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Proceedings Paper

Segmentation of very high spatial resolution panchromatic images based on wavelets and evidence theory
Author(s): Antoine Lefebvre; Thomas Corpetti; Laurence Hubert Moy
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Paper Abstract

This paper is concerned with the segmentation of very high spatial resolution panchromatic images. We propose a method for unsupervised segmentation of remotely sensed images based on texture information and evidence theory. We first perform a segmentation of the image using a watershed on some coefficients issued from a wavelet decomposition of the initial image. This yields an over-segmented map where the similar objects, from a textural point of view, are aggregated together in a step forward. The information of texture is obtained by analyzing the wavelet coefficients of the original image. At each band of the wavelet decomposition, we compute an indicator of similarity between two objects. All the indicators are then fused using some rules of evidence theory to derive a unique criterion of similarity between two objects.

Paper Details

Date Published: 22 October 2010
PDF: 13 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 78300E (22 October 2010); doi: 10.1117/12.864802
Show Author Affiliations
Antoine Lefebvre, CNRS/LIAMA, Chinese Academy of Sciences (China)
COSTEL, CNRS, Univ. de Rennes 2 (France)
Thomas Corpetti, CNRS/LIAMA, Chinese Academy of Sciences (China)
Laurence Hubert Moy, COSTEL, CNRS, Univ. de Rennes 2 (France)


Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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