Experimental comparison of FOSART and FLVQ in a remotely sensed image classification task

Palma N. Blonda; Andrea Baraldi; G. Bafunno; Giuseppe Satalino; G. Ria

doi:10.1117/12.290270

13 October 1997 Experimental comparison of FOSART and FLVQ in a remotely sensed image classification task

Palma N. Blonda, Andrea Baraldi, G. Bafunno, Giuseppe Satalino, G. Ria

Proceedings Volume 3165, Applications of Soft Computing; (1997) https://doi.org/10.1117/12.290270
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

This paper deals with the application of a new competitive, on-line, neuro-fuzzy architecture, the fully self-organizing simplified adaptive resonance theory (FOSART), to the analysis of remote sensed Antarctic data, in a classification experiment. FOSART employs fuzzy set memberships in the weights updating rule; it applies an ART-based vigilance test to control neuron proliferation and takes advantage of the fact that it employs a new version of the competitive Hebbian Rule to dynamically generate and remove synaptic links between neurons, as well as neurons. As a consequence, FOSART can develop disjointed subnets. The results obtained with FOSART have been compared with those obtained with other neuro-fuzzy unsupervised architecture: FuzzySART, FLVQ, SOM. The finding suggests that FOSART performances are lower, at convergence, than those of FLVQ and SOM, even if it shows a faster adaptivity to the input data structure, due to its topological and on-line characteristics.

Citation Download Citation

Palma N. Blonda, Andrea Baraldi, G. Bafunno, Giuseppe Satalino, and G. Ria "Experimental comparison of FOSART and FLVQ in a remotely sensed image classification task", Proc. SPIE 3165, Applications of Soft Computing, (13 October 1997); https://doi.org/10.1117/12.290270

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