High-spectrum data dimensionality reduction method based on factor analysis model

A factor analysis model and data dimensionality reduction technology, applied in the field of hyperspectral data dimensionality reduction, can solve problems such as loss of data information

Inactive Publication Date: 2009-07-22
BEIHANG UNIV
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Problems solved by technology

[0004] The purpose of the present invention is to propose a hyperspectral data dimensionality reduction method based on a factor anal

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  • High-spectrum data dimensionality reduction method based on factor analysis model
  • High-spectrum data dimensionality reduction method based on factor analysis model
  • High-spectrum data dimensionality reduction method based on factor analysis model

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Embodiment Construction

[0040] In order to better illustrate the hyperspectral data dimensionality reduction method based on the factor analysis model involved in the present invention, the PHI airborne hyperspectral data is used to carry out fine classification of crops in the Fanglu tea farm area of ​​Jiangsu. The present invention is a hyperspectral data dimensionality reduction method based on a factor analysis model, and the realization process is as follows figure 1 As shown, the specific implementation steps are as follows:

[0041] (1) Reading in hyperspectral data: read in the PHI hyperspectral data of Fanglu Tea Farm, remove bands such as low signal-to-noise ratio and atmospheric absorption, and the original data size is 210×150×64;

[0042] (2) Establish a factor analysis model for hyperspectral data dimensionality reduction;

[0043] The factor analysis model for hyperspectral data dimensionality reduction is:

[0044] X=μ+AF+ε

[0045] In the formula, X=(x 1 , x 2 ,...,x 64 )' is a...

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Abstract

The invention provides a method for reducing dimensions of high spectroscopic data based on a factorial analysis model, comprising the following steps: (1) reading in the high spectroscopic data; (2) establishing the factorial analysis model for dimension reduction of the high spectroscopic data; (3) calculating average of data, covariance matrix and correlation matrix; (4) calculating proper value and standard eigen vector of the correlation matrix of the data; (5) carrying out solving on factor loading matrix by a parametric estimation method; (6) calculating covariance of special factors and communality of data variables in the factorial analysis model; (7) calculating the biggest factor loading spin matrix based on variance; (8) calculating factor scores by a least square method based on variance; and (9) obtaining eigen dimensionality characterizing high spectroscopic data, thereby realizing dimension reduction of the high spectroscopic data. The method is an automatic method for dimension reduction of the high spectroscopic data, which can effectively remove relativity of wave bands of the high spectroscopic data and improve separability of different categories of ground objects.

Description

technical field [0001] The invention relates to a hyperspectral data dimensionality reduction method based on a factor analysis model, which belongs to the field of hyperspectral data processing methods and application technologies, and is suitable for research on theoretical methods and application techniques for hyperspectral data dimensionality reduction. Background technique [0002] The hyperspectral imager is a new type of remote sensing payload. Its spectrum is compact and continuous, and it can record the spectral and spatial information characteristics of the same ground object at the same time. Therefore, the hyperspectral data has high dimensions and a large amount of data; due to The spectral resolution is high, there is a high correlation between each band, and there is a large amount of redundant information. And with the increase of band data, the amount of data processing increases exponentially. Therefore, how to reduce the dimensionality of hyperspectral d...

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Application Information

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IPC IPC(8): G01S7/48G06K9/62
Inventor 赵慧洁李娜蔡辉贾国瑞徐州
Owner BEIHANG UNIV
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