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

Evolutionary multi-objective optimization-based method for extracting end members of hyperspectral remote sensing images

A hyperspectral remote sensing and multi-objective optimization technology, which is applied in the field of endmember extraction of hyperspectral remote sensing images, can solve time-consuming and other problems

Active Publication Date: 2017-10-20
XIDIAN UNIV
View PDF4 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, most of these algorithms get different results by performing a series of separate runs, which is very time-consuming

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
  • Evolutionary multi-objective optimization-based method for extracting end members of hyperspectral remote sensing images
  • Evolutionary multi-objective optimization-based method for extracting end members of hyperspectral remote sensing images
  • Evolutionary multi-objective optimization-based method for extracting end members of hyperspectral remote sensing images

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0070] The present invention will be further described in detail below in conjunction with specific embodiments, which are explanations of the present invention rather than limitations.

[0071] An endmember extraction method based on evolutionary multi-objective optimization (MOEE), which can accurately extract endmembers from hyperspectral images. The method mainly solves the problems in the prior art that the calculation complexity is high and the algorithm needs to be run multiple times to obtain results with different numbers of endmembers. The implementation steps of the invention are: (1) determine the objective function; (2) construct the initial solution population, and use a random method to initialize the individuals in the solution population; (3) update the self-optimal position of the particle and the global optimal position of all particles The optimal position is in the history; (4) Use the speed of each particle to update its position; (5) Determine whether 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 discloses an evolutionary multi-objective optimization-based method for extracting end members of hyperspectral remote sensing images, and belongs to the field of hyperspectral remote sensing image processing. According to the method, an end member number is taken as an objective function of multi-objective optimization, and different numbers of end member extraction results are obtained by adoption of a singly operation algorithm, so that the execution speed of the algorithm is improved and the precision is improved; extraction of end members of hyperspectral remote sensing images is considered as a multi-objective problem, two objective functions are optimized at the same time by utilizing a discrete particle swarm optimization method, and through single operation, different end member numbers can be obtained, namely, optimum end members can be obtained; and the problem that different numbers of end member results are obtained through executing the single operation algorithm for multiple times in the prior art is overcome. By adoption of a reverse-growth leader selection strategy, the users do not need to search all the end members, so that the calculation complexity is decreased.

Description

technical field [0001] The invention belongs to the field of hyperspectral image processing, and relates to a linear spectral unmixing method of hyperspectral images, in particular to an endmember extraction method of hyperspectral remote sensing images based on evolutionary multi-objective optimization. Background technique [0002] One of the greatest achievements of remote sensing technology in the 1980s was the rise of hyperspectral remote sensing. Hyperspectral remote sensors have the ability to capture hundreds of continuous spectral bands which can be used to improve the identification of different classes of features. Due to its rich spectral information, hyperspectral images have been widely used, such as in mineral exploration, environmental monitoring and military surveillance. The problem of mixed pixel decomposition is an important problem that limits the development of hyperspectral remote sensing images, and this problem can be solved by spectral unmixing tec...

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 Applications(China)
IPC IPC(8): G06T7/00G06N3/00
Inventor 公茂果徐皓李豪詹涛
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