Disparity maps estimated using computer vision-derived algorithms usually lack quantitative error estimates. This can be a major issue when the result is used to measure reliable physical parameters, such as topography for instance. Thus, we developed a new method to infer the dense disparity map from two images. We use a probabilistic approach in order to compute uncertainties as well. Within this framework, parameters are described in terms of random variables. We start by defining a generative model for both raw observed images given all model variables, including disparities. The forward model mainly consists of warping the scene using B-Splines and adding a radiometric change map. Then we use Bayesian inference to invert and recover the a posteriori probability density function (pdf) of the disparity map. The main contributions are: The design of an efficient fractal model to take into account radiometric changes between images; A multigrid processing so as to speed up the optimization process; The use of raw data instead of orthorectified imagery; Efficient approximation schemes to integrate out unwanted parameters and compute uncertainties on the result. Three applications could benefit from this disparity inference method: DEM generation from a stereo pair (along or across track), automatic calibration of pushbroom cameras, and ground deformation estimation from two images at different dates. 1

We focus on a geophysical application of image processing: the measurement of high resolution ground deformation from two optical satellite images taken at different dates. Dispar-ity maps estimated from image pairs usually lack quantitative error estimates. This is a major issue for measuring phys-ical parameters, such as ground deformation or topography variations. Thus, we propose a new method to infer the dis-parity map. We adopt a probabilistic approach, treating all parameters as random variables, which provides a rigorous framework for parameter estimation and uncertainty evalua-tion. We start by defining a generative model of the data given all model variables. This forward model consists of warping the scene using B-Splines and applying a spatially adaptive radiometric change map. Then we use Bayesian inference to invert and recover the a posteriori probability density function (pdf) of the disparity map. The method is validated on mul-tidate SPOT 5 imagery related to the Bam earthquake (Iran), showing results compatible with INSAR measurements.