Reliable band-to-band registration of Multispectral Thermal Imager data using multivariate mutual information and cyclic consistency


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In multispectral imaging, automated cross-spectral (band-to-band) image registration is difficult to achieve with a reliability approaching 100%. This is particularly true when registering infrared to visible imagery, where contrast reversals are common and similarity is often lacking. Algorithms that use mutual information as a similarity measure have been shown to work well in the presence of contrast reversal. However, weak similarity between the long-wave infrared (LWIR) bands and shorter wavelengths remains a problem. A method is presented in this paper for registering multiple images simultaneously rather than one pair at a time using a multivariate extension of the mutual information measure. This approach improves the success rate of automated registration by making use of the information available in multiple images rather than a single pair. This approach is further enhanced by including a cyclic consistency check, for example registering band A to B, B to C, and C to A. The cyclic consistency check provides an automated measure of success allowing a different combination of bands to be used in the event of a failure. Experiments were conducted using imagery from the Department of Energy's Multispectral Thermal Imager satellite. The results show a significantly improved success rate.



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