Deep learning model from Lockheed Martin tackles satellite image analysis

BETHESDA, MARYLAND. A satellite imagery recognition system designed by Lockheed Martin engineers uses open-source deep learning libraries to quickly identify and classify objects or targets in large areas across the world. Company officials say the tool could potentially saving image analysts many man hours categorizing and labeling items within an image.

The model, Global Automated Target Recognition (GATR), runs in the cloud, using Maxar Technologies’ Geospatial Big Data platform (GBDX) to access Maxar’s 100 petabyte satellite imagery library and millions of curated data labels across dozens of categories that expedite the training of deep learning algorithms. Fast GPUs enable GATR to scan a large area very quickly, while deep learning methods automate object recognition and reduce the need for extensive algorithm training.

The tool teaches itself what the identifying characteristics of an object area or target, for example, learning how to distinguish between a cargo plane and a military transport jet. The system then scales quickly to scan large areas, such as entire countries. GATR uses common deep learning techniques found in the commercial sector and can identify airplanes, ships,, buildings, seaports, etc.

“There’s more commercial satellite data than ever available today, and up until now, identifying objects has been a largely manual process,” says Maria Demaree, vice president and general manager of Lockheed Martin Space Mission Solutions. “Artificial intelligence models like GATR keep analysts in control while letting them focus on higher-level tasks. GATR has a high accuracy rate, well over 90% on the models we’ve tested so far. It only took two hours to search the entire state of Pennsylvania for fracking sites – that’s 120,000 square kilometers."

“I’m not an expert on what oil production sites are, and I don’t have to be,” says Mark Pritt, senior fellow at Lockheed Martin and principle investigator for GATR. “This system teaches itself the defining characteristics of an object, saving valuable time training an algorithm and ultimately letting an image analyst focus more on their mission.”

GATR builds on research Pritt’s team devloped during a Intelligence Advanced Research Projects Activity (IARPA) challenge, called the “Functional Map of the World.”