Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Sparse Unmixing Method for Hyperspectral Image Groups Based on Spatial Spectral Information Abundance Constraints

A hyperspectral image, degree-constrained technology, applied in the field of sparse unmixing of hyperspectral image groups, can solve the problem of not considering regional structure information and so on

Active Publication Date: 2020-04-24
XIDIAN UNIV
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

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.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Sparse Unmixing Method for Hyperspectral Image Groups Based on Spatial Spectral Information Abundance Constraints
  • Sparse Unmixing Method for Hyperspectral Image Groups Based on Spatial Spectral Information Abundance Constraints
  • Sparse Unmixing Method for Hyperspectral Image Groups Based on Spatial Spectral Information Abundance Constraints

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0062] combined with figure 1 The concrete steps of the present invention are described as follows:

[0063] (1) Input hyperspectral image data and standard spectral library in is the spectral feature of the zth pixel in the hyperspectral image data, is the j-th column spectral feature in the spectral library, L is the number of bands, n is the number of pixels, m is the number of object spectra contained in the spectral library, represents the field of real numbers;

[0064] The size of the simulated data set is 224×30×30, and it consists of nine regions of the same size with three rows and three columns, each region is 10×10 in size, and the types and numbers of endmembers contained in each region 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.

[0065] Real data set: Mineral data set in the Nevada region of the United S...

Embodiment 2

[0106] Attached below image 3 And attached Figure 4 The effects of the present invention are further described.

[0107] The emulation experiment of the present invention is realized on the MATLAB R2011b on the Windows7 platform of Intel Core (TM) 2Duo CPU, main frequency 2.00GHz, internal memory 2G.

[0108] 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) = E||Ax|| 2 / E||n|| 2 They are: 20dB, 30dB and 40dB.

[0109] 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 proce...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

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

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
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
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com