Multitemporal urban area characterization through fuzzy neural networks

This paper is devoted to the introduction of a fuzzy ARTMAP classifier based on a two-step approach. First, a pixel-by-pixel classification is performed, then a kernel-based refinement is applied to the output of the first step. The approach is applied to a multitemporal data set of a urban area, both to improve any single classification and to exploit the extra information carried by more images of the same area in different dates. We show that the fuzzy ARTMAP neural classifier is well suited to handle this kind of data. We discuss also the advantages deriving from the use of a multitemporal data set in urban areas, where many features are stable and so more data make their detection and recognition easier.