Machine Learning and Classification
Stottler Henke is at the forefront of innovative machine learning tactics and techniques. Our projects delve deep into the newest technologies to maximize efficiency.
Success Stories
AI/ML Pathogen Classification Tool (PACT) Awarded Contract for Further Development: In response to the growing global threat of bioterrorism, Stottler Henke explores the potential for machine learning to address the need for biosurveillance technology capable of assessing the pathogenic potential of novel bacteria, for… Read more |
RF-IR Data Fusion for Space Object Classification Using Human-Quality Artificial Intelligence Techniques: In a recent effort, Stottler Henke used low-level pixel processing techniques to process actual infrared and radar images for better correlation, classification, and… Read more |
QGEN: |
Anomaly Detection via Topological Feature Map: |
AMER: High Accuracy Ship Target Classification from Multi-Modal Imagery: Stottler Henke is developing the Automated Maritime Entity Recognition (AMER) system to hierarchically classify ships in IR ISAR imagery. AMER is designed to provide the U.S. Navy with an automatic target recognition system usable for both single target identification and for surveying multiple objects in a scene for situational awareness… Read more |
DeltaSat: Automatic Change Detection on Commercial Satellite Imagery: DeltaSat incorporates ortho-rectification, geo-referencing, homographic projection, and linear normalization, as well as state-of-the-art image processing techniques such as adaptive… Read more |
Building Adaptive Opponents for Pilot Training: This project is focused on using artificial intelligence and machine learning techniques to create adaptive agents for simulation-based air combat. This effort is part of a larger program at the Air Force Research Laboratory aimed at… Read more |