25 September 2003 Study of artificial neural network method for weather and AVHRR thermal data classification
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Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.538869
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China
Abstract
In recent years, the Asian dust storm project was carried out. One of tasks was to study dust rising mechanism in dust source area. Surface temperature condition was regarded as one of the important factors for dust rise. In the study we retrieved surface temperature by using NOAA/AVHRR data. Basedon the published articles, traditionally, split window algorithm was use to deriving surface temperatures in the case of our study area mostly desert area, there was only three field observation data available in Talimu basin, at Dunhuang and Changwu. It was very difficult to validate the results. However, there were 52 county wearther observation stations in the area. The data might be used as import data in artificial neural network calculation. Most success examples of remote sensing data classification by using neural networks were in the condition of network training and classifying in the same types of data such as spatial data. For the use different data type collected by different techniques system such as satellite system and ground weather observation data to training, to find rule and to direct classification could be more impersonal which was one of the nature of artifical neural network method. In our case 52 weather temperature data were used from 52 observation stations where they were also the same positions for collecting AVHRR 1b data CH2, CH4, CH5 thermal data. Both groups of data were applied as fundamental import data in for artificial neural network calculation. Finally resultant rule was applied for classifying 15000 x 3 pixels in the whole area. The result was more reliable than that of split window not only because uncertainty caused by variations of topography but also it was very difficult to validate in field.
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Hasi Bagan, Hasi Bagan, Jianwen Ma, Jianwen Ma, Zijiang Zhou, Zijiang Zhou, } "Study of artificial neural network method for weather and AVHRR thermal data classification", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.538869; https://doi.org/10.1117/12.538869
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