Multiple Classifier Systems
Lecture Notes in Computer Science, 2001, Volume 2096/2001, 269-278, DOI: 10.1007/3-540-48219-9_27

Combining Supervised Remote Sensing Image Classifiers Based on Individual Class Performances

Paul C. Smits

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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.

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