11 May 2012 Blob-level active-passive data fusion for Benthic classification
Author Affiliations +
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.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Joong Yong Park, Joong Yong Park, Hemanth Kalluri, Hemanth Kalluri, Abhinav Mathur, Abhinav Mathur, Vinod Ramnath, Vinod Ramnath, Minsu Kim, Minsu Kim, Jennifer Aitken, Jennifer Aitken, Grady Tuell, 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

Back to Top