Morphological Processing of Hyperspectral Images Using Kriging-Based Supervised Ordering

Santiago Velasco-Forero 1 Jesus Angulo 1
1 CMM - Centre de Morphologie Mathématique
Abstract : A novel approach for vectorial 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. In particular, we consider here a kriging-based vectorial ordering. This supervised ordering may then used for the extension of mathematical morphology to vectorial images. Application of morphological processing to hyperspectral image illustrates the performance of proposal operators.
Keywords : Generic frameworks Hyper-spectral images Hyperspectral imagery Kriging Learning formulation Morphological processing Training sets Vectorial images Image processing Imaging systems Independent component analysis Interpolation Mathematical operators Remote sensing Supervised learning Mathematical morphology
Type de document :
Communication dans un congrès
17th IEEE International Conference on Image Processing (ICIP), Sep 2010, Hong-Kong, Hong Kong SAR China. IEEE, pp.1409-1412, 2010, 〈10.1109/ICIP.2010.5651305〉
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https://hal-mines-paristech.archives-ouvertes.fr/hal-00834434
Contributeur : Bibliothèque Mines Paristech <>
Soumis le : samedi 15 juin 2013 - 08:51:05
Dernière modification le : mardi 27 mars 2018 - 16:06:13

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Santiago Velasco-Forero, Jesus Angulo. Morphological Processing of Hyperspectral Images Using Kriging-Based Supervised Ordering. 17th IEEE International Conference on Image Processing (ICIP), Sep 2010, Hong-Kong, Hong Kong SAR China. IEEE, pp.1409-1412, 2010, 〈10.1109/ICIP.2010.5651305〉. 〈hal-00834434〉

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