@InProceedings{Supelec485,
author = {Michel Barret and Isidore-Paul Akambita and Jean-Louis Gutzwiller and Florio Dalla Vedova},
title = {{Lossy hyperspectral image coding with exogenous quasi optimal transforms}},
year = {2009},
booktitle = {{Data Compression Conference}},
pages = {411 - 419},
month = {Mars},
address = {Snowbird (USA)},
url = {http://dx.doi.org/10.1109/DCC.2009.8},
doi = {10.1109/DCC.2009.8},
abstract = {It is well known in transform coding that the Karhunen- Loève Transform (KLT) can be suboptimal for non Gaussian sources. However in many applications using JPEG2000 Part 2 codecs, the KLT is generally considered as the optimal linear transform for reducing redundancies between components of hyperspectral images. In previous works, optimal spectral transforms (OST) compatible with the JPEG2000 Part 2 standard have been introduced, performing better than the KLT but with an heavier computational cost. In this paper, we show that the OST computed on a learning basis constituted of Hyperion hyperspectral images issued from one sensor performs very well, and even better than the KLT, on other images issued from the same sensor.}
}