M2dSOMAP: clustering and classification of remotely sensed imagery by combining multiple Kohonen self-organizing maps and associative memory

This paper investigates a hybrid neural network framework by combining unsupervised and supervised neural learning paradigms on a unified representation platform of multiple Kohonen 2D self-organizing maps (M2dSOM) with the assistance of associative memory for clustering and classification of remotely sensed (RS) imagery. The M2dSOM is a regional form of such for both cluster region and decision region. A new supervised learning algorithm is proposed that exploits the input portion of supervising samples to discover mismatches between cluster and decision regions by a k-winner selection process and then correct the cluster boundaries based on a majority vote for a new cluster membership from the k winners. Finally, an associative memory is employed to form a mapping between clusters and classification labels by samples. Two association configurations are suggested. Analysis of this mapping SONN model (called M2dSOMAP) in relation to RS imagery analysis with comparison to other methods is briefly discussed.