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Proceedings Paper

Reduced memory multiscale fusion for combined topographic and bathymetric data
Author(s): Sweungwon Cheung; Hojin Jhee; Kenneth Clint Slatton
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Paper Abstract

The multiscale Kalman smoother (MKS) is a globally optimal estimator for fusing remotely sensed data. The MKS algorithm can be readily parallelized because it operates on a Markov tree data structure. However, such an implementation requires a large amount of memory to store the parameters and estimates at each scale in the tree. This becomes particularly problematic in applications where the observations have very different resolutions and the finest scale data are sparse or aggregated. Such cases commonly arise when fusing data to capture both regional and local structure. In this work, we develop an efficient MKS algorithm and apply it to the fusion of topographic and bathymetric elevation data.

Paper Details

Date Published: 28 March 2005
PDF: 10 pages
Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); doi: 10.1117/12.604259
Show Author Affiliations
Sweungwon Cheung, Univ. of Florida (United States)
Hojin Jhee, Univ. of Florida (United States)
Kenneth Clint Slatton, Univ. of Florida (United States)


Published in SPIE Proceedings Vol. 5813:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005
Belur V. Dasarathy, Editor(s)

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