Optimizing panchromatic image change detection based on change index multiband image analysis

Martínez Izquierdo, María Estíbaliz; Arquero Hidalgo, Águeda y Molina Sánchez, Iñigo (2015). Optimizing panchromatic image change detection based on change index multiband image analysis. "IEEE Latin America Transactions", v. 13 (n. 3); pp. 870-875. ISSN 1548-0992. https://doi.org/10.1109/TLA.2015.7069117.

Descripción

Título: Optimizing panchromatic image change detection based on change index multiband image analysis
Autor/es:
  • Martínez Izquierdo, María Estíbaliz
  • Arquero Hidalgo, Águeda
  • Molina Sánchez, Iñigo
Tipo de Documento: Artículo
Título de Revista/Publicación: IEEE Latin America Transactions
Fecha: Marzo 2015
Volumen: 13
Materias:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

This work proposes an optimization of a semi-supervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.

Más información

ID de Registro: 35710
Identificador DC: http://oa.upm.es/35710/
Identificador OAI: oai:oa.upm.es:35710
Identificador DOI: 10.1109/TLA.2015.7069117
URL Oficial: http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9907
Depositado por: Memoria Investigacion
Depositado el: 08 Jul 2015 06:58
Ultima Modificación: 17 Nov 2017 08:39
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