By Topic

An automatic cloud-masking system using backpro neural nets for AVHRR scenes

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
J. A. T. Arriaza ; Dept. de Lenguajes y Computacion, Univ. de Almeria, Spain ; F. G. Rojas ; M. P. Lopez ; M. Canton

The automation of pattern recognition in the field of remote sensing involves several preprocessing steps to remove noise and nonuseful data. When infrared data are used to obtain either ocean or land information, cloud pixels must first be identified and eliminated from the image, because cloud contamination is the main producer of errors in deriving sea surface temperatures from remotely sensed data. Cloud masking is usually tackled as a statistical classification problem using threshold or texture-based information from satellite scenes. We attempt to construct an automatic cloud-masking system which uses heuristic knowledge about cloud features in Advanced Very High Resolution Radiometer scenes and artificial neural networks as classifiers. This system could be used as a preprocessing step in a future automatic oceanic feature identification system now being developed for the North Atlantic Ocean. The system has been compared with other traditional cloud mask methods to determine its accuracy.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:41 ,  Issue: 4 )