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

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3 Author(s)
Lee, B.G. ; Department of Electrical and Computer Engineering, University of Wisconsin, Madison, 53706 ; Chin, R.T. ; Martin, D.W.

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.

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:GE-23 ,  Issue: 3 )