We find several parametric Bayesian methods for linear unmixing in the literature. On the other hand, the number of articles on unmixing with nonparametric Bayesian methods is more limited. The work done during this internship consisted in proposing a new linear unmixing method based on a nonparametric Bayesian method. To do so, I started by studying and applying on synthetic data the different Dirichlet process mixture models (DPMM) using the three processes (Pólya's urn, Chinese restaurant and Indian bullet) that allow us to generate samples according to the Dirichlet process. Then, I chose the most suitable process in order to apply it to hyperspectral imaging for linear unmixing. The proposed algorithm will allow not only the estimation of the parameters (the abundances and the endmembers) but also their number. More precisely, a Gibbs sampler was used to generate samples asymptotically distributed according to the a-posteriori distribution of the different parameters. These samples were then used to estimate the different unknown parameters of the mixture model, via the Bayesian estimators MAP and MMSE. The resulting algorithm was validated on synthetic and real data.