A smart iterative algorithm for multisensor image registration

Realtime multisensor image registration algorithms must be computationally efficient. Often, simplifying assumptions are made to reduce computational time. However, these simplifications usually trade registration convergence performance for reduced runtime. For non-realtime applications where computational resources are not severely limited, this tradeoff may be reversed to improve convergence performance at the expense of increased computational cost. To this end we introduce a smart iterative approach to minimize mis-registrations and thus optimize registration convergence probability. The approach involves performing a registration sweep over a smart sampling of parameters governing feature generation. This approach involves use of two components; a feature sensitivity measure (FSM) and a registration verification metric (VM). The FSM measures the effect of parameter values on feature set variability. This measure enables choice of a suitable parameter sampling density to use for performing iterative registration solution search. The VM provides feedback on the registration solution verity in the absence of ground truth and is used to identify a converged solution. First, we provide an overview of the registration framework used to generate convergence results. Next we introduce the FSM and present mathematical properties. We then describe the VM and present the iterative algorithm. We present numerical results illustrating FSM convergence with increasing parameter sampling density for Canny edge features in SAR imagery. We illustrate use of FSM convergence behavior to select a suitable parameter sampling density for use in the iterative algorithm. Finally, SAR-to-EO registration performance results are presented showing improved convergence probability.