A Hyperspectral Image Unmixing Method Based on Infinite Gaussian Mixture Model

A Gaussian mixture model and hyperspectral image technology, applied in the field of image processing, can solve the problem that the matrix problem is easy to fall into the minimum solution, and achieve the effect of reducing the computational complexity

Active Publication Date: 2017-05-10
ZHEJIANG UNIV
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Problems solved by technology

For NMF, solving the matrix problem in NMF is easy to fall into the minimum solution problem

Method used

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  • A Hyperspectral Image Unmixing Method Based on Infinite Gaussian Mixture Model
  • A Hyperspectral Image Unmixing Method Based on Infinite Gaussian Mixture Model
  • A Hyperspectral Image Unmixing Method Based on Infinite Gaussian Mixture Model

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0037] A hyperspectral image unmixing method based on an infinite Gaussian mixture model,

[0038] 11) Perform dimensionality reduction processing on hyperspectral images to obtain processed dimensionality reduction data;

[0039] 12) Use the virtual dimension method to determine the size of the number of Gaussian components, and obtain the range of the number of Gaussian components. For each number of Gaussian components, use the K-means method to cluster separately. For each Clustered groups, using the PPI method, extract the purest pixels in each group as the expected vector in the Gaussian mixture model;

[0040]13) For each pixel in the hyperspectral image, based on the infinite mixture model, a two-state strategy is used to sample the number of end members, and then Metropolis-within-Gibb is used to estimate the parameters and hyperparam...

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Abstract

The invention discloses a hyperspectral image unmixing method based on an infinite Gaussian mixture model. It is assumed that picture elements in a hyperspectral image meet the infinite mixture model, compared with a traditional linear model, especially in hyperspectral image application with high resolution, the infinite mixture model can better reflect complexity of the picture elements of the image, and reasonable dimensionality reduction strategies are utilized to reduce computational complexity; in order to determine the number of Gaussian components, the number of the components is estimated through virtual dimensionality, and therefore the range of the number of the Gaussian components is expanded; for solving the infinite mixture model, different from a traditional solving method, the number of the components is effectively determined by the adoption of a TTS strategy, parameters and hyper-parameters in the infinite mixture model are determined by using a Metropolis-within-Gibbs method, and abundance corresponding to a machine of components of the mixed picture elements can be effectively obtained through sampling of the parameters and the hyper-parameters.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image unmixing method, in particular to a hyperspectral image unmixing method based on an infinite Gaussian mixture model. Background technique [0002] Hyperspectral images are three-dimensional images, including ordinary two-dimensional plane image information and wavelength information. While imaging the spatial characteristics of the target, each spatial pixel is dispersed to form dozens or even hundreds of narrow bands for continuous spectral coverage. A hyperspectral image is a three-dimensional hyperspectral image composed of two-dimensional images corresponding to several wavelengths. [0003] Near-infrared hyperspectral spectroscopy is widely used in food, medicine, petrochemical and other industries due to its fast and non-destructive characteristics. However, since most of the current hyperspectral images are synthesized by mixing multiple diff...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T19/00
Inventor 邓水光徐亦飞李莹吴健尹建伟吴朝晖
Owner ZHEJIANG UNIV
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