Adversarial physics-based augmentations for robust training using synthetic data

Emma Clark; Ellie Walters; Edmund Zelnio

doi:10.1117/12.3013613

7 June 2024 Adversarial physics-based augmentations for robust training using synthetic data

Emma Clark, Ellie Walters, Edmund Zelnio

Author Affiliations +

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320K (2024) https://doi.org/10.1117/12.3013613
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Given the limited amount of measured synthetic aperture radar data available to train object recognition algorithms. Synthetic data is used to train the algorithm while using measured data to test. To account for the variability of measured data and to ensure robustness to various conditions, extensive physics- based augmentations are used during the training process. These augmentations include target, background, and sensor variability. In order to explore the augmentation space most efficiently, the background and sensor variability are explored on-line during the training process using an adversarial learning strategy. Performance trades are reported as a function of the various augmentation strategies.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Emma Clark, Ellie Walters, and Edmund Zelnio "Adversarial physics-based augmentations for robust training using synthetic data", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320K (7 June 2024); https://doi.org/10.1117/12.3013613

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