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Multiscale stochastic watershed for unsupervised hyperspectral image segmentation

Abstract : This paper deals with unsupervised segmentation of hyper-spectral images. It is based on the stochastic watershed, an approach to estimate a probability density function (pdf) of contours of an image using Monte Carlo simulations of watershed segmentations. In particular, it is introduced for the first time a multiscale framework for the computation of the pdf of contours using the stochastic watershed. Two multiscale approaches are considered: i) a linear scale-space using Gaussian filters, ii) a nonlinear morphological scale-space pyramid using levelings. In addition, a multiscale pyramid obtained by modifying the size of the random markers is also studied. Then, it is shown how the pdf of contours can finally be segmented using the non-parametric waterfalls algorithm. The performances of the proposed methods are compared using two examples of standard remote sensing hyperspectral images.
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https://hal.archives-ouvertes.fr/hal-00449454
Contributor : Jocelyn Chanussot <>
Submitted on : Thursday, January 21, 2010 - 4:48:10 PM
Last modification on : Friday, April 16, 2021 - 4:28:35 PM

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Jesus Angulo, Santiago Velasco-Forero, Jocelyn Chanussot. Multiscale stochastic watershed for unsupervised hyperspectral image segmentation. IGARSS 2009 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2009, Le Cap, South Africa. pp.93-96, ⟨10.1109/IGARSS.2009.5418095⟩. ⟨hal-00449454⟩

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