15 September 1993 Identification of optically thin cirrus clouds by automated classification algorithms using nighttime multispectral multisensor meteorological satellite data
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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.
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Keith D. Hutchison, Jerry Mack, Greg Logan, Kenneth R. Hardy, Steven D. Westerman, "Identification of optically thin cirrus clouds by automated classification algorithms using nighttime multispectral multisensor meteorological satellite data", Proc. SPIE 1934, Passive Infrared Remote Sensing of Clouds and the Atmosphere, (15 September 1993); doi: 10.1117/12.154908; https://doi.org/10.1117/12.154908
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