A dual U-Net algorithm for automating feature extraction from satellite imagery
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Authors
Humphries, Samuel
Parker, Trevor
Jonas, Bryan
Adams, Bryan
Clark, Nicholas J.
Issue Date
2021
Type
journal-article
Language
Keywords
Artificial Intelligence , Military intelligence operations , Convolutional Neural Network
Alternative Title
Abstract
Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.
Description
Citation
1. Humphries S, Parker T, Jonas B, Adams B, Clark NJ. A dual U-Net algorithm for automating feature extraction from satellite imagery. The Journal of Defense Modeling and Simulation. 2021;18(3):193-205. doi:10.1177/1548512920983549
Publisher
Journal of Defense Modeling & Simulation
License
Journal
Volume
Issue
PubMed ID
ISSN
1548-5129
1557-380X
1557-380X