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

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.