure 6
Figure 6
Power spectrum density analysis for simulated datasets.
Figure 7
Figure 7
The training loss graph.
Figure 8
Figure 8
The recognition rate graph.
See this image and copyright information in PMC

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