A multichannel temporally adaptive system for continuous cloud classification from satellite imagery

Author

Saitwal, Kishor ; Azimi-Sadjadi, Mahmood R. ; Reinke, Donald

Author_Institution

Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA

Volume

41

Issue

5

fYear

2003

fDate

5/1/2003 12:00:00 AM

Firstpage

1098

Lastpage

1104

Abstract

A two-channel temporal updating system is presented, 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 continuous 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 1-2 weeks time difference are also presented that show the potential of this system as an operational continuous cloud classification system.

Keywords

adaptive systems; atmospheric techniques; clouds; image classification; remote sensing; GOES 8; Geostationary Operational Environmental Satellite; infrared satellite images; multichannel temporally adaptive system; multispectral satellite imaging; operational continuous cloud classification system; probabilistic neural network classifiers; satellite imagery; two-channel temporal updating system; visible satellite images; Adaptive systems; Clouds; Frequency; High-resolution imaging; Infrared imaging; Neural networks; Optical imaging; Satellites; System testing; Weather forecasting;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/TGRS.2003.813550

Filename

1206708