Neural Network-Based Hyperspectral Algorithms

Abstract

The long-term goal of our effort is development of robust numerical inversion algorithms, which will retrieve inherent optical properties of the water column as well as depth, and bottom type information from remotely sensed hyperspectral data sets of littoral regions. We have two primary objectives; 1) using a combination of in-situ and model data of water column variables (IOP's, depth, bottom type, upwelling radiance, etc.) a neural network non-linear function approximation model will be used to establish the inverse relationship between upwelling surface radiance and the water column variables, 2) validate the resulting inversion algorithms with in-situ data and provide estimates of the error bounds associated with the inversion algorithm.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 1999
Accession Number
ADA630759

Entities

People

  • Juanita Sandidge
  • Walter F. Smith Jr.

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Data Sets
  • Errors
  • High Resolution
  • Inversion
  • Military Research
  • Models
  • Network Architecture
  • Networks
  • Neural Networks
  • Optical Properties
  • Optics
  • Overflight
  • Radiance
  • Remote Sensing
  • Surveys
  • Training

Readers

  • Coastal Oceanography
  • Neural Network Machine Learning.
  • Wave Propagation and Nonlinear Chaotic Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks