This thesis deals with the problem of detecting unknown signals at low Signal- to- Noise Ratio. This work focuses on the definition, study and implementation of efficient methods able to discern only-noise observations from those that presumably carry the information of interest in a sparse way. The relevance of these methods is assessed on hyperspectral data as an applicative part. In the first part of this work, the basic principles of statistical hypothesis testing together with a general overview on sparse representations, estimation and detection are introduced. In the second part of the manuscript, two statistical hypotheses tests are proposed and studied. Both are adapted to the detection of sparse signals. The behaviors and the relative differences between the tests are theoretically investigated through a detailed study of their analytical and structural characteristics. The tests’ detection performances are compared with those of classical frequentist and Bayesian methods. According to the three-dimensional data sets considered in the applicative part, and to be closer to realistic scenarios involving data acquisition systems, the proposed detection strategies are then adapted in order to: i) account for spectrally variable noise; ii) exploit the spectral similarities of neighbors pixels in the spatial domain and iii) exploit the greater accuracy brought by dictionary-based models, which take into account the spatiospectral blur of information caused by instrumental Point Spread Functions. The tests are finally applied to massive astrophysical hyperspectral data in the context of the European Southern Observatory’s Multi Unit Spectroscopic Explorer.