Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
138 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 1, JANUARY 1999 A Study of Cloud Classification with Neural
 

Summary: 138 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 1, JANUARY 1999
A Study of Cloud Classification with Neural
Networks Using Spectral and Textural Features
Bin Tian, Mukhtiar A. Shaikh, Mahmood R. Azimi-Sadjadi, Senior Member, IEEE,
Thomas H. Vonder Haar, and Donald L. Reinke
Abstract--The problem of cloud data classification from satel-
lite imagery using neural networks is considered in this paper.
Several image transformations such as singular value decompo-
sition (SVD) and wavelet packet (WP) were used to extract the
salient spectral and textural features attributed to satellite cloud
data in both visible and infrared (IR) channels. In addition, the
well-known gray-level cooccurrence matrix (GLCM) method and
spectral features were examined for the sake of comparison. Two
different neural-network paradigms namely probability neural
network (PNN) and unsupervised Kohonen self-organized feature
map (SOM) were examined and their performance were also
benchmarked on the geostationary operational environmental
satellite (GOES) 8 data. Additionally, a postprocessing scheme
was developed which utilizes the contextual information in the
satellite images to improve the final classification accuracy. Over-

  

Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University

 

Collections: Computer Technologies and Information Sciences