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.
Type de document :
Communication dans un congrès
IEEE International Geoscience And Remote Sensing Symposium 2009 (IGARSS 2009), Jul 2009, Le Cap, South Africa. IEEE, 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Proceedings, 3, pp.93-96, 2009, <10.1109/IGARSS.2009.5418095>
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https://hal.archives-ouvertes.fr/hal-00449454
Contributeur : Jocelyn Chanussot <>
Soumis le : jeudi 21 janvier 2010 - 16:48:10
Dernière modification le : mardi 12 septembre 2017 - 11:40:46

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Jesus Angulo, Santiago Velasco-Forero, Jocelyn Chanussot. Multiscale stochastic watershed for unsupervised hyperspectral image segmentation. IEEE International Geoscience And Remote Sensing Symposium 2009 (IGARSS 2009), Jul 2009, Le Cap, South Africa. IEEE, 2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009, Proceedings, 3, pp.93-96, 2009, <10.1109/IGARSS.2009.5418095>. <hal-00449454>

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