Augmentation methods for object detection in overhead imagery

Nicholas Hamilton; Adam Webb; Zachary DeKraker; Ben Hendrickson; Matt Blanck; Erin Nelson; Wiley Roemer; Timothy C. Havens

doi:10.1117/12.2588502

12 April 2021 Augmentation methods for object detection in overhead imagery

Nicholas Hamilton, Adam Webb, Zachary DeKraker, Ben Hendrickson, Matt Blanck, Erin Nelson, Wiley Roemer, Timothy C. Havens

Author Affiliations +

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290I (2021) https://doi.org/10.1117/12.2588502
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

The multidisciplinary area of geospatial intelligence (GEOINT) is continually changing and becoming more complex. From efforts to automate portions of GEOINT using machine learning, which augment the analyst and improve exploitation, to optimizing the growing number of sources and variables, there is no denying that the strategies involved in this collection method are rapidly progressing. The unique and inherent complexities involved in imagery analysis from an overhead perspective—e.g., target resolution, imaging band(s), and imaging angle{|test the ability of even the most developed and novel machine learning techniques. To support advancement in the application of object detection in overhead imagery, we have developed a spin-set augmentation method that leverages synthetic data generation capabilities to augment the training data sets. We then test this method with the popular object detection deep network YOLOv4. This paper analyzes the synthetic augmentation method in terms of algorithm detection performance, computational complexity, and generalizability.

Conference Presentation

Citation Download Citation

Nicholas Hamilton, Adam Webb, Zachary DeKraker, Ben Hendrickson, Matt Blanck, Erin Nelson, Wiley Roemer, and Timothy C. Havens "Augmentation methods for object detection in overhead imagery", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290I (12 April 2021); https://doi.org/10.1117/12.2588502

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