Fuzzy ARTMAP Based Neurocomputational Spatial Uncertainty Measures
2008
Li, Zhe
<p><i>This paper proposes non-parametric measures for the fuzzy ARTMAP computational neural network to handle spatial uncertainty in remotely sensed imagery classification, i.e., ART Commitment (ART-C) and ART Typicality (ART-T), expressing in the first case the degree of commitment a classifier has for each class for a specific pixel, and in the second case, how typical that pixel’s reflectances are of the ones upon which the classifier was trained for each class. Results from case studies were compared against the previously developed SOM Commitment (SOM-C) and SOM Typicality (SOM-T) classifiers as well as conventional Bayesian posterior probability and Mahalanobis typicality soft classifiers. Principal Components Analysis (PCA) was used to explore the relationship between these different measures. Results indicate that ART-C and SOM-C measures express values similar to Bayesian posterior probabilies, and ART-T and SOM-T are closely related to Mahalanobis typicalities. However, the proposed neural approaches outperform the traditional methods due to their non-parametric advantage.</i></p>
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