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An Azimuth-Continuously Controllable SAR Image Generation Algorithm Based on GAN.

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Title: An Azimuth-Continuously Controllable SAR Image Generation Algorithm Based on GAN.
Authors: Cui, Yongjie1 (AUTHOR), Liu, Zhiqu1,2 (AUTHOR) lzq@dasa.net.cn, Ruan, Linian1,3 (AUTHOR), Sheng, Bowen2,3,4 (AUTHOR), Wang, Ning1,2,3 (AUTHOR), Xiao, Xiulai1,2 (AUTHOR), Bian, Xiaolin1,3,4 (AUTHOR)
Source: Remote Sensing. Nov2025, Vol. 17 Issue 22, p3763. 19p.
Subjects: Synthetic aperture radar, Azimuth, Generative adversarial networks, Data augmentation, Deep learning
Abstract: Highlights: What are the main findings? An enhanced GAN for SAR image generation, called Azimuth-Continuously Controllable Generative Adversarial Network (ACC-GAN), is proposed to enable precise interpolation between arbitrary azimuth angles. ACC-GAN improves the flexibility on angular generation, while maintaining the physical fidelity and angular accuracy of SAR images. What are the implications of the main findings? Since the multi-view SAR images are very scarce and the azimuth characteristics are particularly important for SAR target recognition, the proposed ACC-GAN can provide flexible and accessible augmentation of multi-view SAR images. The performance of deep learning models largely depends on the scale and quality of training data. However, acquiring sufficient, high-quality samples for specific observation scenarios is often challenging due to high acquisition costs. Unlike optical imagery, synthetic aperture radar (SAR) target images exhibit strong nonlinear scattering variations with changing azimuth angles, making conventional data augmentation methods such as cropping or rotation ineffective. To tackle these challenges, this paper introduces an Azimuth-Continuously Controllable Generative Adversarial Network (ACC-GAN), which incorporates a continuous azimuth conditional variable to achieve precise azimuth-controllable target generation from dual-input SAR images. Our key contributions are threefold: (1) a continuous azimuth control mechanism that enables precise interpolation between arbitrary azimuth angles; (2) a dual-discriminator framework combining similarity and azimuth supervision to ensure both visual realism and angular accuracy; and (3) conditional batch normalization integrated with adaptive feature fusion to maintain scattering consistency. Experiments on the MSTAR dataset demonstrate that ACC-GAN effectively captures nonlinear azimuth-dependent transformations, generating high-quality images that improve downstream classification accuracy and validate its practical value for SAR data augmentation. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An Azimuth-Continuously Controllable SAR Image Generation Algorithm Based on GAN.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Nov2025, Vol. 17 Issue 22, p3763. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Azimuth%22">Azimuth</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+adversarial+networks%22">Generative adversarial networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: Highlights: What are the main findings? An enhanced GAN for SAR image generation, called Azimuth-Continuously Controllable Generative Adversarial Network (ACC-GAN), is proposed to enable precise interpolation between arbitrary azimuth angles. ACC-GAN improves the flexibility on angular generation, while maintaining the physical fidelity and angular accuracy of SAR images. What are the implications of the main findings? Since the multi-view SAR images are very scarce and the azimuth characteristics are particularly important for SAR target recognition, the proposed ACC-GAN can provide flexible and accessible augmentation of multi-view SAR images. The performance of deep learning models largely depends on the scale and quality of training data. However, acquiring sufficient, high-quality samples for specific observation scenarios is often challenging due to high acquisition costs. Unlike optical imagery, synthetic aperture radar (SAR) target images exhibit strong nonlinear scattering variations with changing azimuth angles, making conventional data augmentation methods such as cropping or rotation ineffective. To tackle these challenges, this paper introduces an Azimuth-Continuously Controllable Generative Adversarial Network (ACC-GAN), which incorporates a continuous azimuth conditional variable to achieve precise azimuth-controllable target generation from dual-input SAR images. Our key contributions are threefold: (1) a continuous azimuth control mechanism that enables precise interpolation between arbitrary azimuth angles; (2) a dual-discriminator framework combining similarity and azimuth supervision to ensure both visual realism and angular accuracy; and (3) conditional batch normalization integrated with adaptive feature fusion to maintain scattering consistency. Experiments on the MSTAR dataset demonstrate that ACC-GAN effectively captures nonlinear azimuth-dependent transformations, generating high-quality images that improve downstream classification accuracy and validate its practical value for SAR data augmentation. [ABSTRACT FROM AUTHOR]
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  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2025
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