Research on synthetic aperture radar recognition method based on deep learning

Zhendong Hua; Haifeng Wang; Haoxin Wang

doi:10.1117/12.3051698

18 November 2024 Research on synthetic aperture radar recognition method based on deep learning

Zhendong Hua, Haifeng Wang, Haoxin Wang

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134031O (2024) https://doi.org/10.1117/12.3051698
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

Target recognition in SAR images is a key issue in remote sensing image processing, which is widely used in many fields. Traditional recognition methods face many challenges due to the complexity of SAR images. In this paper, we propose an improved method based on GoogLeNet and improved YOLOV5 algorithm to cope with the limitations of MSTAR dataset. In this paper, GoogLeNet is introduced to improve the performance of target recognition in complex backgrounds, and its unique module improves the efficiency while reducing the parameters, which is especially suitable for handling small targets and complex textures in SAR images. Meanwhile, YOLOV5 is improved to enhance the detection accuracy and reduce the computational complexity. The effectiveness and superiority of the method is verified through experiments on the MSTAR dataset.

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

Citation Download Citation

Zhendong Hua, Haifeng Wang, and Haoxin Wang "Research on synthetic aperture radar recognition method based on deep learning", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134031O (18 November 2024); https://doi.org/10.1117/12.3051698

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