EE Colloquium: Image Exploitation in Electro-Optical and Synthetic Aperture Radar Imagery

Abstract: The automated detection and classification of objects in imagery is an important topic for many applications in remote sensing. These can include the counting of cars and ships and the tracking of military vehicles for the defense and intelligence industry. Electro-optical (EO) and Synthetic Aperture Radar (SAR) imagery provide powerful and complimentary modalities. Deep Learning (DL) based solutions for detection and classification have emerged as the leading architectures. During this talk, SAR Automated Target Recognition (ATR) will be discussed using the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. In addition, Lockheed Martin’s Globally scalable ATR (GATR) system that is built on top of DigitalGlobe’s GBDX and Amazon Web Services (AWS) will be presented. This system currently exploits EO imagery but is developing SAR capabilities as well.

Biography: Ryan Soldin is a Senior Research Scientist at Lockheed Martin in Valley Forge, Pennsylvania. His interests lie in electro-optical and radar image processing and exploitation. Previously he worked at a machine learning startup and the Johns Hopkins Applied Physics Lab. Mr. Soldin holds M.S. degrees in engineering physics and computational mathematics from Embry-Riddle Aeronautical University and Johns Hopkins.

 

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Media Contact: Ram Narayanan

 
 

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The School of Electrical Engineering and Computer Science was created in the spring of 2015 to allow greater access to courses offered by both departments for undergraduate and graduate students in exciting collaborative research in fields.

We offer B.S. degrees in electrical engineering, computer science, computer engineering and data science and graduate degrees (master's degrees and Ph.D.'s) in electrical engineering and computer science and engineering. EECS focuses on the convergence of technologies and disciplines to meet today’s industrial demands.

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