Can multiresolution fusion techniques improve classification accuracy?

In this paper we propose an analysis of the effects of the multiresolution fusion process on the accuracy provided by supervised classification algorithms. In greater detail, the rationale of this analysis consists in understanding in what conditions the merging process can increase/decrease the classification accuracy of different labeling algorithms. On the one hand, it is expected that the multiresolution fusion process can increase the classification accuracy of simple classifiers, characterized by linear or "moderately" non-linear discriminant functions. On the other hand, the spatial and spectral artifacts unavoidably included in the fused images can decrease the accuracy of more powerful classifiers, characterized by strongly non-linear discriminant functions. In this last case, in fact, the classifier is intrinsically able to extract and emphasize all the information present in the original images without any need of a merging procedure. These effects may be different by considering different fusion methodologies and different classification techniques. Several experiments are carried out by applying the different fusion and classification techniques to an image acquired by the Quickbird sensor on the city of Pavia (Italy). From these experiments it is possible to derive interesting conclusions on the effectiveness and the appropriateness of the different investigated multiresolution fusion techniques with respect to classifiers having different complexity and capacity.