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

Martínez Izquierdo, María Estíbaliz and Arquero Hidalgo, Águeda and 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.

Description

Title: Optimizing panchromatic image change detection based on change index multiband image analysis
Author/s:
  • Martínez Izquierdo, María Estíbaliz
  • Arquero Hidalgo, Águeda
  • Molina Sánchez, Iñigo
Item Type: Article
Título de Revista/Publicación: IEEE Latin America Transactions
Date: March 2015
ISSN: 1548-0992
Volume: 13
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 35710
DC Identifier: http://oa.upm.es/35710/
OAI Identifier: oai:oa.upm.es:35710
DOI: 10.1109/TLA.2015.7069117
Official URL: https://ieeexplore.ieee.org/document/7069117
Deposited by: Memoria Investigacion
Deposited on: 08 Jul 2015 06:58
Last Modified: 31 May 2019 13:38
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