It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection.In this paper,a new method is presented to capture the contour edge,texture and geometric structure of clo...It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection.In this paper,a new method is presented to capture the contour edge,texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm.More abundant texture information is extracted.Cloud images can be obtained a multiscale and multidirection decomposition.The coefficient matrix from Contourlet transform of ground nephogram is calculated.The energy,mean and variance characteristics calculated from coefficient matrix are composed of the feature information.The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis.Then Support Vector Machines(SVM)classifier is used to classify according to the frequency information of the trend graph of data series.It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.展开更多
The texture of ground-based nephogram is abundant and multiplicity.Many cloud textures are not as clear as artificial textures.A nephogram enhancement algorithm based on Adaptive Fractional Differential is established...The texture of ground-based nephogram is abundant and multiplicity.Many cloud textures are not as clear as artificial textures.A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image.Grunwald-Lentikov(G-L)and Grunwald-Lentikov(R-L)fractional differential operators are applied to the enhancement algorithm of ground-based nephogram.An operator mask based on adaptive differential order is designed.The corresponding mask template is used to process each pixel.The results show that this method can extract image texture and edge details and simplify the process of differential order selection.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41775165,41305137,41706109,41475022).
文摘It is important to extract texture feature from the ground-base cloud image for cloud type automatic detection.In this paper,a new method is presented to capture the contour edge,texture and geometric structure of cloud images by using Contourlet and the power spectrum analysis algorithm.More abundant texture information is extracted.Cloud images can be obtained a multiscale and multidirection decomposition.The coefficient matrix from Contourlet transform of ground nephogram is calculated.The energy,mean and variance characteristics calculated from coefficient matrix are composed of the feature information.The frequency information of the data series from the feature vector values is obtained by the power spectrum analysis.Then Support Vector Machines(SVM)classifier is used to classify according to the frequency information of the trend graph of data series.It is shown that altocumulus and stratus with different texture frequencies can be effectively recognized and further subdivided the types of clouds.
基金This work is supported by the National Natural Science Foundation of China(Grant No.41775165)Guangxi Key Laboratory of Automatic Detecting Technology and Instruments(YQ21207)the Qinglan Project of Jiangsu Province.
文摘The texture of ground-based nephogram is abundant and multiplicity.Many cloud textures are not as clear as artificial textures.A nephogram enhancement algorithm based on Adaptive Fractional Differential is established to extract the natural texture of visible ground-based cloud image.Grunwald-Lentikov(G-L)and Grunwald-Lentikov(R-L)fractional differential operators are applied to the enhancement algorithm of ground-based nephogram.An operator mask based on adaptive differential order is designed.The corresponding mask template is used to process each pixel.The results show that this method can extract image texture and edge details and simplify the process of differential order selection.
