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

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.