A neural network for unsupervised categorization of multivalued input patterns: an application to satellite imaee clustering

This article presents an implementation of an artificial neural network (ANN) which performs unsupervised detection of recognition categories from arbitrary sequences of multivalued input patterns. The proposed ANN is called Simplified Adaptive Resonance Theory Neural Network (SARTNN). First, an Improved Adaptive Resonance Theory 1 (IARTl)-based neural network for binary pattern analysis is discussed and a Simplified ARTl (SART1) model is proposed. Second, the SARTl model is extended to multivalued input pattern clustering and SART” is presented. A normalized coefficient which measures the degree of match between two multivalued vectors, the Vector Degree of Match (VDM), provides SARTNN with the metric needed to perform clustering. Every ART architecture guarantees both plasticity and stability to the unsupervised learning stage. The SARTNN plasticity requirement is satisfied by implementing its attentional subsystem as a self-organized, feed-forward, flat Kohonen’s ANN (KANN). The SARTNN stability requirement is properly driven by its orienting subsystem. SARTNN processes multivalued input vectors while featuring a simplified architectural and mathematical model with respect to both the ARTl and the AkT2 models, the latter being the ART model fitted to multivalued input pattern categorization. While the ART2 model exploits ten user-defined parameters, SARTNN requires only two user-defined parameters to be run: the first parameter is the vigilance threshold, p, that affects the network’s sensibility in detecting new output categories, whereas the second parameter, T, is related to the network's learning rate. Both parameters have an intuitive physical meaning and allow the user to choose easily the proper discriminating power of the category extraction algorithm. The SARTNN performance is tested as a satellite image clustering algorithm. A chromatic component extractor is recommended in a satellite image preprocessing stage, in order to pursue SARTNN invariant pattern recognition. In comparison with classical clustering algorithms like ISODATA, the implemented system gives good results in terms of ease of use, parameter robustness and computation time. SARTNN should improve the performance of a Constraint Satisfaction Neural Network (CSNN) for image segmentation. SARTNN, exploited as a self-organizing first layer, should also improve the performance of both the Counter Propagation Neural Network (CPNN) and the Reduced connectivity Coulomb Energy Neural Network (RCENN).