Eureka AIR delivers breakthrough ideas for toughest innovation challenges, trusted by R&D personnel around the world.

Snow passive microwave mixed pixel decomposition method based on classified information of five types of ground features

A technology of mixed pixel decomposition and ground object classification, which is applied in the field of passive microwave mixed pixel decomposition of snow cover, to achieve the effect of improving the inversion accuracy

Inactive Publication Date: 2013-06-12
JILIN UNIV
View PDF2 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0016] The technical problem to be solved by the present invention is to provide a snow passive microwave mixed pixel decomposition method based on five types of ground object classification information, which can effectively solve the snow passive microwave mixed pixel problem and improve the inversion accuracy of snow parameters , has important application value in the fields of climate and hydrology research and disaster assessment

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
  • Snow passive microwave mixed pixel decomposition method based on classified information of five types of ground features
  • Snow passive microwave mixed pixel decomposition method based on classified information of five types of ground features
  • Snow passive microwave mixed pixel decomposition method based on classified information of five types of ground features

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0139] The present invention uses AMSR-E passive microwave remote sensing data and MODIS land cover classification UMD data products, combined with the proposed snow passive microwave remote sensing hybrid pixel decomposition method, to realize the snow accumulation in Xingkai Lake area, Heilongjiang Province, China on November 26, 2010 Efficient decomposition of passive microwave hybrid pixels.

[0140] Specifically include the following steps:

[0141] 1. Acquisition of ground object classification data:

[0142] The snow microwave mixed pixel decomposition method requires the classification information of the underlying surface objects in the observation area. The microwave brightness temperature of snow cover is greatly affected by the underlying surface. Through foundation experiments, it is found that the brightness temperature of snow cover on forest land is greatly affected by tree trunks, the brightness temperature of snow cover on grassland is mainly affected by the...

Embodiment 2

[0218] The present invention uses AMSR-E passive microwave remote sensing data and MODIS land cover classification LAI / FPAR data products, combined with the proposed snow passive microwave remote sensing hybrid pixel decomposition method, to realize the passive snow accumulation in Jilin Province, China on November 13, 2010. Efficient decomposition of microwave mixed pixels.

[0219] Specifically include the following steps:

[0220] 1. Acquisition of ground object classification data:

[0221] The snow microwave mixed pixel decomposition method requires the classification information of the underlying surface objects in the observation area. The microwave brightness temperature of snow cover is greatly affected by the underlying surface. Through foundation experiments, it is found that the brightness temperature of snow cover on forest land is greatly affected by tree trunks, the brightness temperature of snow cover on grassland is mainly affected by the scattering of grass,...

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 remotely sensed image processing, and provides a snow passive microwave mixed pixel decomposition method based on the classified information of five types of ground features. According to five types of typical underlying surfaces affecting the radiation characteristic of snow, the method acquires the classified data of the ground features with high special resolution in an observed region, utilizes the different characteristics of the microwave radiation of snow on the different underlying surfaces and selects a microwave antenna gain function and a sampling rate to build a snow passive microwave mixed pixel decomposition model, and adopts the least square method with constraint conditions to iteratively solve an underdetermined system of equations, thus realizing snow passive microwave mixed pixel decomposition. The method can effectively solve the problem of the mixed pixels of passive microwaves of snow, improves the precision of snowparameter inversion, and has a significant application value in fields such as climate and hydrological research and snow disaster assessment.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a snow passive microwave mixed pixel decomposition method based on five kinds of ground object classification information, which can solve the problem of low inversion accuracy of snow parameters due to snow microwave mixed pixels The problem. Background technique [0002] The variability and characteristics of snow cover are important parameters in climate research, weather forecasting and water resource management (see Reference 1 listed below). The most commonly used wavebands in snow remote sensing are visible light, near-infrared and microwave. Among them, visible light and near-infrared are mainly used to extract snow coverage. Their biggest weakness is that they cannot be used to retrieve snow depth and snow water equivalent. Microwave plays an indispensable role in remote sensing of snow cover. It can not only observe snow cover all da...

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): G01S7/41
Inventor 顾玲嘉任瑞治张爽王昊丰孙健
Owner JILIN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
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