Automated Rain-Rate Classification of Satellite Images Using Statistical Pattern Recognition

Author

Lee, Bonita G. ; Chin, Roland T. ; Martin, David W.

Author_Institution

Department of Electrical and Computer Engineering, University of Wisconsin, Madison, 53706

Issue

3

fYear

1985

fDate

5/1/1985 12:00:00 AM

Firstpage

315

Lastpage

324

Abstract

This paper describes an automated procedure to determine rain rates in visible and infrared satellite images by means of statistical pattern recognition. Using brightness and textural features extracted from the images, the procedure classifies 8 km X 8 km windows of data into one of three classes of rain rate: none, light, and heavy. The training process utilizes both weather radar and cloud-development information derived from image sequences. Images from three different days were tested and classification accuracies of 70 percent or better were obtained. An automated scheme of this type has the potential to greatly speed the process of producing an estimate of rainfall from satellite imagery with little compromise in overall accuracy.

Keywords

Brightness; Data mining; Feature extraction; Image sequences; Infrared imaging; Meteorological radar; Pattern recognition; Rain; Satellites; Testing; Statistical pattern recognition; classification; feature extraction; rainfall retrieval; remote sensing data; satellite rain estimation;

fLanguage

English

Journal_Title

Geoscience and Remote Sensing, IEEE Transactions on

Publisher

ieee

ISSN

0196-2892

Type

jour

DOI

10.1109/TGRS.1985.289534

Filename

4072298