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Space domain awareness has gained traction in recent years, encompassing the charting and cataloging of space objects, anticipating orbital paths, and keeping track of re-entering objects. Radar techniques can be used to monitor the fast-growing population of satellites, but so far this is mainly used for detection and tracking. For the characterization of a satellite’s capabilities, more detailed information, such as inverse synthetic-aperture radar (ISAR) imaging, is needed. Deep learning has become the preferred method for automated image analysis in various applications. Development of deep learning models typically requires large amounts of training data, but recent studies have shown that synthetic data can be used as an alternative in combination with domain adaption techniques to overcome the domain gap between synthetic and real data.
In this study, we present a deep learning-based methodology for automated segmentation of the satellite’s bus and solar panels in ISAR images. We first train a segmentation model using thousands of fast simulated ISAR images and then we finetune the model using a domain adaptation technique that only requires a few samples of the target domain. As a proof of concept, we use a small set of high fidelity simulated ISAR images closely resembling real ISAR images as the target domain. Our proof of concept demonstrates that this domain adaptation technique effectively bridges the domain gap between the training and target radar image domains. Consequently, fast simulated (low fidelity) synthetic datasets are proven to be invaluable for training segmentation models for ISAR images, especially when combined with domain adaptation techniques.
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