Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances

Authors:
Paul C. Smits
Affiliations:
-
Venue:
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Year:
2001

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Abstract

This article focuses on the use of multiple classifier systems (MCSs) based on dynamic classifier selection. Four implementation strategies of MCSs are compared: majority voting, belief networks, and two designs based on dynamic classifier selection. Experimental results indicate that the direction taken by Woods et al. [1] is the best alternative for remote sensing applications for which the classifier-dependent posterior distributions are unknown.