Dr. Xu Zhu Profile

Dr. Xu Zhu

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Proceedings Article | 12 September 2021 Presentation + Paper

Data augmentation using style transfer in SAR automatic target classification

Xu Zhu, Hiroki Mori

Proceedings Volume 11870, 118700E (2021) https://doi.org/10.1117/12.2599071

KEYWORDS: Image classification, Synthetic aperture radar, Image fusion, Data modeling, Library classification systems, Information security, Image processing, Extremely high frequency, Neural networks, Network security

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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.

Proceedings Article | 24 April 2020 Presentation + Paper

A novel approach to sparse synthesis for imaging

Xu Zhu, Mori Hiroki

Proceedings Volume 11393, 1139307 (2020) https://doi.org/10.1117/12.2557017

KEYWORDS: Synthetic aperture radar, Radar, Antennas, Imaging systems, Radar imaging, Reconstruction algorithms

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The recent surge in the application of millimeter-wave sensing for public security has been accompanied by deployments of whole-body scanners at airports and stations. Some exiting imaging apparatuses using a synthetic aperture which requires a large number of measurements to meet the requirement of half-wavelength spacing and thus put a stringent requirement on hardware design. In this paper, we proposed a novel sparse synthetic algorithm that applies a multistatic scheme to the coprime measurements. It replaces every monostatic radar by a pair of separated transmitter and receiver along with phase corrections. Due to the multiplexing of all transmitters and receivers, the multistatic scheme further reduces the number of measurements and the amount of data to about 0.3% of the standard SAR. The efficacy of the proposed method is demonstrated using simulations and experiments.

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