Supervised Ordering in Rp : Application to Morphological Processing of Hyperspectral Images

Abstract : A novel approach for vector ordering is introduced in this paper. The generic framework is based on a supervised learning formulation which leads to reduced orderings. A training set for the background and another training set for the foreground are needed as well as a supervised method to construct the ordering mapping. Two particular cases of learning techniques are considered in detail: 1) kriging-based vector ordering and 2) support vector machines-based vector ordering. These supervised orderings may then be used for the extension of mathematical morphology to vector images. In particular, in this paper, we focus on the application of morphological processing to hyperspectral images, illustrating the performance with practical examples.
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IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2011, 20 (11), pp.3301 - 3308. 〈10.1109/TIP.2011.2144611〉
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00833508
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Soumis le : mercredi 12 juin 2013 - 22:24:50
Dernière modification le : mercredi 13 septembre 2017 - 01:03:02

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Santiago Velasco-Forero, Jesus Angulo. Supervised Ordering in Rp : Application to Morphological Processing of Hyperspectral Images. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2011, 20 (11), pp.3301 - 3308. 〈10.1109/TIP.2011.2144611〉. 〈hal-00833508〉

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