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.