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Accurate co-registration of multi-sensor data is a primary step in data integration for photogrammetric and remote sensingapplications. A proven intensity-based registration approach is Mutual Information (MI). However the effectiveness of MI forautomated registration of multi-sensor remote sensing data can be impacted to the point of failure by its non-monotonic convergencesurface. Since MI-based methods rely on joint probability density functions (PDF) for the datasets, errors in PDF estimation candirectly affect the MI value. Certain PDF parameter values, such as the bin-size of the joint histogram and the smoothing kernel,need to be assigned in advance, since they play a key role in forming the convergence surface. The lack of a general approach to theassignment of these parameter values for various data types reduces both the automation level and the robustness of registration.This paper proposes a new approach for selection of optimal parameter values for PDF estimation in MI-based registration of opticalimagery to LiDAR point clouds. The proposed method determines the best parameters for PDF estimation via an analysis of therelationship between similarity measure values of the data and the adopted geometric transformation in order to achieve the optimalregistration reliability. The performance of the proposed parameter selection method is experimentally evaluated and the obtainedresults are compared with those achieved through a feature-based registration method

Optimal parameter selection for intensity-based multi-sensor data registration