Sensitivity analysis of ResNet-based automatic target recognition performance using MuSES-generated EO/IR synthetic imagery

Mark D. Klein; Matthew T. Young; J. David Taylor; John E. Thiel; Corey D. Packard; Matthew K. Fox; Arvin Ignaci; John Zalewski; Alex J. Klaschus; Peter L. Rynes

doi:10.1117/12.2663571

13 June 2023 Sensitivity analysis of ResNet-based automatic target recognition performance using MuSES-generated EO/IR synthetic imagery

Mark D. Klein, Matthew T. Young, J. David Taylor, John E. Thiel, Corey D. Packard, Matthew K. Fox, Arvin Ignaci, John Zalewski, Alex J. Klaschus, Peter L. Rynes

Author Affiliations +

Proceedings Volume 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications; 125290A (2023) https://doi.org/10.1117/12.2663571
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Machine learning algorithms have demonstrated state-of-the-art automated target recognition performance but require a large training set. In the case of electro-optical infrared (EO/IR) remote sensing, acquiring sufficient measured imagery can be difficult, but EO/IR scene simulation is a possible alternative. CoTherm, a co-simulation tool which operates MuSES in an automated fashion, is used to manipulate relevant target, background and sensor inputs to generate a library of radiance images. Various options affecting simulation run-time and output fidelity are considered and the trade-off between accuracy and compute time requirements is quantified using a measured imagery benchmark and ResNets for classification.

Conference Presentation

Citation Download Citation

Mark D. Klein, Matthew T. Young, J. David Taylor, John E. Thiel, Corey D. Packard, Matthew K. Fox, Arvin Ignaci, John Zalewski, Alex J. Klaschus, and Peter L. Rynes "Sensitivity analysis of ResNet-based automatic target recognition performance using MuSES-generated EO/IR synthetic imagery", Proc. SPIE 12529, Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications, 125290A (13 June 2023); https://doi.org/10.1117/12.2663571

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available