Modern sensors have a range of modalities including SAR, EO, and IR. Registration of multimodal imagery from such
sensors is becoming an increasingly common pre-processing step for various image exploitation activities such as image
fusion for ATR. Over the past decades, several approaches to multisensor image registration have been developed.
However, performance of these image registration algorithms is highly dependent on scene content and sensor operating
conditions, with no single algorithm working well across the entire operating conditions space. To address this problem,
in this paper we present an approach for dynamic selection of an appropriate registration algorithm, tuned to the scene
content and feature manifestation of the imagery under consideration. We consider feature-based registration using
Harris corners, Canny edge detection, and CFAR features, as well as pixel-based registration using cross-correlation and
mutual information. We develop an approach for selecting the optimal combination of algorithms to use in the dynamic
selection algorithm. We define a performance measure which balances contributions from convergence redundancy and
convergence coverage components calculated over sample imagery, and optimize the measure to define an optimal
algorithm set. We present numerical results demonstrating the improvement in registration performance through use of
the dynamic algorithm selection approach over results generated through use of a fixed registration algorithm approach.
The results provide registration convergence probabilities for geo-registering test SAR imagery against associated EO
reference imagery. We present convergence results for various match score normalizations used in the dynamic selection