<title>Spatial and temporal classification with multiple self-organizing maps</title>

Abstract

There has been a great deal of interest recently in pattern recognition and classification for remote sensing, using both classical statistics and artificial neural networks. An interesting neural network is Kohonen's seif-organising map (SOM), which is a clustering algorithm based on competitive learning. We have found that seif-organisation is a neural network paradigm that is especially suited to remote sensing applications, because of its power and accuracy, its conceptual simplicity and efficiency during learning. A disadvantage of the Kohonen SOM is that there is no inherent partitioning. We have investigated a natural extension of the SOM to multiple seif-organising maps, which we call MSOM, as a means of providing a framework for various remote sensing classification requirements. These include both supervised and unsupervised classification, high dimensional data analysis, multisource data fusion, spatial analysis and combined spatial and temporal classification.