Automatic registration of multisensor images using an integrated spatial and mutual information (SMI) metric

A new image-registration method is presented by integrating the area-based and feature-based methods. The integrated method is characterized by a novel similarity metric based on spatial and mutual information (SMI), the ant colony optimization for continuous domain (ACO_R), and a two-phase searching strategy. The SMI-based metric takes into account both spatial relations of detected features [spatial information (SI)] and the mutual information (MI) between the reference and sensed images. The spatial relation is to derive a fast transformation of the near global optimum without specifying the initial searching range. The MI is to obtain an optimal transformation with high accuracy. ACO_R is adopted to optimize SMI for the first time in this paper, as the function of SMI is generally non-convex and irregular. In addition, a two-phase searching strategy is designed to improve the performance of ACO_R. Phase-1 only considers the SI and finds some low-accurate solutions. Phase-2 considers both SI and MI so it is to search for a more accurate solution. These two phases are switched according to the diversity of the solutions. The proposed integrated method has been tested using the remote-sensing images acquired from different sensors, including TM, SPOT, and SAR. The experimental results indicate that the SMI-based metric is more robust than the conventional metrics which consider SI or MI alone. This method is able to achieve a highly accurate automatic registration of multisensor images.