| dc.description.abstract | Abstract
Cloud is one of the very important factors influencing the weather system of the earth. Since the cloud type in attitude fluctuates multifariously, discussions on the cloud parameter have to be conducted firstly in this thesis. These include “total cloud amount”, “cloud speed”, “cloud thickness”, and “cloud high”.
The classification of cloud high in traditional methods is performed by the estimating of human eyes, radiosunde, and liadar detection …etc. In addition, we can apply satellite images to collocate with remote sensing theory to implement the cloud classifies. Since satellites can offer the information on a large scale to observe the materials efficiently, a new generation’’s MODIS (Moderate-resolution Imaging Spectroradiometer) is used in this thesis to obtain satellite image data to conduct the “cloud high” classification. The observation wavelength range of MODIST is from visible, near IR, middle IR to thermal IR, which has 36 channels suitable for analyzing and accomplishing the application.
In this thesis, 17 channels that are frequently used in cloud distinguishing are adopted, and the “Back Propagation Networks” (BPN) is employed for classification purpose. Since it is difficult to evaluate the results of cloud high classification, the cloud top temperature and cloud
high results generated from remote sensing theory are used to obtain the goal value of classification model outputs to distinguish the results for verification and consulting purposes. In our experiments, image data of different seasons were collected to test and compare the performance. Experimental results demonstrate that the proposed method is feasible and achieves very high accuracy rate in cloud classification | en_US |