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 self-organizing map (SOM), which is a clustering algorithm based on competitive learning. We have found that self-organization 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 self-organizing 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. !45
展开▼
