By Topic

Neural network-based cloud classification on satellite imagery using textural features

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
$31 $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)
Bin Tian ; Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA ; Azimi-Sadjadi, M.R. ; Haar, T.H.V. ; Reinke, D.

Automatic cloud classification of satellite imagery can be of great help to meteorological studies. A neural network-based cloud classification system is developed and introduced. Several image transformation schemes such as wavelet transform (WT) and singular value decomposition (SVD) are used to extract the salient textural feature of the data and is then compared with those of the well-known gray-level co-occurrence matrix (GLCM) approach. Two different neural network paradigms namely the probability neural network (PNN) and the unsupervised Kohonen (1990) self-organized feature map (SOM) are chosen and examined. The performance of the proposed cloud classification system is benchmarked on the Geostationary Operational Environmental Satellite (GOES) 8 data set and promising results have been achieved

Published in:

Image Processing, 1997. Proceedings., International Conference on  (Volume:3 )

Date of Conference:

26-29 Oct 1997