Demonstration of a fully neural network based synthetic aperture radar processing pipeline for image formation and analysis

Andrew Rittenbach; John Paul Walters

doi:10.1117/12.2599955

12 September 2021 Demonstration of a fully neural network based synthetic aperture radar processing pipeline for image formation and analysis

Andrew Rittenbach, John Paul Walters

Author Affiliations +

Proceedings Volume 11858, Sensors, Systems, and Next-Generation Satellites XXV; 118580K (2021) https://doi.org/10.1117/12.2599955
Event: SPIE Remote Sensing, 2021, Online Only

Abstract

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 classification or automatic target recognition. Today, however, SAR applications consist of multiple operations: image formation followed by image processing. In this work, we train a deep neural network that performs both the image formation and image processing tasks, integrating the SAR processing pipeline. Results show that our integrated pipeline can output accurately classified SAR imagery with image quality comparable to those formed using a traditional algorithm, showing that fully neural network based SAR processing pipeline is feasible.

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Andrew Rittenbach and John Paul Walters "Demonstration of a fully neural network based synthetic aperture radar processing pipeline for image formation and analysis", Proc. SPIE 11858, Sensors, Systems, and Next-Generation Satellites XXV, 118580K (12 September 2021); https://doi.org/10.1117/12.2599955

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