In a more general framework, multichannel data can be the concatenation of physical observations made at different frequency channels. This is the case of multispectral or hyperspectral imaging. Formally, assuming that each observation has t samples, such multichannel are written as  m x t matrix.

Diversity can then be found in each observation separately and also across the observations. In the case of hyperspectral imaging, each observation has an intrinsic structure; a fixed pixel is also a spectrum that has also a given structure. For instance, real world hyperspectral images have hyperspectral pixels that are absorption spectra.

Below, the image features a Mars Express observation. A fixed pixel is then a unidimensional spectrum.






















Then extending the morphological diversity to the multichannel case requires accounting for both spatial and spectral morphologies.


Applications :
































GMCA for BSS with spatial and spectral sparsity constraints (will be available soon)