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

High-spectral image nonlinear unmixing method based on bilinear mixing model

A hyperspectral image and hybrid model technology, applied in character and pattern recognition, instrument, scene recognition, etc., can solve the problems of over-fitting results, noise sensitivity, complex calculations, etc., achieve good robustness and reduce algorithm complexity , the effect of reducing the operation time

Active Publication Date: 2017-05-31
FUDAN UNIV
View PDF6 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, they have the problem that the calculation is too complicated and often fall into the local minimum, and the collinear effect produced by the strong correlation between the endmember and the virtual endmember will also lead to the overfitting of the result and be more sensitive to noise [9]

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
  • High-spectral image nonlinear unmixing method based on bilinear mixing model
  • High-spectral image nonlinear unmixing method based on bilinear mixing model
  • High-spectral image nonlinear unmixing method based on bilinear mixing model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] In the following, the specific implementation manners of the present invention will be described by taking simulated data and actual remote sensing image data as examples respectively.

[0067] The hyperspectral remote sensing image nonlinear unmixing algorithm based on the bilinear mixed model adopted in the present invention is represented by GEAB-FCLS.

[0068] 1. Simulation data experiment

[0069] In this section, the GAEB-FCLS algorithm is combined with the linear abundance estimation algorithm FCLS[10], the data-driven nonlinear unmixing method KFCLS[12] based on the Gaussian kernel (kernel parameters are obtained between 0.01-300 by cross-validation method) and FM, GBM and PPNM three models corresponding to the traditional solution algorithm: Fan-FCLS [6], GBM-GDA [7] and PPNM-GDA [8] for performance comparison. And use the root mean square error RMSE (Root Mean Square Error) of the abundance and the reconstruction error RE (Reconstructed Error) of the image to...

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 remote sensing image processing technology, in particular to a high-spectral image nonlinear unmixing method based on a bilinear mixing model. According to geometrical characteristics of the bilinear mixing model, the bilinear mixing components in data are represented as linear contributions fusing common nonlinear effect ends, the complex nonlinear unmixing is converted into a simple linear unmixing problem, and by combining the linear unmixing algorithm, the abundance which is estimated to be correct is iterated. According to the invention, based on the mixing model of high-spectral observation picture element, by combining the geometrical and physical meanings of the mixing model, disadvantages of linear unmixing can be effectively overcome; unfavorable effects caused by collinear effects can be well overcome; robustness to noise and the number of end elements is quite high; the method can be used as an effective means for solving nonlinear unmixing of high-spectral remote sensing images; and the method has an important application value in aspects of high precision unmixing based on high spectra remote sensing images and detection and recognition of ground targets.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral image nonlinear unmixing method. Background technique [0002] Remote sensing technology is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is one of the most powerful technical means for studying the earth's resources and environment. Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology with spectral technology. Its image has the characteristics of high spectral resolution and map-spectrum integration, which provides extremely rich information for the extraction and analysis of ground object information. However, due to the low spatial resolution and the complex and diverse distribution of ground objects, most of the pixels in the image are mixed p...

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
IPC IPC(8): G06K9/00
CPCG06V20/194G06V20/13
Inventor 杨斌王斌
Owner FUDAN 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