Data augmentation using style transfer in SAR automatic target classification

Xu Zhu; Hiroki Mori

doi:10.1117/12.2599071

12 September 2021 Data augmentation using style transfer in SAR automatic target classification

Xu Zhu, Hiroki Mori

Proceedings Volume 11870, Artificial Intelligence and Machine Learning in Defense Applications III; 118700E (2021) https://doi.org/10.1117/12.2599071
Event: SPIE Security + Defence, 2021, Online Only

Abstract

Artificial intelligence (AI)-based methods for automatic target detection have been a research hotspot in the field of millimeter-wave security. That is, using artificial intelligence to determine if the results of millimeter-wave imaging include dangerous items, and to communicate the results to security personnel. This will not only avoid the leakage of private information, but also reduce the workload of security personnel and improve the efficiency during the security process. Existing deep learning networks require a large number of training dataset to optimize the network parameters. However, there are few datasets in the field of millimeter-wave imaging. In addition, due to local legal restrictions, researchers often do not have access to a large number of dangerous goods samples for the training of millimeter-wave imaging, which greatly limits the performance and applications of automatic classification in millimeter-wave security. In this paper, a method is proposed which uses style transfer techniques to combine a small number of millimeter-wave images with a large number of optical images to generate a library of millimeter-wave-like images. Specifically, the style transfer method combines the style features of a millimeter-wave image with the content features of an optical image to generate a new image. By combining different style images and content images, a large number of new images can be generated. The above generated images are then used to train any deep network for classification. The performance of proposed method is compared with a conventional method of data augmentation. The comparison results show that the method proposed in this paper effectively improves the accuracy of automatic classification in SAR automatic target classification.

Conference Presentation

Citation Download Citation

Xu Zhu and Hiroki Mori "Data augmentation using style transfer in SAR automatic target classification", Proc. SPIE 11870, Artificial Intelligence and Machine Learning in Defense Applications III, 118700E (12 September 2021); https://doi.org/10.1117/12.2599071

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