Hyperspectral remote sensing image abundance estimation method and device based on conditional number weighting

A technology for hyperspectral remote sensing and abundance estimation, which is applied in the field of hyperspectral remote sensing image abundance estimation based on condition number weighting, can solve the problems of low unmixing accuracy and large abundance estimation error, and achieves improved unmixing accuracy and improved The effect of least squares estimation

Pending Publication Date: 2022-04-05
CHINA UNIV OF GEOSCIENCES (WUHAN)
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Problems solved by technology

[0004] In order to solve the technical problems of large abundance estimation errors and low unmixing accuracy by the fully constrained least squares method, the technical solution adopted by the present invention is to provide a hyperspectral remote sensing image abundance estimation method and device based on condition number weighting

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  • Hyperspectral remote sensing image abundance estimation method and device based on conditional number weighting
  • Hyperspectral remote sensing image abundance estimation method and device based on conditional number weighting
  • Hyperspectral remote sensing image abundance estimation method and device based on conditional number weighting

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[0044] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, the specific implementation manners of the present invention will now be described in detail with reference to the accompanying drawings.

[0045] Such as figure 1 As shown, the embodiment of the present invention provides a hyperspectral remote sensing image abundance estimation method based on condition number weighting, comprising the following steps:

[0046] S1: Obtain hyperspectral data matrix and endmember data matrix;

[0047] Wherein, the hyperspectral data matrix is ​​the hyperspectral image to be unmixed;

[0048] The endmember data matrix can be obtained by some classical algorithms, such as Vertex Component Analysis Algorithm (VCA), Spatial Spectral Endmember Extraction Algorithm (SSEE), etc., or directly extracted from the endmember spectral library.

[0049] S2: Using biorthogonal wavelets, respectively perform wavelet packet decomposition ...

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Abstract

The invention provides a hyperspectral remote sensing image abundance estimation method and device based on conditional number weighting. The method comprises the following steps: acquiring a hyperspectral data matrix and an end metadata matrix; performing wavelet packet decomposition on the hyperspectral data matrix and the end metadata matrix by using biorthogonal wavelets to obtain corresponding wavelet packet trees; obtaining a coefficient matrix at each node of a wavelet packet tree corresponding to the end metadata matrix; calculating the condition number of each coefficient matrix; calculating the weight of each node according to each condition number; and taking the hyperspectral data matrix and the end metadata matrix corresponding to each node as the input of the least square method, multiplying the result output by the least square method each time by the weight of the corresponding node, and finally summing each node to obtain an abundance estimation result. According to the method, the conditional number of the coefficient matrix after wavelet packet decomposition is used for weighting, effective information in the data can be utilized to a greater extent, and therefore the least square estimation is improved, and the unmixing precision of the hyperspectral remote sensing image is improved.

Description

technical field [0001] The invention relates to the technical field of hyperspectral unmixing, in particular to a hyperspectral remote sensing image abundance estimation method and device based on condition number weighting. Background technique [0002] Due to the limitation of spatial resolution of hyperspectral remote sensing image information and the influence of complex ground objects, a large number of mixed pixels appear in the image, that is, one pixel contains spectral information of several ground objects at the same time. If a pixel contains only one kind of ground object, the pixel is called an end member. Hyperspectral unmixing mainly includes endmember extraction and abundance estimation, that is, to determine the types of ground objects that make up the mixed pixel and to determine the proportion of each type. [0003] The existing hyperspectral remote sensing image abundance estimation method mainly adopts the fully constrained least squares method, but when...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06V20/10G06F17/14G06F17/16
Inventor 袁志轩李杏梅
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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