Reduced memory multiscale fusion for combined topographic and bathymetric data

Sweungwon Cheung; Hojin Jhee; Kenneth Clint Slatton

doi:10.1117/12.604259

28 March 2005 Reduced memory multiscale fusion for combined topographic and bathymetric data

Sweungwon Cheung, Hojin Jhee, Kenneth Clint Slatton

Proceedings Volume 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005; (2005) https://doi.org/10.1117/12.604259
Event: Defense and Security, 2005, Orlando, Florida, United States

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.

Citation Download Citation

Sweungwon Cheung, Hojin Jhee, and Kenneth Clint Slatton "Reduced memory multiscale fusion for combined topographic and bathymetric data", Proc. SPIE 5813, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2005, (28 March 2005); https://doi.org/10.1117/12.604259

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