Abstract : The MRF in computer vision and image analysis is a powerful framework for solving complex problems using the MAP estimation. However, some image processing problems deal with high dimensional data and require non-convex MRF energy functions. Thus, optimization process becomes a hard task. The first contribution of this thesis is developing new efficient optimization algorithms for the class of the first order multi-label MRF energies with any likelihood terms and convex prior. The proposed algorithms rely on the graph-cut technique, and iterative strategies based on large and multi-label partition moves. These algorithms offer a trade-off between optimum quality and algorithm complexity. The main application of this work is the digital elevation model (DEM) estimation using interferometric synthetic aperture radar (InSAR) data. This problem is usually considered as a complex and an ill-posed one, because of the high noise rate affecting interferograms and the complex structures qualifying real natural and urban area. So, the second contribution of this work is developing new MRF models relying on the multichannel interferometric likelihood density function and the total variation regularization. Appropriate optimization algorithms are then proposed. The new approach is applied to synthetic and real InSAR data showing better performances than the state of the art techniques.