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Proceedings Paper

Analysis and classification of remote-sensed cloud imagery
Author(s): John S. DaPonte; Joseph N. Vitale; George Tselioudis; William B. Rossow
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Paper Abstract

The objective of this research is to automate the classification of clouds from satellite images providing a method for studying their properties over time. Analysis was applied to the International Satellite Cloud Climatology Project (ISCCP) low resolution (2.5 degrees per pixel) database for January 1987. Our approach differs from earlier studies by taking advantage of cloud top pressure and optical thickness from the ISCCP database, providing more accurate measures of cloud height with less dependency on the sun's angle of illumination. A total of 365 regions of interest (ROI), each classified Storm or Non Storm were used in the analysis. The algorithms used were Backpropagation Artificial Neural Network and Nearest Neighbor Pattern Classification. Each ROI was assigned on identification number between 1 and 365. One third of the ROIs were randomly selected for testing using a random number generator and the remaining ROIs were assigned to be training set. This process was repeated 29 times resulting in a mean classification error of 5.76% for the nearest neighbor algorithm and 3.97% for the backpropagation neural network.

Paper Details

Date Published: 4 April 1997
PDF: 5 pages
Proc. SPIE 3077, Applications and Science of Artificial Neural Networks III, (4 April 1997); doi: 10.1117/12.271516
Show Author Affiliations
John S. DaPonte, Southern Connecticut State Univ. (United States)
Joseph N. Vitale, Southern Connecticut State Univ. (United States)
George Tselioudis, Columbia Univ. and NASA Goddard Institute for Space Studies (United States)
William B. Rossow, NASA Goddard Institute for Space Studies (United States)

Published in SPIE Proceedings Vol. 3077:
Applications and Science of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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