Automatic registration of multi-source data using mutual information

Automatic image registration is a basic step in multi-sensor data integration in remote sensing and photogrammetric applications such as data fusion. The effectiveness of Mutual Information (MI) as a technique for automated multi-sensor image registration has previously been demonstrated for medical and remote sensing applications. In this paper, a new General Weighted MI (GWMI) approach that improves the robustness of MI to local maxima, particularly in the case of registering optical imagery and 3D point clouds, is presented. Two different methods including a Gaussian Mixture Model (GMM) and Kernel Density Estimation have been used to define the weight function of joint probability, regardless of the modality of the data being registered. The Expectation Maximizing method is then used to estimate parameters of GMM, and in order to reduce the cost of computation, a multi-resolution strategy has been used. The performance of the proposed GWMI method for the registration of aerial orthotoimagery and LiDAR range and intensity information has been experimentally evaluated and the results obtained are presented.