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 r...
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| Veröffentlicht in: | IEEE transactions on geoscience and remote sensing 2003-05, Vol.41 (5), p.1098-1104 |
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| container_title | IEEE transactions on geoscience and remote sensing |
| container_volume | 41 |
| creator | Saitwal, K. Azimi-Sadjadi, M.R. Reinke, D. |
| description | 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. |
| doi_str_mv | 10.1109/TGRS.2003.813550 |
| format | Article |
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| ispartof | IEEE transactions on geoscience and remote sensing, 2003-05, Vol.41 (5), p.1098-1104 |
| issn | 0196-2892 1558-0644 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Adaptive systems Applied geophysics Classification Clouds Earth sciences Earth, ocean, space Exact sciences and technology Frequency High-resolution imaging Infrared Infrared imaging Internal geophysics Neural networks Optical imaging Probability theory Satellites System testing Temporal logic Weather forecasting |
| title | A multichannel temporally adaptive system for continuous cloud classification from satellite imagery |
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