Remote sensing classification method for binary tree multi-category support vector machines

Abstract

The invention relates to a remote sensing classification method for binary tree multi-category support vector machines, and belongs to a remote sensing classification method. The method comprises the following steps of: dividing all classes into two subclasses; further dividing each subclass into two secondary subclasses; and repeating the process until a separate class is obtained. Therefore, a binary classification tree consisting of a series of support vector machines is finally obtained to complete the construction of a binary tree multi-category support vector machine classifier; in the classification process, classification errors appear in a classifier away from a root node, and a class with the maximum JM distance is separated first; and the established binary tree multi-category support vector machine classifier is utilized to classify remote sensing images. The invention has the advantages that: the classification speed of the method is high; the classification accuracy is superior to that of the common multi-category support vector machine classification method and the traditional classifier; and advantages of class separability and the binary tree support vector machines are integrated, so the remote sensing images can be quickly classified and the classification accuracy can be improved.

Description

technical field [0001] The invention relates to a remote sensing classification method, in particular to a binary tree multi-class support vector machine remote sensing classification method. Background technique [0002] The Support Vector Machine algorithm (Support Vector Machine, SVM) is a machine learning algorithm based on statistical learning theory, which uses the Structural Risk Minimization (SRM) criterion to minimize the sample error while reducing the model generalization error. upper bound to improve the generalization ability of the model. For the application research of SVM in hyperspectral remote sensing classification, many scholars in the world have carried out in-depth research and analysis. Foody mainly studies the impact of small samples on SVM classification during hyperspectral classification. Camps-Valls et al. proposed that fuzzy Sigmoid kernel for remote sensing classification can achieve higher classification accuracy, and has a lower computationa...

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Problems solved by technology

Although the second type of method looks simple, there are far more variables in the process of solving the optimization problem than the first type of method, the training speed is not as good as the first type of method, and the classification accuracy is not dominant.

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