ISAR Images Generation Via Generative Adversarial Networks

One of the challenges faced by current intelligent target recognition tasks is the lack of samples, especially in the Inverse Synthetic Aperture Radar (ISAR) images understanding. In this paper, we proposed an ISAR objects generative network to generate multi-aspect ISAR images. A simulated ISAR dat...

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Bibliographic Details
Main Authors: Zhou, Ruo-Yi, Yang, Zhi-Long, Wang, Feng
Format: Conference Proceeding
Language:English
Subjects:
Aircraft
Automatic Target Recognition (ATR)
Generative Adversarial Nets (GANs)
Generative adversarial networks
Geoscience and remote sensing
Image synthesis
Inverse Synthetic Aperture Radar (ISAR)
Ray tracing
Target recognition
Training
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Summary:One of the challenges faced by current intelligent target recognition tasks is the lack of samples, especially in the Inverse Synthetic Aperture Radar (ISAR) images understanding. In this paper, we proposed an ISAR objects generative network to generate multi-aspect ISAR images. A simulated ISAR dataset of six types of aircrafts is produced via, using bidirectional analytic ray tracing (BART) method. Then, the proposed generative network is trained with the simulated ISAR dataset. We evaluated the performance of the proposed network using structural similarity (SSIM). The experimental results show that the generated targets are very close to the real ISAR samples, and the SSIM between generated and real ISAR images of aircrafts is larger than 0.7.
ISSN:2153-7003
DOI:10.1109/IGARSS47720.2021.9553814