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

Vegetation index time series data reconstruction method based on wavelet multi-scale decomposition

A multi-scale decomposition and time-series data technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., can solve the problems of non-stationarity of vegetation index time-series data, unsuitable for stationarity methods, etc., to achieve a wide range of applications, high precision effect

Active Publication Date: 2014-11-12
FUZHOU UNIV
View PDF6 Cites 10 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These methods all have certain rationality and practical promotion value, but their shortcoming is that the vegetation index time series data are generally non-stationary, so it is not suitable to use the stationary method, so there are inevitably certain limitations

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
  • Vegetation index time series data reconstruction method based on wavelet multi-scale decomposition
  • Vegetation index time series data reconstruction method based on wavelet multi-scale decomposition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0008] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0009] The method for reconstructing vegetation index time series data based on wavelet multi-scale decomposition in the present invention, such as figure 1 As shown, using wavelet transform, the original vegetation index signal is decomposed into high-frequency and low-frequency components corresponding to half-moon, month, bimonthly, season, half-year, and inter-annual scales, and further based on the vegetation index on each scale Time-series data change law Select the appropriate model to reconstruct the vegetation index time-series data, and finally integrate the time-series data of different scales to realize the reconstruction of the vegetation index time-series data.

[0010] Specifically, the reconstruction method of vegetation index time-series data based on wavelet multi-scale decomposition in this embodiment further includes the f...

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 relates to a vegetation index time series data reconstruction method based on wavelet multi-scale decomposition. The method is characterized by comprising the following steps: performing wavelet transformation according to the vegetation index time series data to respectively sequentially decompose the original time series data of vegetation indexes and climatic factors into corresponding half moth, month, double month, season, half a year, and time series data based on interannual scales; further selecting the climatic factor time series data under the corresponding scale according to the features of the vegetation index time series data in each scale; selecting a proper model; reconstructing time series data in different scales; finally combining the vegetation index time series data in all scales to realize the reconstruction of the original vegetation index time series data. The method has the characteristics of being high in precision and wide in applicable scope.

Description

technical field [0001] The present invention relates to the technical field of time series analysis, in particular to a reconstruction method for vegetation index (Vegetation index, VI) time series data based on wavelet multi-scale decomposition. Background technique [0002] The time series data of remote sensing vegetation index are widely used in the monitoring of dynamic changes in forest and crop vegetation. In the process of remote sensing data collection and image processing, due to the interference of various factors such as observation angles and clouds, the quality of the generated vegetation index time series data is not ideal. Therefore, it is necessary to further denoise and reconstruct the original vegetation index time series data. [0003] There are many methods for denoising and reconstructing vegetation index time series data, such as Best Slope Index Extraction (Best slope extraction, BISE), Fourier analysis, multivariate least squares, geostatistics, non...

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): G06F19/00
Inventor 邱炳文钟鸣
Owner FUZHOU 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