Hyperspectrum classification method based on composite kernel function

Abstract

The invention provides a hyperspectrum classification method based on a composite kernel function. The hyperspectrum classification method comprises the steps of inputting a set of hyperspectrum images in N classes, taking a support vector machine as a base classifier, and meanwhile, randomly selecting S samples from every classes of the hyperspectrum images to form a training set and forming a test set with the left samples, determining the change range of each parameter, next, determining the optimal performance parameters, including a penalty factor and a kernel of the support vector machine by virtue of cross validation for K times, constructing a composite kernel function by use of a composite kernel construction policy and training the support vector machine, and circulating for N times by use of the parameters of a support vector machine decision function obtained in the training process to obtain decision function values of which the test set belongs to every classes and to form a matrix as shown in the specification, and then determining multiple classifier policies, namely finding the maximum values of every columns of the matrix as shown in the specification. The hyperspectrum classification method based on the composite kernel function has the characteristics of better description of distribution features of a data set, relatively high classification accuracy and the like. The time taken by parameter optimization of the hyperspectrum classification method is also relatively short in contrast with a traditional multi-kernel learning method.

Description

technical field [0001] The invention relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method based on a new composite kernel function (Hyperspectral Image Classification Based on A New Composite Kernel). Background technique [0002] In recent years, the development of satellite sensors has improved the spatial resolution and spectral resolution, and also shortened the access time, which in turn created the conditions for the development of hyperspectral classification methods. Neural network classifiers, K-nearest neighbor classifiers, Bayesian classifiers, decision tree classifiers, and kernel-based classifiers have been widely used in the hyperspectral field, among which kernel function-based classification methods have received more and more attention. . Support Vector Machine (SVM) is the most typical classification method based on kernel function. When dealing with limited high-dimensional training samples, ...

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Problems solved by technology

However, during the training process, the time consumed by parameter optimization is also huge.

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