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A spatial feature enhanced MMI algorithm for multi-modal wild-fire image registration

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2 Author(s)
Xiaofeng Fan ; Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, NY 14623, USA ; Harvey E. Rhody

The integration of multi-spectral airborne imagery and geographic data for wildfire and emergency response requires 3D multiple view registration. Registration of maps, visible imagery and IR imagery, especially LWIR, is challenging because of the difference in brightness, color and features that are available in the different modalities. We have developed a semi-automated workflow for the registration and exploitation of this imagery and data that can produce quick-turnaround products for research and wildfire management. The technique is based upon an enhancement of the conventional maximization of mutual information. This technique largely overcomes the problems that arise from uncorrelated variations in pixel intensity between visible sensors, LWIR sensors that respond to temperature variations, and artificial colorations present in maps. A measure of registration confidence based upon the kurtosis of search space has been developed to enable operators to be cued to examine suspicious result produced by the semi-automated workflow algorithms. Experiments on real wild-fire imagery demonstrate the performance of the technique.

Published in:

2008 37th IEEE Applied Imagery Pattern Recognition Workshop

Date of Conference:

15-17 Oct. 2008