Abstract
In many practical situations it is not feasible to collect
labeled samples for all available classes in a domain.
Especially in supervised classification of remotely sensed
images it is impossible to collect ground truth information
over large geographic regions for all thematic classes.
As a result often analysts collect labels for aggregate classes.
In this paper we present a novel learning scheme that automatically
learns sub-classes from the user given aggregate classes.
We model each aggregate class as finite Gaussian mixture
instead of classical assumption of unimodal Gaussian per class.
The number of components in each finite Gaussian mixture are
automatically estimated. Experimental results on real remotely
sensed image classification showed not only improved accuracy
in aggregate class classification but the proposed method
also recognized sub-classes.
