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Proceedings Paper

Segmentation of vegetation scenes: the SIEMS method
Author(s): Alexandre Alakian
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Paper Abstract

This paper presents an unsupervised segmentation method dedicated to vegetation scenes with decametric or metric spatial resolutions. The proposed algorithm, named SIEMS, is based on the iterative use of the Expectation–Maximization algorithm and offers a good trade-off between oversegmentation and undersegmentation. Moreover, the choice of its input parameters is not image–dependent on the contrary to existing technics and its performances are not crucially determined by these input parameters. SIEMS consists in creating a coarse segmentation of the image by applying an edge detection method (typically the Canny–Deriche algorithm) and splitting iteratively the undersegmented areas with the Expectation–Maximization algorithm. The method has been applied on two images and shows satisfactory results. It notably allows to distinguish segments with slight radiometric variations without leading to oversegmentation.

Paper Details

Date Published: 8 November 2012
PDF: 10 pages
Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85371A (8 November 2012); doi: 10.1117/12.973705
Show Author Affiliations
Alexandre Alakian, ONERA (France)


Published in SPIE Proceedings Vol. 8537:
Image and Signal Processing for Remote Sensing XVIII
Lorenzo Bruzzone, Editor(s)

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