Spatial and temporal classification with multiple self-organizing maps

Weijan Wan; Donald Fraser

doi:10.1117/12.262899

17 December 1996 Spatial and temporal classification with multiple self-organizing maps

Weijan Wan, Donald Fraser

Proceedings Volume 2955, Image and Signal Processing for Remote Sensing III; (1996) https://doi.org/10.1117/12.262899
Event: Satellite Remote Sensing III, 1996, Taormina, Italy

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.

Citation Download Citation

Weijan Wan and Donald Fraser "Spatial and temporal classification with multiple self-organizing maps", Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); https://doi.org/10.1117/12.262899

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