High-spectral image demixing method based on end member cluster

A technology of endmembers and images, which is applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve problems such as unmixing errors, achieve the effects of improving unmixing accuracy, strong adaptability, and wide application environment

Active Publication Date: 2018-07-13
BEIHANG UNIV
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AI Technical Summary

Problems solved by technology

Spectral variation causes changes in spectral values, which in turn lead to unmixing errors

Method used

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  • High-spectral image demixing method based on end member cluster
  • High-spectral image demixing method based on end member cluster
  • High-spectral image demixing method based on end member cluster

Examples

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Embodiment Construction

[0043] The application method of the present invention will be further described below in conjunction with examples.

[0044] In this example, the hyperspectral image data captured by the interferometric imaging spectrometer carried by the Chang'e-1 satellite successfully launched by my country on October 24, 2007 is used for unmixing processing. The size of the hyperspectral data processed this time is 300×128, the wavelength range is 480-960nm, and it contains 32 bands, 20 of which are selected for calculation, and the spatial resolution is 200m / pixel.

[0045] (1) Sparse representation based on global image

[0046] Construct an over-complete dictionary with a size of 38400×20, and perform sparse representation for each pixel. In this example, the sparsity of the sparse representation is set to 3, and the number of candidate endmembers is set to 15.

[0047] (2) Alternative endmember screening based on voting

[0048] For each pixel and its obtained sparse coefficient, v...

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Abstract

The invention discloses a high-spectral image demixing method based on an end member cluster. Adaptive accurate demixing of a high-spectral image is achieved, through extraction of the end member cluster and abundance inversion based on the end member cluster. The method includes the steps: (1) performing sparse representation based on a global image; (2) screening alternative end members based onvoting; (3) establishing the end member cluster by extracting spectral shape characteristics; (4) constructing a sub-block over-complete dictionary; (5) traversing the entire image, and selecting anoptimal end member cluster based on block sparsity according to each pixel; (6) performing abundance estimation by the aid of a fully-constrained least square method, and outputting an end member spectrum and an abundance result. According to spectrum variation in a high-spectral image, the method can effectively decrease errors caused by the spectrum variation, demixing accuracy is improved, andadaptivity of an algorithm is high based on extraction of the end member cluster of the image.

Description

technical field [0001] The invention relates to a novel hyperspectral image unmixing method based on endmember clusters, which can effectively reduce errors caused by spectral variation during unmixing through technical means, and belongs to the field of remote sensing image processing. Background technique [0002] Hyperspectral remote sensing refers to remote sensing technology with high spectral resolution, and its detection band covers the spectral region (0.4 μm-2.5 μm) including ultraviolet, visible light, near-infrared, mid-infrared and thermal infrared with nanometer-level spectral resolution. Hyperspectral remote sensing can continuously and finely describe the spectrum of surface objects, and has outstanding advantages in exploration, detection, and identification. The development potential of hyperspectral remote sensing is huge. Since its advent in the 1980s, it has been regarded as the two most important technological breakthroughs in remote sensing technology t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/46
CPCG06V10/40G06F18/2193G06F18/241
Inventor 尹继豪黄晨雨罗晓燕罗旭坤
Owner BEIHANG UNIV
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