Hyperspectral Image Classification Method Based on Local Cooperative Representation and Neighborhood Information Constraint

Abstract

The invention discloses a hyperspectral image classification method based on local cooperative expression and neighbourhood information constraint, and is mainly used for solving the problem of high computation complexity in the prior art. The hyperspectral image classification method comprises the following realization steps of: (1) forming a neighbourhood matrix by M samples in a neighbourhood of a hyperspectral image tested sample and a tested sample; (2) forming a dictionary by all training samples, and calculating a cooperative expression coefficient matrix of the neighbourhood matrix by the dictionary; (3) calculating 12 norms of each row of the coefficient matrix, selecting N atoms from the dictionary according to large N row marks of the 12 norms to form a sub dictionary, and calculating a cooperative expression coefficient of the tested sample by the sub dictionary; (4) dividing the cooperative expression coefficient and the sub dictionary into n parts according to the number of every class of training samples; and (5) calculating and comparing a residuals between the tested sample and each reconstruction of the n parts, wherein a class mark of the tested sample is corresponding to a subscript of the minimum residual. According to the hyperspectral image classification method disclosed by the invention, the local cooperative expression is used, thus reducing the quantity of the atoms and reducing the computation complexity; and the hyperspectral image classification method can be used for solving the problem of hyperspectral image classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to the classification of hyperspectral images, and can be used for target identification. Background technique [0002] With the development of aerospace hyperspectral remote sensing technology, hyperspectral remote sensing data are becoming more and more popular and widely used by people. An important feature of hyperspectral image processing is to understand the characteristics, distribution and changes of ground objects in the spatial dimension from the spectral dimension. Among them, the fine classification of ground features and target detection based on hyperspectral data has always been one of the core contents of the application of hyperspectral remote sensing technology. Hyperspectral image target detection has important theoretical value and application prospects in both civilian and military applications, and is a hot research issue in the current research field of ta...

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Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a hyperspectral image classification method based on local collaborative representation and neighborhood information constraints. long problem; on the other hand, overcoming the l of the sparse dictionary approach 0 norm or l 1 Difficulty in solving norms, reducing the number of dictionary atoms, and more conducive to quickly classifying samples of hyperspectral images

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