Hyperspectral image classification method based on affinity propagation clustering and sparse multiple kernel learning

Abstract

The invention discloses a hyperspectral image classification method based on affinity propagation clustering and sparse multiple kernel learning to mainly solve a problem that technologies of the prior art are low in hyperspectral image classification performance. A technical solution is that training samples in all wave bands are used for constructing a kernel matrix set, an affinity propagation method is used for clustering, and a kernel matrix subset which is high in discriminability and low in redundancy is selected; by using the kernel matrix subset which is selected, kernel weight and support vector coefficients can be learned via a sparse-constrained multiple kernel learning method; unknown hyperspectral images can be classified via a learned classifier. According to the hyperspectral image classification method, the multiple kernel learning method is adopted, a plurality kinds of kernels that are different in function and parameter are used, complex hyperspectral data having changeable local distribution can be processed, high-precision hyperspectral image classification results can be obtained, and the hyperspectral image classification method can be applied to discrimination of surface features in the fields of agriculture monitoring, geological exploration, disaster environment assessment and the like.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral image classification method, which can be used to distinguish and distinguish ground objects in the fields of agricultural monitoring, geological exploration, disaster environment assessment and the like. Background technique [0002] Over the past thirty years, with the development of science and technology, remote sensing technology has also been greatly developed. The hyperspectral remote sensing system occupies an extremely important position in the field of earth observation. Hyperspectral remote sensing technology is a new type of remote sensing technology developed on the basis of multispectral remote sensing technology. Compared with multispectral images, hyperspectral images can provide richer spectral information of ground features. Hyperspectral images can obtain approximately continuous spectral information of targ...

AI Technical Summary

Problems solved by technology

However, due to the huge kernel size, the multi-kernel learning method has high computational complexity, and it is difficult to efficiently process complex hyperspectral image data.

In addition, in hyperspectral image data, when the number of labeled samples is limited, a large number of spectral bands will cause the Hughes phenomenon, resulting in the degradation of the classification performance of multi-kernel learning.

[0006] In summary, the existing multi-kernel learning classification methods are directly used for hyperspectral image classification, but there are problems of excessive kernel size and poor classification accuracy.

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