학술논문

Globally-scalable Automated Target Recognition (GATR)
Document Type
Conference
Source
2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR) Applied Imagery Pattern Recognition Workshop (AIPR), 2019 IEEE. :1-7 Oct, 2019
Subject
Aerospace
Bioengineering
Computing and Processing
Geoscience
Photonics and Electrooptics
Robotics and Control Systems
Signal Processing and Analysis
Artificial intelligence (AI)
automatic target recognition (ATR)
classification
computer vision
deep learning
image interpretation
machine learning (ML)
neural networks
Language
ISSN
2332-5615
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 km 2 /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 Maxar’s 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.