SAR image data enhancement based on IF-SinConGAN

Ruizhe Mu; Yingmei Qin; Chunxiao Han; Yanqiu Che

doi:10.1117/12.3050094

8 November 2024 SAR image data enhancement based on IF-SinConGAN

Ruizhe Mu, Yingmei Qin, Chunxiao Han, Yanqiu Che

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134162O (2024) https://doi.org/10.1117/12.3050094
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

In order to solve the problem of insufficient samples of near-shore synthetic aperture radar data in ship detection, a ship synthetic aperture radar (SAR) image data augmentation model based on generative adversarial network was designed in this study. Specifically, this study combines image fusion and data enhancement to design an Image Fusion Concurrent-Single-Image-GAN model (IF-SinConGAN).This model first fuses offshore ship images with nearshore scenes, employing a dual-threshold sea-land segmentation method to seamlessly integrate offshore ships into nearshore water regions. These fused images are then used as input for training the ConSinGAN model. Compared to the original model, IF-SinConGAN significantly improves both the diversity and quality of generated SAR images.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ruizhe Mu, Yingmei Qin, Chunxiao Han, and Yanqiu Che "SAR image data enhancement based on IF-SinConGAN", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134162O (8 November 2024); https://doi.org/10.1117/12.3050094

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