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Optimizing Machine Learning Algorithms for Hyperspectral Very Shallow Water (VSW) Products
FLORIDA ENVIRONMENTAL RESEARCH INST TAMPA FL
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This one-year effort will focus on the transition of FERIs machine learning algorithms for HyperSpectral Imagery HSI in the VSW into a distributable code set. This will provide a stable code platform for the application and transition of machine learning-based hyperspectral classification techniques into 6.36.4 programs. This work was funded mid-year 2008. Our objective is to focus on three areas of application research and transitions. First, we will transition our machine learning-based algorithms and computer code for the determination of bathymetry, bottom type, and water column Inherent Optical Properties from HyperSpectral Imagery HSI into a deliverable Message Passing Interface MPI program that may be easily used by other research and military operators. Second, we will use this program to determine the impacts of the granularity of the classification database on the inversion bathymetry, bottom type, and IOPs. Third, we will move beyond the use of single pixel HSI inversion to the use of spatial context-filtering to remove pixel-topixel noise inherent in the HSI data.
APPROVED FOR PUBLIC RELEASE