Hyperspectral image classification multi-kernel learning method capable of maximizing class separability

Abstract

The invention relates to a hyperspectral image classification multi-kernel learning method capable of maximizing the class separability, and relates to multi-kernel learning model solving. The invention aims to solve problems that multi-kernel model solving is not considered to be combined with subsequent classification application and the solving efficiency is low in an existing multi-kernel learning method applied to hyperspectral image classification. The hyperspectral image classification multi-kernel learning method is implemented according to the following steps: step one, training samples and test samples are acquired from a given input hyperspectral image data set; step two, a base kernel matrix Km in the multi-kernel learning model is constructed by using the training sample set Xtrain={xi}<i=1><N>, and a base kernel matrix set {Km}<m=1><M>={K1, K2,..., Km} is acquired; step three, the within-class discrete degree and the between-class discrete degree of the data set in a Hilbert kernel space are measured by using the base kernel matrix set; and step four, the class separability is measured according to a maximum class interval principle on the basis of the within-class discrete degree and the between-class discrete degree, and the weight of the base kernel is solved by taking the maximum class separability as a solving principle of the multi-kernel learning model. The hyperspectral image classification multi-kernel learning method is applied to the field of pattern recognition.

Description

technical field [0001] The invention belongs to the field of pattern recognition and relates to solving multi-core learning models. Background technique [0002] Existing multi-kernel learning methods can be summarized into three categories. The first type is a multi-core learning method based on fixed criteria. This type of method uses fixed criteria such as addition and multiplication to combine base cores during the multi-core learning process, without considering the similarity measurement characteristics of each base core and without using any optimization criteria. Hence the classification performance is lower. The second type is the single-step optimization multi-core learning method. This type of method solves the weight parameters of the base core in the multi-core model and the parameters in the classifier at the same time, so the calculation amount is very large, the learning efficiency is very low, and its classification performance is relatively strong. The th...

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Problems solved by technology

[0004] In order to solve the problem that the existing multi-kernel learning method applied to hyperspectral image classification does not consider the combination of multi-kernel model solving and subsequent classification applications and the problem of low solution efficiency, the present invention proposes a criterion based on maximizing category separability multi-core learning method

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