Improving buried threat detection in ground-penetrating radar with transfer learning and metadata analysis

Kenneth A. Colwell; Peter A. Torrione; Kenneth D. Morton Jr.; Leslie M. Collins

doi:10.1117/12.2177940

21 May 2015 Improving buried threat detection in ground-penetrating radar with transfer learning and metadata analysis

Kenneth A. Colwell, Peter A. Torrione, Kenneth D. Morton Jr., Leslie M. Collins

Proceedings Volume 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX; 94541C (2015) https://doi.org/10.1117/12.2177940
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

Abstract

Ground-penetrating radar (GPR) technology has proven capable of detecting buried threats. The system relies on a binary classifier that is trained to distinguish between two classes: a target class, encompassing many types of buried threats and their components; and a nontarget class, which includes false alarms from the system prescreener. Typically, the training process involves a simple partition of the data into these two classes, which allows for straightforward application of standard classifiers. However, since training data is generally collected in fully controlled environments, it includes auxiliary information about each example, such as the specific type of threat, its purpose, its components, and its depth. Examples from the same specific or general type may be expected to exhibit similarities in their GPR data, whereas examples from different types may differ greatly. This research aims to leverage this additional information to improve overall classification performance by fusing classifier concepts for multiple groups, and to investigate whether structure in this information can be further utilized for transfer learning, such that the amount of expensive training data necessary to learn a new, previously-unseen target type may be reduced. Methods for accomplishing these goals are presented with results from a dataset containing a variety of target types.

Citation Download Citation

Kenneth A. Colwell, Peter A. Torrione, Kenneth D. Morton Jr., and Leslie M. Collins "Improving buried threat detection in ground-penetrating radar with transfer learning and metadata analysis", Proc. SPIE 9454, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XX, 94541C (21 May 2015); https://doi.org/10.1117/12.2177940

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