Neural Network Hyperspectral Algorithms

Abstract

The long-term goal is to develop the scientific and computational basis for neural network-based algorithms for retrieval of inherent optical properties (IOP), water depth, and bottom type from hyperspectral imagery of coastal waters. These algorithms will capitalize on the power of neural networks and their associated "learning" algorithms to define complex nonlinear relationships between spectral radiance and water/bottom properties.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1998
Accession Number
ADA550919

Entities

People

  • Juanita C. Chase
  • Ronald J. Holyer
  • Walter F. Smith

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Absorption Coefficients
  • Abstracts
  • Algorithms
  • Batch Processing
  • Bathymetry
  • Coefficients
  • Data Sets
  • Hyperspectral Imagery
  • Internal Waves
  • Military Research
  • Neural Networks
  • Observation
  • Optical Properties
  • Radiance
  • Radiative Transfer
  • Remote Sensing
  • Training

Readers

  • Coastal Oceanography
  • Neuroscience
  • Operations Research

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks