Inferring Deformation Fields from Multidate Satellite Images

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. Disparity maps estimated from image pairs usually lack quantitative error estimates. This is a major issue for measuring physical parameters, such as ground deformation or topography variations. Thus, we propose a new method to infer the disparity map. We adopt a probabilistic approach, treating all parameters as random variables, which provides a rigorous framework for parameter estimation and uncertainty evaluation. 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 multidate SPOT 5 imagery related to the Bam earthquake (Iran), showing results compatible with INSAR measurements.