Hyper-spectral information extraction method based on main component and cluster analysis

Abstract

The invention discloses a hyper-spectral information extraction method based on main component and cluster analysis. The method comprises the steps of: determining a standardized matrix Z of data of n samples; determining a correlation coefficient matrix R of the standardized matrix Z, calculating the characteristic roots of the correlation coefficient matrix R and characteristic vectors respectively corresponding to each characteristic root, and converting standardized sample data variables into main components, wherein a variable U1 highest in contribution rate is a number 1 main component, a variable U2 the second highest in contribution rate is a number 2 main component, ..., and a variable whose ranking is p is the number p main component; carrying out weighted summation on m main components which are highest in contribution rate to obtain an accumulated contribution rate, and taking the m main components whose accumulated contribution rate exceeds a threshold as cluster analysis main components; and carrying out classification scale calculation on the cluster analysis main components to determine the similarity of the samples, and classifying the samples according to the determined similarity of the samples. According to the invention, the main component analysis method and the cluster analysis method are combined to realize effective extraction of hyper-spectral image information.

Description

technical field [0001] The invention relates to spectral data analysis, in particular to a hyperspectral information extraction method based on principal component and cluster analysis. Background technique [0002] Hyperspectral image technology combines target spectral information and target spatial characteristics, and the acquired image cube contains rich spatial and spectral information, which makes human observation and information acquisition capabilities a big step forward. However, there are still many problems in hyperspectral image technology that need to be solved urgently, such as eliminating the influence of intra-class and inter-class differences, realizing real-time massive information processing, and so on. [0003] Traditional analysis methods include principal component analysis. Principal component analysis is an important statistical method, the purpose of which is to linearly transform the original variables to select several important variables, a mul...

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Problems solved by technology

[0005] Traditional analysis methods have some disadvantages, such as principal component analysis, although it can reduce the dimensionality and roughly distinguish different features in the vector space, the amount of information after dimensionality reduction is often unstable and cannot be used for Classify objects with different characteristics

The traditional single statistical method is difficult to meet the requirements of current hyperspectral image information extraction, so it is necessary to integrate multiple statistical methods

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