A Spectrum-Matching and Look-Up-Table Approach to Interpretation of Hyperspectral Remote-Sensing Data

Abstract

A spectrum-matching and look-up-table (LUT) methodology has been developed and evaluated for extracting environmental information from remotely sensed hyperspectral imagery. The LUT methodology works as follows. First, a database of remote-sensing reflectance (R(sub rs) spectra corresponding to various water depths, bottom reflectance spectra, and water-column inherent optical properties (IOPs) is constructed using a special version of the Hydrolight radiative transfer numerical model. Second, the R(sub rs) spectrum for a particular image pixel is compared with each spectrum in the database and the closest match to the image spectrum is found using a least-squares minimization. The environmental conditions in nature are then assumed to be the same as the input conditions that generated the closest-matching Hydrolight-generated spectrum in the database. The LUT methodology has been evaluated by application to an Ocean PHILLS (Portable Hyperspectral Imaging Low-Light Spectrometer) image acquired near Lee Stocking Island, Bahamas, on 17 May 2000. The LUT-retrieved bottom depths were on average within 5% and 0.5 m of independently obtained acoustic depths. The LUT-retrieved bottom classification was in qualitative agreement with diver and video spot classification of bottom types, and the LUT-retrieved IOPs were consistent with IOPs measured at nearby times and locations.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2004
Accession Number
ADA419452

Entities

People

  • Curtis D. Mobley

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Absorption Spectra
  • Agreements
  • Backscattering
  • Classification
  • Crossings
  • Databases
  • Detection
  • Environment
  • Hyperspectral Imagery
  • Optical Properties
  • Optics
  • Particles
  • Radiative Transfer
  • Reflectance
  • Remote Sensing
  • Scattering
  • Spectra

Readers

  • Coastal Oceanography
  • Image Processing and Computer Vision.
  • Spectroscopy.