This thesis deals with estimation and supervised detection issues in hyperspectral imagery, applied in coastal environments. Bathymetric models of reflectance are used for modeling the water column influence on the incident light. Various parameters are optically active and are responsible for distorting the reflectance spectrum (phytoplankton, colored dissolved organic matter...). We adopt a new statistical approach for estimating these parameters, which are usually retrieved by inverting physical models. Various methods such as maximum likelihood estimation, maximum a posteriori estimation, and Cramér-Rao bound calculation, are successfully implemented on simulated and real data. Moreover, we adapt the frequently used supervised detectors to the underwater target detection context. If some parameters describing the water column influence are unknown, we propose a new filter, based on the generalized likelihood ratio test, and that enables the detection without any a priori knowledge on these parameters.
