Observatorio de I+D+i UPM

Descripción
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.
Internacional	Si
Nombre congreso	Sensing and understanding our planet
Tipo de participación	960
Lugar del congreso	Piscataway, NJ, EEUU
Revisores	Si
ISBN o ISSN	1-4244-1212-9
DOI
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Título de las actas

Esta actividad pertenece a memorias de investigación

• Participante: Soledad Delgado Sanz
• Autor: M.Estibaliz Martinez Izquierdo (UPM)
• Autor: Agueda Arquero Hidalgo (UPM)
• Autor: Consuelo Gonzalo Martin (UPM)

• Creador: Grupo de Investigación: Informática Aplicada al Procesado de Señal e Imagen
• Departamento: Arquitectura y Tecnología de Sistemas Informáticos