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

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

Description

Title: Multiscale object-based classification of satellite images merging multispectral information with panchromatic textural features
Author/s:
  • Gonzalo Martín, Consuelo
  • Lillo Saavedra, Mario Fernando
Item Type: Presentation at Congress or Conference (Article)
Event Title: 31st EARSeL Symposium and 35th General Assembly 2011
Event Dates: 30/05/2011 - 02/06/2011
Event Location: Praga, República Checa
Title of Book: Proceedings of 31st EARSeL Symposium and 35th General Assembly 2011
Date: 2011
Subjects:
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 12749
DC Identifier: http://oa.upm.es/12749/
OAI Identifier: oai:oa.upm.es:12749
Official URL: http://www.conferences.earsel.org/abstract/show/2243
Deposited by: Memoria Investigacion
Deposited on: 13 Dec 2012 18:24
Last Modified: 21 Apr 2016 12:01
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