Target detecting deep neural network countermeasure technology for space-based remote sensing and its application to mobile radar

Yan Ouyang; Peiqi Deng; Binbin Shi; Ting Gao

doi:10.1117/12.2667710

10 February 2023 Target detecting deep neural network countermeasure technology for space-based remote sensing and its application to mobile radar

Yan Ouyang, Peiqi Deng, Binbin Shi, Ting Gao

Author Affiliations +

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125522D (2023) https://doi.org/10.1117/12.2667710
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Space-based remote sensing is an important way of detecting many types of land targets. For the purpose of taking cover, land targets have a strong demand for avoiding space-based remote sensing reconnaissance. In practice, space-based reconnaissance will produce huge data, which are unbearable for human-beings. Therefore, the data processing must rely on artificial intelligence technology such as deep neural network. Many previous works show that the existing intelligent target detection algorithm based on deep neural network will be affected by perturbations. Firstly, this paper establishes a target detection method based on the Faster RCNN framework, and then three types of disturbances methods are studied to help the mobile radar to counter the typical space-based artificial intelligence detection algorithm. The simulation results show that the three types of disturbances methods can fool the typical target detection technology based on deep neural network.

Citation Download Citation

Yan Ouyang, Peiqi Deng, Binbin Shi, and Ting Gao "Target detecting deep neural network countermeasure technology for space-based remote sensing and its application to mobile radar", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125522D (10 February 2023); https://doi.org/10.1117/12.2667710

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