An Information-Theoretic Approach to Georegistration of Digital Elevation Maps

Georegistration of digital elevation maps is a vital step in fusing sensor data. In this paper, we present an entropic registration method using Morse singularities. The core idea behind our proposed approach is to encode an elevation map into a set of Morse singular points. Then an information-theoretic dissimilarity measure between the Morse features of the target and the reference maps is maximized to bring the elevation data into alignment. We also show that maximizing this divergence measure leads to minimizing the total length of the joint minimal spanning tree of both elevation data maps. Illustrating experimental results are presented to show the robustness and the georegistration accuracy of the proposed approach.

[1]  Jianhua Lin,et al.  Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.

[2]  A. Ben Hamza,et al.  Nonextensive information-theoretic measure for image edge detection , 2006, J. Electronic Imaging.

[3]  J. Astola,et al.  Information divergence measures-for detection of borders between coding and noncoding DNA regions using recursive entropic segmentation , 2003, IEEE Workshop on Statistical Signal Processing, 2003.

[4]  Lyndon S. Hibbard,et al.  Region segmentation using information divergence measures , 2003, Medical Image Anal..

[5]  Guy Marchal,et al.  Multi-modality image registration by maximization of mutual information , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[6]  Yun He,et al.  A generalized divergence measure for robust image registration , 2003, IEEE Trans. Signal Process..

[7]  Masaki Hilaga,et al.  Topological Modeling for Visualization , 1997 .

[8]  Tosiyasu L. Kunii,et al.  Algorithms for Extracting Correct Critical Points and Constructing Topological Graphs from Discrete Geographical Elevation Data , 1995, Comput. Graph. Forum.

[9]  Alfred O. Hero,et al.  Applications of entropic spanning graphs , 2002, IEEE Signal Process. Mag..

[10]  Paul A. Viola,et al.  Alignment by Maximization of Mutual Information , 1997, International Journal of Computer Vision.

[11]  A. Rényi On Measures of Entropy and Information , 1961 .

[12]  Anuj Srivastava,et al.  Stochastic models for capturing image variability , 2002, IEEE Signal Process. Mag..

[13]  C. R. Rao,et al.  On the convexity of some divergence measures based on entropy functions , 1982, IEEE Trans. Inf. Theory.

[14]  Mert R. Sabuncu,et al.  Gradient based optimization of an EMST image registration function , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[15]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[16]  C. Tsallis Possible generalization of Boltzmann-Gibbs statistics , 1988 .

[17]  S. M. Ali,et al.  A General Class of Coefficients of Divergence of One Distribution from Another , 1966 .

[18]  Jan Havrda,et al.  Quantification method of classification processes. Concept of structural a-entropy , 1967, Kybernetika.

[19]  Josiane Zerubia,et al.  Image retrieval and indexing: a hierarchical approach in computing the distance between textured images , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[20]  David J. Brady,et al.  Information theory in optoelectronic systems: introduction to the feature , 2000 .