Classification of SAR Images Through a Convex Hull Region Oriented Approach
This paper presents a new symbolic classifier based on a region oriented approach. Concerning the learning step, each class is described by a region (or a set of regions) in Rp defined by the convex hull of the objects belonging to this class. In the allocation step, the assignment of a new object to a class is based on a dissimilarity matching function which compares the class description (a region or a set of regions) with a point in Rp. To show the usefulness of this approach, experiments with simulated SAR images were considered. The evaluation of the proposed classifier is based on the prediction accuracy and it is achieved in the framework of a Monte Carlo experience.
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