It is often useful to fuse remotely sensed data taken from different sensors. However, before this multi-sensor data fusion can be performed the data must first be registered. In this paper we investigate the use of a new information-theoretic similarity measure known as Cross-Cumulative Residual Entropy (CCRE) for multi-sensor registration of remote sensing imagery. The results of our experiments show that the CCRE registration algorithm was able to automatically register images captured with SAR and optical sensors with 100% success rate for initial maximum registration errors of up to 30 pixels and required at most 80 iterations in the successful cases. These results demonstrate a significant improvement over a recent mutual-information based technique.
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