Globally-scalable Automated Target Recognition (GATR)

TL;DR

GATR is a scalable, GPU-accelerated deep learning system for real-time satellite imagery analysis, capable of detecting various objects across large geographic areas with high recall rates.

Contribution

The paper introduces GATR, a modular cloud-based system that enables fast, scalable, and accurate object detection in satellite imagery using deep learning models.

Findings

Processes over 16 sq km/sec on a single GPU

Achieves over 90% recall in unseen regions

Extensible to new targets and imagery types

Abstract

GATR (Globally-scalable Automated Target Recognition) is a Lockheed Martin software system for real-time object detection and classification in satellite imagery on a worldwide basis. GATR uses GPU-accelerated deep learning software to quickly search large geographic regions. On a single GPU it processes imagery at a rate of over 16 square km/sec (or more than 10 Mpixels/sec), and it requires only two hours to search the entire state of Pennsylvania for gas fracking wells. The search time scales linearly with the geographic area, and the processing rate scales linearly with the number of GPUs. GATR has a modular, cloud-based architecture that uses the Maxar GBDX platform and provides an ATR analytic as a service. Applications include broad area search, watch boxes for monitoring ports and airfields, and site characterization. ATR is performed by deep learning models including RetinaNet and Faster R-CNN. Results are presented for the detection of aircraft and fracking wells and show that the recalls exceed 90% even in geographic regions never seen before. GATR is extensible to new targets, such as cars and ships, and it also handles radar and infrared imagery.