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Proceedings Paper

Identification of optically thin cirrus clouds by automated classification algorithms using nighttime multispectral multisensor meteorological satellite data
Author(s): Keith D. Hutchison; Jerry Mack; Greg Logan; Kenneth R. Hardy; Steven D. Westerman
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Paper Abstract

The accurate identification of clouds in meteorological satellite imagery by automated detection and classification algorithms is critical to environmental remote sensing studies, such as those related to Global Climate Change. Significant improvements in these algorithms were realized with the arrival of multispectral, meteorological satellite imagery, collected by NOAA's advanced very high resolution radiometer (AVHRR). However, deficiencies remained, especially with the positive identification of optically thin cirrus clouds due, in part, to the effects of atmospheric attenuation on cloud signatures caused primarily by variations in water vapor. Thus, the goal of this research was to enhance the accuracy of the automated classification of optically thin cirrus in nighttime, multispectral meteorological satellite imagery through an improved treatment of atmospheric attenuation caused by moisture.

Paper Details

Date Published: 15 September 1993
PDF: 4 pages
Proc. SPIE 1934, Passive Infrared Remote Sensing of Clouds and the Atmosphere, (15 September 1993); doi: 10.1117/12.154908
Show Author Affiliations
Keith D. Hutchison, Lockheed Missiles & Space Co., Inc. (United States)
Jerry Mack, Lockheed Missiles & Space Co., Inc. (United States)
Greg Logan, Lockheed Missiles & Space Co., Inc. (United States)
Kenneth R. Hardy, Lockheed Missiles & Space Co., Inc. (United States)
Steven D. Westerman, Lockheed Missiles & Space Co., Inc. (United States)


Published in SPIE Proceedings Vol. 1934:
Passive Infrared Remote Sensing of Clouds and the Atmosphere
David K. Lynch, Editor(s)

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