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Cloud Classification Based on Geostationary Meteorological Satellite Imagery

Author: LiuYang
Tutor: HanLei
School: Ocean University of China
Course: Signal and Information Processing
Keywords: Cloud classification Support Vector Machine Threshold method Meteorological satellite
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 59
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Abstract


Radiation characteristics and distribution of various types of clouds , largely affect the accuracy of weather forecasts , climate monitoring , effectiveness and global climate change . Since meteorological satellites produce , people began activities for analysis of satellite cloud , cloud images in various types of cloud classification and recognition has been the research focus of the field of remote sensing . Firstly, the review of the introduction of cloud classification study at home and abroad , and briefly describes the radiation characteristics of the various types of clouds and cloud characteristics . For FY-2C satellite data , the paper also details the the csv format positioning satellite data and the calibration method . In this paper, the threshold for our Beijing-Tianjin region early summer afternoon cumulus identify some of these early cumulus clouds may produce severe convective weather in a short period of time . For non - convective cloud clusters , such as volume -like clouds or stratiform clouds , strong convective weather or convective activity has occurred late cumulonimbus can be excluded from the forecast range . This reduces the amount of the subsequent prediction operator , shortening the time of the forecast . Three typical case specific analysis paper will discuss the characteristics and performance of the threshold method of cloud classifier . In order to obtain a wider variety of cloud types , the paper uses support vector machines (Support Vector Machines, SVM) classification of satellite cloud . First of all , one-against-one classification structure used in the experiment , and the selection of radial basis function as the kernel function of SVM classifier . Cloud classification results for each data category labels refer to the National Satellite Meteorological Center , the experimental data divided the surface mixed altostratus or nimbostratus pixel cirrostratus dense cirrus , cumulonimbus stratocumulus or altocumulus 7 . By consulting the relevant literature , selected 9 features a feature vector to describe the clouds . Third, learn the bisection method , setting several groups of parameters C and gamma SVM classifier training data to train a classifier for each set of parameters . Finally, the use of training from the SVM classification model to classify the test data , the classification results are displayed . Articles Select the classification result of the preferred groups of parameter combinations were analyzed and evaluated by comparison of the results , and noted that the direction of improvement .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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