  In this study, a multiple-comparison approach is developed for detecting
faint hyperspectral sources. The detection method relies on a sparse and
non-negative representation on a highly coherent dictionary to track a
spatially varying source. A robust control of the detection errors is ensured
by learning the test statistic distributions on the data. The resulting control
is based on the false discovery rate, to take into account the large number of
pixels to be tested. This method is applied to data recently recorded by the
three-dimensional spectrograph Multi-Unit Spectrograph Explorer.
