Cart (Loading....) | Create Account
Close category search window
 

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Baraldi, A. ; IMGA-CNR, Modena ; Parmiggiani, F.

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 (IART1)-based neural network for binary pattern analysis is discussed and a Simplified ART1 (SART1) model is proposed. Second, the SART1 model is extended to multivalued input pattern clustering and SARTNN 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 acid mathematical model with respect to both the ART1 and the ART2 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, ρ, that affects the network's sensibility in detecting new output categories, whereas the second parameter, τ, 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 CounterPropagation Neural Network (CPNN) and the Reduced connectivity Coulomb Energy Neural Network (RCENN)

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:33 ,  Issue: 2 )

Date of Publication:

Mar 1995

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.