Optimizing Machine Learning Algorithms for Hyperspectral Very Shallow Water (VSW) Products

Abstract

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-to-pixel noise inherent in the HSI data.

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Document Details

Document Type
Technical Report
Publication Date
Jun 30, 2009
Accession Number
ADA504929

Entities

People

  • William Paul Bissett

Organizations

  • Florida Environmental Research Institute

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Classification
  • Data Science
  • Databases
  • Environmental Protection
  • Fungi
  • Hyperspectral Imagery
  • Information Science
  • Learning
  • Machine Learning
  • Optical Properties
  • Optics
  • Remote Sensing
  • Shallow Water
  • Signal Processing
  • Water

Readers

  • Acoustical Oceanography.
  • Database Systems and Applications
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML