SARCUT: contrastive learning for optical-SAR image translation with self-attention and relativistic discrimination

Yusen Zhang; Min Li; Wei Cai; Yao Gou; Shuaibing Shi

doi:10.1117/12.2669086

3 May 2023 SARCUT: contrastive learning for optical-SAR image translation with self-attention and relativistic discrimination

Yusen Zhang, Min Li, Wei Cai, Yao Gou, Shuaibing Shi

Author Affiliations +

Proceedings Volume 12644, International Workshop on Frontiers of Graphics and Image Processing (FGIP 2022); 126440B (2023) https://doi.org/10.1117/12.2669086
Event: International Workshop on Frontiers of Graphics and Image Processing (FGIP 2022), 2022, Beijing, China

Abstract

Focusing on how to obtain high-quality and sufficient synthetic aperture radar (SAR) data in deep learning, this paper proposed a new mothed named SARCUT (Self-Attention Relativistic Contrastive Learning for Unpaired Image-to-Image Translation) to translate optical images into SAR images. In order to improve the coordination of generated images and stabilize the training process, we constructed a generator with the self-attention mechanism and spectral normalization operation. Meanwhile, relativistic discrimination adversarial loss function was designed to accelerate the model convergence and improved the authenticity of the generated images. Experiments on open datasets with 6 image quantitative evaluation metrics showed our model can learn the deeper internal relations and main features between multiple source images. Compared with the classical methods, SARCUT has more advantages in establishing the real image domain mapping, both the quality and authenticity of the generated image are significantly improved.

Citation Download Citation

Yusen Zhang, Min Li, Wei Cai, Yao Gou, and Shuaibing Shi "SARCUT: contrastive learning for optical-SAR image translation with self-attention and relativistic discrimination", Proc. SPIE 12644, International Workshop on Frontiers of Graphics and Image Processing (FGIP 2022), 126440B (3 May 2023); https://doi.org/10.1117/12.2669086

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