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Proceedings Paper

Automatic registration of SAR and optical imagery using cross-cumulative residual entropy
Author(s): Mark R. Pickering; Yi Xiao; Xiuping Jia
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Paper Abstract

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.

Paper Details

Date Published: 28 September 2009
PDF: 10 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 74770W (28 September 2009); doi: 10.1117/12.830046
Show Author Affiliations
Mark R. Pickering, The Univ. of New South Wales (Australia)
Australian Defence Force Academy (Australia)
Yi Xiao, The Univ. of New South Wales (Australia)
Australian Defence Force Academy (Australia)
Xiuping Jia, The Univ. of New South Wales (Australia)
Australian Defence Force Academy (Australia)


Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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