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.
In signal processing, signals are often treated as Gaussian random variables in order to simplify processing when, in fact, they are not. Similarly, in multispectral image processing, grayscale images from individual spectral bands do not have simple, predictable distributions nor are the bands independent from one another. Equalization and histogram shaping techniques have been used for many years to map signals and images to more desirable probability mass functions such as uniform or Gaussian. The ability to extend these techniques to multivariate random variables, i.e. jointly across multiple bands or channels, can be difficult due to an insufficient number of samples for constructing a multidimensional distribution. If successful, however, the resulting components can be made to be both uncorrelated and statistically independent. A method is presented here that achieves reasonably good equalization and Gaussian shaping in multispectral imagery. When combined with principle components analysis, the resulting components are not only uncorrelated, as would be expected, but are statistically independent as well.
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