Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features

Gonzalo Martín, Consuelo y Lillo Saavedra, Mario Fernando (2011). Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features. En: "31st EARSeL Symposium and 35th General Assembly 2011", 30/05/2011 - 02/06/2011, Praga, República Checa. pp. 394-400.

Descripción

Título: Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features
Autor/es:
  • Gonzalo Martín, Consuelo
  • Lillo Saavedra, Mario Fernando
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 31st EARSeL Symposium and 35th General Assembly 2011
Fechas del Evento: 30/05/2011 - 02/06/2011
Lugar del Evento: Praga, República Checa
Título del Libro: Proceedings of 31st EARSeL Symposium and 35th General Assembly 2011
Fecha: 2011
Materias:
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Once admitted the advantages of object-based classification compared to pixel-based classification; the need of simple and affordable methods to define and characterize objects to be classified, appears. This paper presents a new methodology for the identification and characterization of objects at different scales, through the integration of spectral information provided by the multispectral image, and textural information from the corresponding panchromatic image. In this way, it has defined a set of objects that yields a simplified representation of the information contained in the two source images. These objects can be characterized by different attributes that allow discriminating between different spectral&textural patterns. This methodology facilitates information processing, from a conceptual and computational point of view. Thus the vectors of attributes defined can be used directly as training pattern input for certain classifiers, as for example artificial neural networks. Growing Cell Structures have been used to classify the merged information.

Más información

ID de Registro: 12749
Identificador DC: http://oa.upm.es/12749/
Identificador OAI: oai:oa.upm.es:12749
URL Oficial: http://www.conferences.earsel.org/abstract/show/2243
Depositado por: Memoria Investigacion
Depositado el: 13 Dic 2012 18:24
Ultima Modificación: 21 Abr 2016 12:01
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