Detecting the Presence of Vehicles and Equipment in SAR Imagery Using Image Texture Features

In this work, we present a methodology for monitoring man-made, construction-like activities in low-resolution SAR imagery. Our source of data is the European Space Agency Sentinel-l satellite which provides global coverage at a 12-day revisit rate. Despite limitations in resolution, our methodology enables us to monitor activity levels (i.e. presence of vehicles, equipment) of a pre-defined location by analyzing the texture of detected SAR imagery. Using an exploratory dataset, we trained a support vector machine (SVM), a random binary forest, and a fully-connected neural network for classification. We use Haralick texture features in the VV and VH polarization channels as the input features to our classifiers. Each classifier showed promising results in being able to distinguish between two possible types of construction-site activity levels. This paper documents a case study that is centered around monitoring the construction process for oil and gas fracking wells.

A Comparison of Deep Learning Object Detection Models for Satellite Imagery

In this work, we compare the detection accuracy and speed of several state-of-the-art models for the task of detecting oil and gas fracking wells and small cars in commercial electro-optical satellite imagery. Several models are studied from the single-stage, two-stage, and multi-stage object detection families of techniques. For the detection of fracking well pads (50m - 250m), we find single-stage detectors provide superior prediction speed while also matching detection performance of their two and multi-stage counterparts. However, for detecting small cars, two-stage and multi-stage models provide substantially higher accuracies at the cost of some speed. We also measure timing results of the sliding window object detection algorithm to provide a baseline for comparison. Some of these models have been incorporated into the Lockheed Martin Globally-Scalable Automated Target Recognition (GATR) framework.

Globally-scalable Automated Target Recognition (GATR)

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.