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Proceedings Paper

Multispectral image segmentation via multiscale weighted aggregation method
Author(s): Luca Galli; Damiana de Candia
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Paper Abstract

We present a new hierarchical image segmentation algorithm, which extends a fast multiscale iterated weighted aggregation method originally developed by Brandt et al.(1, 2) to multichannel data. This approach, combining both a region-based and an edge-based approach, results well suited for data (like remote sensing images), which present heterogeneous multiscale characteristics. The generalization of the weighted aggregation method to multispectral remote sensing data requires the choice of a suitable statistical model to describe the multichannel data, together with an appropriate spectral homogeneity criterion. To this purpose, we adopted the multivariate Gaussian model, and the Bhattacharyya distance. For multivariate Gaussian distribution the Bhattacharyya distance results in a closed expression, which is an analytical function of the mean and covariance matrix. Such statistical moments can be computed recursively during the segmentation process, preserving the overall linear complexity of the segmentation process. Our segmentation method has been successfully integrated into Knowledge Information Mining (KIM), a tool developed within an ESA research project for content-based image retrieval from remote sensing large archives. Moreover, the proposed segmentation procedure has been extensively used within the DesertWatch project, an ESA Data User Program (DUP) project for the study and the monitoring of desertification processes from multiple Earth Observation data.

Paper Details

Date Published: 19 October 2005
PDF: 8 pages
Proc. SPIE 5982, Image and Signal Processing for Remote Sensing XI, 598207 (19 October 2005); doi: 10.1117/12.627534
Show Author Affiliations
Luca Galli, Advanced Computer Systems S.p.A. (Italy)
Damiana de Candia, Advanced Computer Systems S.p.A. (Italy)


Published in SPIE Proceedings Vol. 5982:
Image and Signal Processing for Remote Sensing XI
Lorenzo Bruzzone, Editor(s)

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