Bathymetry from Hyperspectral Sensors: A Preliminary Analysis of the Problem

Abstract

Water depth, bottom reflectance, inherent optical properties of the water column (scattering, absorption, and fluorescence), and illumination conditions combine to determine the upwelling spectral radiance of coastal waters. If these complex optical relationships could be quantified, it would be possible to extract coastal information from spectral radiance data. We use data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to begin to characterize these relationships as a first step toward development of algorithms for retrieval of water depth from hyperspectral imagery. Data are analyzed for two areas: the western coast of Florida in the Tampa Bay area and the Florida Keys between Upper Matecumbe and Plantation Keys. A neural network approach has been used to demonstrate retrieval of reasonable depths from spectral radiance over a depth range of 0 to 6 m. The ability of the neural network to generalize, producing algorithms with some degree of universality among diverse coastal environments, has been investigated in a preliminary fashion.

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Document Details

Document Type
Technical Report
Publication Date
May 20, 1999
Accession Number
ADA365138

Entities

People

  • Juanita C. Sandidge
  • Ronald J. Holyer

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Absorption Coefficients
  • Airborne
  • Algorithms
  • Bathymetry
  • Data Sets
  • Deep Water
  • Environment
  • Hyperspectral Imagery
  • Jet Propulsion
  • Neural Networks
  • Optical Properties
  • Optics
  • Radiative Transfer
  • Remote Sensing
  • Scattering
  • Seabed
  • Test Sets

Fields of Study

  • Environmental science

Readers

  • Atmospheric Remote Sensing.
  • Coastal Oceanography

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks