'Institute of Electrical and Electronics Engineers (IEEE)'
Abstract
Recently morphological diversity and sparsity have
emerged as new and effective sources of diversity for
Blind Source Separation. Based on these new concepts,
novelmethods such as Generalized Morphological Component
Analysis have been put forward. The latter takes
advantage of the very sparse representation of structured
data in large overcomplete dictionaries, to separate
sources based on their morphology. Building on GMCA,
the purpose of this contribution is to describe a new algorithm
for hyperspectral data processing. Large-scale
hyperspectral data refers to collected data that exhibit
sparse spectral signatures in addition to sparse spatial
morphologies, in specified dictionaries of spectral and
spatial waveforms. Numerical experiments are reported
which demonstrate the validity of the proposed extension
for solving source separation problems involving
hyperspectral data