Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

Stanton R. Price; Bryce Murray; Lequn Hu; Derek T. Anderson; Timothy C. Havens; Robert H. Luke; James M. Keller

doi:10.1117/12.2223297

3 May 2016 Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery

Stanton R. Price, Bryce Murray, Lequn Hu, Derek T. Anderson, Timothy C. Havens, Robert H. Luke, James M. Keller

Author Affiliations +

Proceedings Volume 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI; 98231G (2016) https://doi.org/10.1117/12.2223297
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

Abstract

A serious threat to civilians and soldiers is buried and above ground explosive hazards. The automatic detection of such threats is highly desired. Many methods exist for explosive hazard detection, e.g., hand-held based sensors, downward and forward looking vehicle mounted platforms, etc. In addition, multiple sensors are used to tackle this extreme problem, such as radar and infrared (IR) imagery. In this article, we explore the utility of feature and decision level fusion of learned features for forward looking explosive hazard detection in IR imagery. Specifically, we investigate different ways to fuse learned iECO features pre and post multiple kernel (MK) support vector machine (SVM) based classification. Three MK strategies are explored; fixed rule, heuristics and optimization-based. Performance is assessed in the context of receiver operating characteristic (ROC) curves on data from a U.S. Army test site that contains multiple target and clutter types, burial depths and times of day. Specifically, the results reveal two interesting things. First, the different MK strategies appear to indicate that the different iECO individuals are all more-or-less important and there is not a dominant feature. This is reinforcing as our hypothesis was that iECO provides different ways to approach target detection. Last, we observe that while optimization-based MK is mathematically appealing, i.e., it connects the learning of the fusion to the underlying classification problem we are trying to solve, it appears to be highly susceptible to over fitting and simpler, e.g., fixed rule and heuristics approaches help us realize more generalizable iECO solutions.

Citation Download Citation

Stanton R. Price, Bryce Murray, Lequn Hu, Derek T. Anderson, Timothy C. Havens, Robert H. Luke, and James M. Keller "Multiple kernel based feature and decision level fusion of iECO individuals for explosive hazard detection in FLIR imagery", Proc. SPIE 9823, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI, 98231G (3 May 2016); https://doi.org/10.1117/12.2223297

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