An expectation-maximization framework for the estimation of bathymetry from side-scan sonar images

In this paper a new procedure for the computation of seabed altitude information from side-scan sonar data is presented. Although side-scan sensors do not provide direct measures of seabed elevation, their images are directly related to seabed topography. Using a mathematical model for the sonar ensonification process, approximations to the seabed characteristics can be inferred from the sonar image. The problem is however severely under-constrained, in the sense that not all the parameters involved in the image formation process can be directly determined from the side-scan image. To overcome this difficulty, we propose the utilization of a multiresolution expectation-maximization framework to select the most probable parameters from the solution space. At every iteration the estimated solution is used to simulate a side-scan image of the observed scene, which is then be compared to the side-scan image actually observed; solution parameters are then refined using gradient-descent optimization. The process is repeated until convergence is achieved.