11 May 2012 Blob-level active-passive data fusion for Benthic classification
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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.
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Joong Yong Park, Hemanth Kalluri, Abhinav Mathur, Vinod Ramnath, Minsu Kim, Jennifer Aitken, Grady Tuell, "Blob-level active-passive data fusion for Benthic classification", Proc. SPIE 8390, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII, 839009 (11 May 2012); doi: 10.1117/12.918646; https://doi.org/10.1117/12.918646
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