Hierarchical multilayer perceptron network-based fusion algorithms for detection/classification of mines using multiple acoustic images and magnetic data

Martin G. Bello

doi:10.1117/12.241213

31 May 1996 Hierarchical multilayer perceptron network-based fusion algorithms for detection/classification of mines using multiple acoustic images and magnetic data

Martin G. Bello

Author Affiliations +

Proceedings Volume 2765, Detection and Remediation Technologies for Mines and Minelike Targets; (1996) https://doi.org/10.1117/12.241213
Event: Aerospace/Defense Sensing and Controls, 1996, Orlando, FL, United States

Abstract

Hierarchical neural network approaches have been developed first for combining high and low frequency (HF and LF) Side Scan Sonar imagery, and then for the combination of both acoustic images and Magnetic data. The adopted acoustic data fusion approach consists in a image-screening/HF, LF blob matching stage, followed by an information fusion/classification stage. Three variants of the information fusion/classification algorithm were conceived and evaluated based on `aggregate-feature-combining', `neural-network-discriminant-combining', and individual classifier `decision-based-combining', respectively. The `discriminant- combining' case yielded the best classification performance, and when compared with individual HF, LF classifier performance resulted in at least an order of magnitude reduction in the density of false alarms. Next, results are obtained for combining both acoustic and magnetic data using the described high and low frequency side scan sonar discriminant combining fusion algorithm as a starting point. In the next step, acoustic image pair `tokens' are associated with magnetic `tokens', resulting in three classes of resulting `tokens': `associated' acoustic-pair and magnetic tokens, isolated acoustic-pair tokens, and isolated magnetic `tokens'. Neural network output discriminants are derived for each of the three types of tokens mentioned above, and are employed to make classification decisions. The resulting Detection/Classification Algorithm is evaluated based on a combined ground truth obtained from both acoustic and magnetic sources.

Citation Download Citation

Martin G. Bello "Hierarchical multilayer perceptron network-based fusion algorithms for detection/classification of mines using multiple acoustic images and magnetic data", Proc. SPIE 2765, Detection and Remediation Technologies for Mines and Minelike Targets, (31 May 1996); https://doi.org/10.1117/12.241213

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