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Descripción
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| Self-organizing map projects highdimensional signal spaces on a two-dimensional displayable space, compressing information while preserving the most important topological relationships of the training patterns. These characteristics make self-organizing map a useful tool for multidimensional patterns visualization. In this work, a projection method of multidimensional data in two-dimensional space using growing self-organizing maps is proposed. With this projection technique, several traditional methods for visual analysis of Kohonen¿s self-organizing maps have been implemented for growing self-organizing maps. Different groups of multispectral data of images registered by Landsat-ETM+ and QuickBird satellites have been used to perform an exploratory visual analysis with this new graphical technique. | |
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Internacional
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Si |
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Nombre congreso
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Sensing and understanding our planet |
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Tipo de participación
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960 |
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Lugar del congreso
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Piscataway, NJ, EEUU |
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Revisores
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Si |
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ISBN o ISSN
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1-4244-1212-9 |
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DOI
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