RDAnet: A Deep Learning Based Approach for Synthetic Aperture Radar Image Formation
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Synthetic Aperture Radar (SAR) imaging systems operate by emitting radar signals from a moving object, such as a satellite, towards the target of interest. Reflected radar echoes are received and later used by image formation algorithms to form a SAR image. There is great interest in using SAR images in computer vision tasks such as automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we show that deep learning can be used to train a neural network able to form SAR images from echo data. Results show that our neural network, RDAnet, can form SAR images comparable to images formed using a traditional algorithm. This approach opens the possibility to end-to-end SAR applications where image formation and image processing are integrated into a single task. We believe that this work is the first demonstration of deep learning based SAR image formation using real data.
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