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Proceedings Paper

Registration of multi-sensor remote sensing imagery by gradient-based optimization of cross-cumulative residual entropy
Author(s): Mark R. Pickering; Yi Xiao; Xiuping Jia
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Paper 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.

Paper Details

Date Published: 5 May 2008
PDF: 10 pages
Proc. SPIE 6966, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV, 69660U (5 May 2008); doi: 10.1117/12.777016
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. 6966:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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