Registration of multi-sensor remote sensing imagery by gradient-based optimization of cross-cumulative residual entropy

Mark R. Pickering; Yi Xiao; Xiuping Jia

doi:10.1117/12.777016

5 May 2008 Registration of multi-sensor remote sensing imagery by gradient-based optimization of cross-cumulative residual entropy

Mark R. Pickering, Yi Xiao, Xiuping Jia

Author Affiliations +

Proceedings Volume 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV; 69660U (2008) https://doi.org/10.1117/12.777016
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

For multi-sensor registration, previous techniques typically use mutual information (MI) rather than the sum-of-the-squared difference (SSD) as the similarity measure. However, the optimization of MI is much less straightforward than is the case for SSD-based algorithms. A new technique for image registration has recently been proposed that uses an information theoretic measure called the Cross-Cumulative Residual Entropy (CCRE). In this paper we show that using CCRE for multi-sensor registration of remote sensing imagery provides an optimization strategy that converges to a global maximum with significantly less iterations than existing techniques and is much less sensitive to the initial geometric disparity between the two images to be registered.

Citation Download Citation

Mark R. Pickering, Yi Xiao, and Xiuping Jia "Registration of multi-sensor remote sensing imagery by gradient-based optimization of cross-cumulative residual entropy", Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660U (5 May 2008); https://doi.org/10.1117/12.777016

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