%0 Conference Proceedings %T Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images %+ Laboratoire Electronique, Informatique et Image [UMR6306] (Le2i) %+ The Norvegian Research Color Laboratory (ColorLab) %+ The Norwegian Color Research Laboratory (ColorLab) %A Le Moan, Steven %A Mansouri, Alamin %A Voisin, Yvon %A Hardeberg, Jon %< avec comité de lecture %( Image Analysis and Recognition %B International Conference on Image Analysis and Recognition %C Burnaby, Canada %3 Lecture Notes on Computer Science %V 6753/2011 %P 375-384 %8 2011-06-22 %D 2011 %R 10.1007/978-3-642-21593-3_38 %K spectral images %K visualization %K dimensionality reduction %K spatially variant %Z Computer Science [cs]/Signal and Image Processing %Z Engineering Sciences [physics]/Signal and Image processingConference papers %X 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. %G English %2 https://u-bourgogne.hal.science/hal-00637936/document %2 https://u-bourgogne.hal.science/hal-00637936/file/_final_version_ICIAR.pdf %L hal-00637936 %U https://u-bourgogne.hal.science/hal-00637936 %~ UNIV-BOURGOGNE %~ CNRS %~ ENSAM %~ LE2I %~ AGREENIUM %~ IMVIA %~ CORES %~ ARTS-ET-METIERS-SCIENCES-ET-TECHNOLOGIES %~ HESAM %~ HESAM-ENSAM %~ INSTITUT-AGRO