Skip to main content

A hybrid histogram/neural network classifier for creating global cloud masks

Buy Article:

$55.00 plus tax (Refund Policy)

Abstract. A histogram method is used to identify the features which are used to classify the scene elements, and a hierarchical neural network with 'don`t care` nodes is used to perform the actual classification. Although the ultimate goal is to produce a classifier capable of detecting clouds anywhere in the world, this preliminary system is limited to classifications in three distinct ecosystems: the polar region, desert terrain, and scenes of biomass burning in South America. Classification accuracies of 97.5 per cent for desert data, 98 per cent for polar data, and 99.2 per cent for biomass burning data were achieved. More importantly, manual analysis of classified scenes which had not been previously seen by the classifier show 'very good` to 'excellent` correspondence between the computer generated classifications and the human expert classifications.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Data/Media
No Metrics

Document Type: Research Article

Publication date: 1997-03-10

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more