An efficient neural classification chain of SAR and optical urban images

In this paper a suitable neural classification algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and c lassification of optical and SAR urban images. ART networks provide a flexi ble tool for classification, but are ruled by a large number of parameters. Therefore, the simplified ART2-A algorithm is used in this paper, and the neural approach is integrated into a classification chain where fuzzy clust ering for merging of classes is also considered. The interaction between th e two methods leads to encouraging results in less CPU time than classifica tion with fuzzy clustering alone or other classical approaches (ISODATA). E xamples of classification are provided using C-band total power AIRSAR data and optical images of Santa Monica, Los Angeles.