Summary: 1098 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 41, NO. 5, MAY 2003
A Multichannel Temporally Adaptive System
for Continuous Cloud Classification
From Satellite Imagery
Kishor Saitwal, Student Member, IEEE, Mahmood R. Azimi-Sadjadi, and Donald Reinke
Abstract--A two-channel temporal updating system is pre-
sented, which accounts for feature changes in the visible and
infrared satellite images. The system uses two probabilistic neural
network classifiers and a context-based predictor to perform con-
tinuous cloud classification during the day and night. Test results
for 27 h of continuous classification and updating are presented on
a sequence of Geostationary Operational Environmental Satellite
8 images. Further test results of the system on two new sets of
data with 1­2 weeks time difference are also presented that show
the potential of this system as an operational continuous cloud
classification system.
Index Terms--Cloud classification, multispectral satellite
imaging, probabilistic neural networks.
I. INTRODUCTION
THE GEOSTATIONARY Operational Environmental

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

Collections: Computer Technologies and Information Sciences