Segmentation of vegetation scenes: the SIEMS method

Alexandre Alakian

doi:10.1117/12.973705

8 November 2012 Segmentation of vegetation scenes: the SIEMS method

Alexandre Alakian

Proceedings Volume 8537, Image and Signal Processing for Remote Sensing XVIII; 85371A (2012) https://doi.org/10.1117/12.973705
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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.

Citation Download Citation

Alexandre Alakian "Segmentation of vegetation scenes: the SIEMS method", Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85371A (8 November 2012); https://doi.org/10.1117/12.973705

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