Spectral unmixing method based on core prototype sample analysis

A sample analysis and spectral unmixing technology, applied in image analysis, image data processing, instruments, etc., can solve problems such as difficulty in obtaining intuitive information, falling into local extremum, and unstable results

Inactive Publication Date: 2014-07-23
HARBIN ENG UNIV
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Problems solved by technology

This model is more flexible and can effectively extract information such as variance, but its expression form is complex, so it is difficult to obtain some intuitive information.
In addition, it adopts random initialization, which is easy to fall into local extremum, and the result is unstable

Method used

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  • Spectral unmixing method based on core prototype sample analysis
  • Spectral unmixing method based on core prototype sample analysis
  • Spectral unmixing method based on core prototype sample analysis

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specific Embodiment approach

[0072] In order to realize the above-mentioned purpose of the invention, this patent is based on such figure 1 The flow shown completes the unmixing. The specific embodiment of the present invention is as follows:

[0073] 1. Read in the hyperspectral data X to be processed, X∈R M×N , where M is the dimension of the spectral vector, and N is the number of all pixels in the data.

[0074] 2. Set the parameters involved in the overall process. Given the number of endmembers D to be extracted from the image data, set the kernel parameter σ, and the relaxation factor δ.

[0075] 3. Preprocessing the input image data to obtain X'. Use the PCA dimensionality reduction algorithm to extract the first D-1 principal components, that is, X'∈R (D-1)×N .

[0076] 4. On the preprocessed data, the spectral unmixing is realized by using the analysis method based on the nuclear prototype sample.

[0077] In a given dataset X'∈R (D-1)×N , D is the number of prototype vectors (also calle...

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Abstract

The invention relates to a spectral unmixing method based on core prototype sample analysis. The method includes the steps of collecting hyperspectral data to be processed, determining the parameters of the whole process, preprocessing input image data and achieving spectral unmixing on the preprocessed data through a core prototype sample analysis method. The spectral unmixing method is easy to implement, the spectral unmixing process does not need to be independently factorized into an end member extraction process and an unmixing process, the unmixing problem of the inexistence of pure end members can be solved, and the optimal end member selecting and unmixing problem of data at different mixing degrees can be solved as well. In addition, the physical meanings of an ultimate extraction result are definite, and data deciphering capacity is higher. Meanwhile, the result obtained in the method is more stable compared with a non-negative matrix factorization spectral unmixing result, and the precision is better.

Description

technical field [0001] The invention relates to a spectral unmixing method based on nuclear prototype sample analysis. Background technique [0002] Hyperspectral remote sensing imagers can acquire tens to hundreds of very narrow and continuous spectral bands in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, and obtain fine ground object information based on the advantages of high spectral resolution . However, due to the limitation of imaging spatial resolution and the complexity and diversity of the ground surface, some pixels of the remote sensing images obtained by imaging often contain multiple types of ground features. It has received extensive attention and research from scholars at home and abroad. How to accurately extract the spectrum of typical ground objects (endmembers) from hyperspectral mixed data, and effectively decompose the mixed pixels to obtain the mixing ratio (abundance) between them has become an im...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
Inventor 赵春晖赵艮平李晓慧刘务李威
Owner HARBIN ENG UNIV
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