Commitment and Typicality Measurements for the Self-Organizing Map

As a neural approach, Kohonen's Self-Organizing Map (SOM) has not been explored as thoroughly as the MLP, especially for the soft classification. In this paper, we propose two non-parametric algorithms for the SOM to provide soft classification outputs. These algorithms, which are labeling-frequency-based and are called SOM Commitment (SOMC) and SOM Typicality (SOM-T), expressing in the first case the degree of commitment the 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. To evaluate the two proposed algorithms, soft classifications of a SPOT HRV image were undertaken. A Bayesian posterior probability soft classifier and a Mahalanobis typicality soft classifier were also used as a comparison. Principal Components Analysis (PCA) was used to explore the relationship between these measures. Results indicate that great similarities exist between the SOM-C and a parametric Bayesian posterior probability classifier, and between the SOM-T and a Mahalanobis typicality classifier.