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Proceedings Paper

Model-based feature fusion approach
Author(s): Piet B. W. Schwering
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Paper Abstract

In recent years different sensor data fusion approaches have been analyzed and evaluated in the field of mine detection. In various studies comparisons have been made between different techniques. Although claims can be made for advantages for using certain techniques, until now there has been no single method identified with clearly outstanding performance in all scenarios. In this paper we describe a fusion approach based on a combination of modeling data and feature extraction. By using scenario and environmental data, model predictions are made of sensor data, performance data and mine feature data. These data are then compared with the sensor pre-processing as well as made available for use in the sensor fusion processing. These comparisons take into account the expected and measured sensor features for each object. In scenarios with sufficient a prior knowledge it is expected that detection is improved by applying the model based feature fusion algorithm. The new concept is described and first results primarily based on thermal IR test data recorded at the TNO test facility are presented. Particular attention was paid to the detection pre- processing and the feature fusion stage.

Paper Details

Date Published: 18 October 2001
PDF: 12 pages
Proc. SPIE 4394, Detection and Remediation Technologies for Mines and Minelike Targets VI, (18 October 2001); doi: 10.1117/12.445434
Show Author Affiliations
Piet B. W. Schwering, TNO (Netherlands)


Published in SPIE Proceedings Vol. 4394:
Detection and Remediation Technologies for Mines and Minelike Targets VI
Abinash C. Dubey; James F. Harvey; J. Thomas Broach; Vivian George, Editor(s)

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