Possibilistic Versus Belief Function Fusion for Antipersonnel Mine Detection

Two approaches for combining humanitarian mine detection sensors are presented-one based on belief functions and the other one based on possibility theory. The approaches are described in parallel. First, different measures are extracted from the sensor data. Mass functions and possibility distribut...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2008-05, Vol.46 (5), p.1488-1498
Main Authors: Milisavljevic, N., Bloch, I.
Format: Article
Language:English
Subjects:
Antipersonnel mines
Cameras
Data mining
Ground penetrating radar
Infrared detectors
Infrared image sensors
Knowledge representation
Mine detection
Mines
Multisensor systems
Operators
Possibility theory
Radar detection
Remote sensing
Sensor fusion
Sensor phenomena and characterization
Sensors
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Summary:Two approaches for combining humanitarian mine detection sensors are presented-one based on belief functions and the other one based on possibility theory. The approaches are described in parallel. First, different measures are extracted from the sensor data. Mass functions and possibility distributions are then derived from the measures based on prior information. After that, the combination of masses and the combination of possibility degrees are performed in two steps, on a separate sensor level and between the sensors. Combination operators are chosen to account for different characteristics of the sensors. The selection of the decision rules is discussed for both approaches. The proposed approaches are illustrated on a set of real mines and nondangerous objects, and promising results have been obtained.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2008.916210