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A multichannel temporally adaptive system for continuous cloud classification from satellite imagery

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3 Author(s)
K. Saitwal ; Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA ; M. R. Azimi-Sadjadi ; D. Reinke

A two-channel temporal updating system is presented, which accounts for feature changes in the visible and infrared satellite images. The system uses two probabilistic neural network classifiers and a context-based predictor to perform continuous cloud classification during the day and night. Test results for 27 h of continuous classification and updating are presented on a sequence of Geostationary Operational Environmental Satellite 8 images. Further test results of the system on two new sets of data with 1-2 weeks time difference are also presented that show the potential of this system as an operational continuous cloud classification system.

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:41 ,  Issue: 5 )