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Hyperspectral image group sparse demixing method based on empty spectral information abundance restraint

A hyperspectral image, degree-constrained technology, applied in the field of sparse unmixing of hyperspectral image groups, can solve problems such as not considering regional structure information

Active Publication Date: 2015-03-25
XIDIAN UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, the shortcomings of the above two sparse unmixing methods are that they do not consider the regional structure information of hyperspectral image data and further weaken the influence of high correlation between object spectra in the spectral library on sparse unmixing.

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  • Hyperspectral image group sparse demixing method based on empty spectral information abundance restraint
  • Hyperspectral image group sparse demixing method based on empty spectral information abundance restraint
  • Hyperspectral image group sparse demixing method based on empty spectral information abundance restraint

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Embodiment 1

[0065] combined with figure 1 The concrete steps of the present invention are described as follows:

[0066] (1) Input hyperspectral image data and standard spectral library in is the spectral feature of the i-th pixel in the hyperspectral image data, is the jth ground object spectrum in the standard spectral library, L is the number of bands, n is the number of pixels, m is the number of ground object spectra contained in the spectral library, represents the field of real numbers;

[0067] The size of the simulated data set is 224×30×30, which consists of nine areas of the same size, three rows and three columns, each area is 10×10 in size, and the types and numbers of endmembers contained in each area are different. The simulated data set contains The number of endmembers is 9, the endmembers are randomly selected from the spectral library, and the abundance obeys the Dirichlet distribution.

[0068] Real data set: Mineral data set in the Nevada region of the Unit...

Embodiment 2

[0112] Attached below image 3 And attached Figure 4 The effects of the present invention are further described.

[0113] The simulation experiment of the present invention is realized on the MATLAB R2011b on the Windows 7 platform of Intel Core (TM) 2Duo CPU, main frequency 2.00GHz, internal memory 2G.

[0114] The simulation of the present invention is an experimental simulation done on a simulated data set and a real data set. The simulated data is composed of nine small blocks of 10×10, and each block contains different numbers of endmembers. Randomly selected, the abundance of all small blocks obeys the Dirichlet distribution, the simulated data set is 224×900, the data is interfered by different levels of Gaussian white noise, the signal-to-noise ratio SNR (dB) = Ε||Ax|| 2 / Ε||n|| 2 They are: 20dB, 30dB and 40dB.

[0115] The real data is the mine data set in the Nevada region of the United States (as attached figure 2 shown), in which in the actual simulation pro...

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Abstract

The invention belongs to the technical field of image processing, and particularly discloses a hyperspectral image group sparse demixing method based on empty spectral information abundance restraint. The method comprises the steps of inputting a hyperspectral image data set and a standard spectrum bank, conducting adaptive grouping on hyperspectral image data with the mean-shift algorithm, conducting group sparse demixing on each group of hyperspectral image data, trimming the spectrum bank with the abundance matrix of each group of hyperspectral image data as the criteria, and outputting a hyperspectral image data sparse demixing result. According to the method, the structural features of hyperspectral data and spectrum bank data are considered, group sparse demixing and spectrum bank trimming are adopted for hyperspectral image demixing, and hyperspectral data sparse demixing precision is improved. The semi-supervision-based hyperspectral image demixing method has the advantages of sparse demixing and is high in demixing precision and effective.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to the technical field of remote sensing images, in particular to a hyperspectral image group sparse unmixing method based on the abundance constraints of spatial spectrum information. Background technique [0002] Hyperspectral remote sensing image technology has developed rapidly in recent years, and its research is mainly devoted to finding technical methods to enable computers to intelligently learn and identify real objects in hyperspectral images. Hyperspectral images have great application prospects in many aspects such as urban planning, environmental detection, vegetation classification, military target detection, and mineral geological identification. Due to the influence of the resolution of the hyperspectral imager and the complexity of the ground and terrain, there is a mixture of various substances in a single pixel in the image, thus forming a mixed pix...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06T7/00
CPCG06T7/11G06T2207/10032G06T2207/20112
Inventor 张向荣焦李成吴健康马文萍马晶晶侯彪白静刘红英
Owner XIDIAN UNIV
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