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Proceedings Paper

Spatial and temporal classification with multiple self-organizing maps
Author(s): Weijan Wan; Donald Fraser
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Paper 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.

Paper Details

Date Published: 17 December 1996
PDF: 8 pages
Proc. SPIE 2955, Image and Signal Processing for Remote Sensing III, (17 December 1996); doi: 10.1117/12.262899
Show Author Affiliations
Weijan Wan, Univ. of New South Wales (Australia)
Donald Fraser, Univ. of New South Wales (Australia)


Published in SPIE Proceedings Vol. 2955:
Image and Signal Processing for Remote Sensing III
Jacky Desachy, Editor(s)

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