Speaker: Omar DeGuchy
Title: Machine Learning For Applications in Synthetic Aperture Radar
In the world of remote sensing, machine learning algorithms have shown promise in solving a variety of problems associated with a variety of imaging modalities. In particular, the use of neural networks in conjunction with Synthetic Aperture Radar (SAR) images have been shown to be effective for automatic target recognition. This talk focuses on two different applications of neural networks with SAR data. In the first application, we address the lack of SAR data used for training target recognition models by augmenting the quality of synthetic SAR data using a modified generative adversarial network. In the second application, we propose a method to solve the forward and inverse scattering problems for SAR. The method takes advantage of a simplified neural network where the goal is to learn the sensing matrix that maps reflectivities to SAR measurements. We also propose a similar method to learn an approximate inverse used to recover reflectivites from SAR measurements.