A Probabilistic Neural Network Approach to Cloud Classification

Abstract

Automated satellite image interpretation would be useful in many forecasting operations. One aspects of that interpretation, cloud classification, is examined. Ten classes, composed of low, middle, high, and precipitation cloud types plus clear, are used as output nodes in a Probabilistic Neural Network (PNN) approach to classification of data using four Advanced Very High Resolution Radiometer (AVHRR) subscenes. Input to the neural network consists of 12 features that include a mixture of spectral, textural, and physical measures. There measures are selected, using a feature selection routine, from a collection of over 200 features. An overall accuracy of 85.25% is the result. Four classes have agreement of 90% or better. The two classes with the poorest accuracies were presented to the classifier with the smallest sample sizes. An increase in the number of samples should increase the accuracy of the classifier.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1991
Accession Number
ADA247916

Entities

People

  • P. Rabindra
  • R. L. Bankert
  • S. K. Sengupta

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Artificial Satellites
  • Atmospheric Sciences
  • Classification
  • Data Sets
  • Feature Selection
  • Information Science
  • Machine Learning
  • Meteorology
  • Military Research
  • Neural Networks
  • Pattern Recognition
  • Standards
  • Supervised Machine Learning
  • Surface Temperature
  • Training

Readers

  • Atmospheric Remote Sensing.
  • Computer Vision.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Space