Automated Processing of Satellite Imagery Data: Test of a Spectral Classifier.

Abstract

This report details the development and testing of algorithms for automated classification of Very High Resolution (VHR) cloud imagery from orbiting weather satellites. The imagery data are first processed into average wave number spectra. The spectral features are then input to a Bayesian quadratic discriminant classifier. The classifier is designed to work with VHR visual and IR data, but was tested here on VHR visual data alone. Comparison of the classifier to a blind processor using a priori probabilities shows accuracy of classification appreciably better than chance. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1979
Accession Number
ADA068663

Entities

People

  • Elliott S. Blackman
  • Ronald M. Pickett

Organizations

  • BBN Technologies

Tags

Communities of Interest

  • Human Systems
  • Materials and Manufacturing Processes
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Air Force
  • Algorithms
  • Artificial Satellites
  • Computations
  • Frequency
  • High Resolution
  • Infrared Radiation
  • Meteorological Satellites
  • Observers
  • Pattern Recognition
  • Power Spectra
  • Satellite Imaging
  • Spectra
  • Two Dimensional
  • United States
  • Vehicles

Readers

  • Computer Vision.
  • Radar Systems Engineering.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • Space