Share Email Print
cover

Proceedings Paper

Event identification from seismic/magnetic feature vectors: a comparative study
Author(s): James K. Wolford
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The event identification problem plays a large role in the application of unattended ground sensors to the monitoring of borders and checkpoints. The choice of features and methods for classifying features affects how accurately these classifications are made. Finding features which reliably distinguish events of interest may require measurements based on separate physical phenomena. Classification methods include neural net versus fuzzy logic approaches, and within the neural category, different architectures and transfer functions for reaching decisions. This study examines ways of optimizing feature sets and surveys common techniques for classifying feature vectors corresponding to physical events. We apply each technique to samples of existing data, and compare discrimination attributes. Specifically, we calculate the confusion matrices for each technique applied to each sample dataset, and reduce them statistically to scalar scores. In addition, we gauge how the accuracy of each method is degraded by reducing the feature vector length by one element. Finally, we gather rough estimates of the relative cpu performance of the forward prediction algorithms.

Paper Details

Date Published: 24 July 1997
PDF: 8 pages
Proc. SPIE 3081, Peace and Wartime Applications and Technical Issues for Unattended Ground Sensors, (24 July 1997); doi: 10.1117/12.280646
Show Author Affiliations
James K. Wolford, Lawrence Livermore National Lab. (United States)


Published in SPIE Proceedings Vol. 3081:
Peace and Wartime Applications and Technical Issues for Unattended Ground Sensors
Gerold Yonas, Editor(s)

© SPIE. Terms of Use
Back to Top