On the Application of BEMD and Tamura Textural Feature for Recognizing Ground-based Cloud
Ground-based cloud recognition plays an essential role for automatic cloud observation. In particular, the recognition of clouds is remarkably challenging because that the shape, size, and composition of cloud is extremely variable under different atmospheric conditions. A new method is proposed to extract texturral feature using Bidimensional Empirical Mode Decomposition(BEMD) and Tamura textural analysis.. Cloud was decomposed into several IMFs by BEMO. Radial basis function polynomial interpolation was applied to construct the envelope. Then the number of zero-crossing, means and standard deviation of the amplitude in each IMFs were selected as the eigenvector for training processing. And Tamura textural feature analysis was used to extract the feature of directionality. Characteristics of the sample database cloud was established by synthesizing the two normalized eigenvector. The same method was applied to the images to be identified, then the images were categorized compared with the eigenvector of sample database by the average sample method. The simulated experiments show that the ground-based cloud can be recognized effectively by new method.
bidimensional empirical mode decomposition(BEMD) intrinsic mode functions(IMFs) ground-based cloud recognition of cloud type the average sample
CHEN Xiao-ying SONG Ai-guo Yuan Wen ZHEN Jun-jie LI Jian-qing
School of Instrument Science and Engineering Southeast University Nanjing, China Institute of meteor School of Instrument Science and Engineering Southeast University Nanjing, China No.94878 of PLA Wuhu, China Institute of meteorology PLA Univ.of Sci.& Tech.Nanjing, China
国际会议
太原
英文
61-65
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）