Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images

Steven Le Moan 1, 2, * Alamin Mansouri 1 Yvon Voisin 1 Jon Hardeberg 3
* Auteur correspondant
1 Le2i - Laboratoire Electronique, Informatique et Image
2 ColorLab - The Norvegian Research Color Laboratory
3 The Norwegian Color Research Laboratory
Abstract : In this paper, we introduce a new approach for color visu- alization of multi/hyperspectral images. Unlike traditional methods, we propose to operate a local analysis instead of considering that all the pixels are part of the same population. It takes a segmentation map as an input and then achieves a dimensionality reduction adaptively inside each class of pixels. Moreover, in order to avoid unappealing discon- tinuities between regions, we propose to make use of a set of distance transform maps to weigh the mapping applied to each pixel with regard to its relative location with classes' centroids. Results on two hyperspec- tral datasets illustrate the efficiency of the proposed method.
Keywords : spectral images visualization dimensionality reduction spatially variant
Type de document :
Communication dans un congrès
International Conference on Image Analysis and Recognition, Jun 2011, Burnaby, Canada. 6753/2011, pp.375-384, 2011, Lecture Notes on Computer Science. <10.1007/978-3-642-21593-3_38>
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Contributeur : Alamin Mansouri <>
Soumis le : jeudi 3 novembre 2011 - 12:12:26
Dernière modification le : jeudi 3 novembre 2011 - 14:09:34
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Steven Le Moan, Alamin Mansouri, Yvon Voisin, Jon Hardeberg. Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images. International Conference on Image Analysis and Recognition, Jun 2011, Burnaby, Canada. 6753/2011, pp.375-384, 2011, Lecture Notes on Computer Science. <10.1007/978-3-642-21593-3_38>. <hal-00637936>

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