Abstract
We review several detection strategies that account for the possible sparsity of target sources in data cubes. The considered sparsity can exist in the data acquisition space, or in some transform domain. Theoretical aspects of the detection tests are first described. Emphasis is then put on practical issues that may arise in hyperspectral data, such as spatio-spectral dependencies or very low Signal-to-Noise Ratios. Applications are finally described in the framework of the hyperspectral data of MUSE (Multi-Unit Spectroscopic Explorer) instrument. MUSE is a powerful integral field spectrograph whose observational abilities should provide unprecedented insights about the formation and evolution of galaxies.
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