Dynamic algorithm selection for multi-sensor image registration

Stephen DelMarco; Victor Tom; Helen Webb; David Lefebvre

doi:10.1117/12.813559

11 May 2009 Dynamic algorithm selection for multi-sensor image registration

Stephen DelMarco, Victor Tom, Helen Webb, David Lefebvre

Proceedings Volume 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII; 733612 (2009) https://doi.org/10.1117/12.813559
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

Abstract

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 algorithm.

Citation Download Citation

Stephen DelMarco, Victor Tom, Helen Webb, and David Lefebvre "Dynamic algorithm selection for multi-sensor image registration", Proc. SPIE 7336, Signal Processing, Sensor Fusion, and Target Recognition XVIII, 733612 (11 May 2009); https://doi.org/10.1117/12.813559

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