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Proceedings Paper

Blob-level active-passive data fusion for Benthic classification
Author(s): Joong Yong Park; Hemanth Kalluri; Abhinav Mathur; Vinod Ramnath; Minsu Kim; Jennifer Aitken; Grady Tuell
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Paper Abstract

We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.

Paper Details

Date Published: 11 May 2012
PDF: 8 pages
Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839009 (11 May 2012); doi: 10.1117/12.918646
Show Author Affiliations
Joong Yong Park, Optech International, Inc. (United States)
Hemanth Kalluri, Optech International, Inc. (United States)
Abhinav Mathur, Optech International, Inc. (United States)
Vinod Ramnath, Optech International, Inc. (United States)
Minsu Kim, Optech International, Inc. (United States)
Jennifer Aitken, Optech International, Inc. (United States)
Grady Tuell, Georgia Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 8390:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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