Land-based cloud chart recognition method based on classification trees of support vector machine

Abstract

The invention discloses a land-based cloud chart classification method based on classification trees of a support vector machine. The land-based cloud chart classification method comprises the steps as follows: firstly, training samples are selected from land-based cloud charts; secondly, a Gabor filter bank is utilized to perform frequency domain decomposition on the training samples; thirdly, sorting histogram spectrum characteristic vectors and interested operator characteristic vectors of each filter image are extracted, so that training sample sets can be obtained; fourthly, K types of the training samples in the training sample sets are clustered to form ni types according to the specified clustering number, and then centers of the ni types are used as training samples of the ni types, so that new training sample sets can be obtained; fifthly, a classification tree model based on a sorter of the support vector machine is established; and sixthly, the samples in T are classified, and the land-based cloud charts can be classified. The land-based cloud chart classification method considers various characteristic values among different cloud genera based on the land-based cloud charts, combines an SVM (Support Vector Machine) learning algorithm with a classification tree algorithm so as to classify and recognize a plurality of types of the cloud charts automatically, and has the advantages of stronger robustness, higher classification speed and high classification accuracy rate.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and particularly relates to a method for recognizing ground-based cloud images by utilizing various features and a classification tree based on a support vector machine. Background technique [0002] Clouds play an important role in atmospheric radiative transfer. The shape, distribution, quantity and changes of clouds indicate the state of atmospheric motion. Therefore, the realization of automatic quantitative observation of clouds is of great significance for weather forecasting and flight support. At present, the commonly used method is to use the satellite cloud images obtained by meteorological satellites to identify the types of cloud clusters. Experts interpret the cloud clusters on the satellite cloud images and then use them with other forecasting tools. This manual analysis method has a certain degree of subjectivity and low efficiency, so it is inevitable to miss a lot of useful in...

AI Technical Summary

Problems solved by technology

Among them, the K-means clustering method is easily affected by the selection of the initial center of the category; the Bayesian method needs to know factors such as the prior probability of each category, and these factors are often unpredictable in actual situations; the neural network method has a slow training speed. Disadvantages such as low classification accuracy when there are many sample categories

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