Name
Title | ||
|---|---|---|
Spatially variant dimensionality reduction for the visualization of multi/hyperspectral images |
Abstract | ||
|---|---|---|
In this paper, we introduce a new approach for color visualization 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 discontinuities 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 hyperspectral datasets illustrate the efficiency of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-21593-3_38 | ICIAR |
Keywords | Field | DocType |
spatially variant dimensionality reduction,traditional method,color visualization,local analysis,relative location,segmentation map,dimensionality reduction adaptively,hyperspectral datasets,new approach,hyperspectral image | Population,Computer vision,Dimensionality reduction,Pattern recognition,Visualization,Segmentation,Computer science,Hyperspectral imaging,Distance transform,Independent component analysis,Artificial intelligence,Pixel | Conference |
Volume | ISSN | Citations |
6753 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven Le Moan | 1 | 46 | 10.80 |
| Alamin Mansouri | 2 | 137 | 22.29 |
| Yvon Voisin | 3 | 65 | 12.66 |
| Jon Y. Hardeberg | 4 | 26 | 4.94 |