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A Method of Automatically Extracting Surface Vegetation Phenology Information Based on Gaussian Function Fitting Variance

A technology of surface vegetation and Gaussian functions, applied in image data processing, image analysis, image enhancement, etc., to achieve the effects of eliminating subjective factors, strong correlation, and simplifying the calculation process

Inactive Publication Date: 2017-06-30
JILIN UNIV
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Problems solved by technology

[0008] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and provide a method based on Gaussian function fitting for the existing vegetation phenology information extraction process such as complex calculation process and the existence of subjective factors in manual judgment. The Method of Variance Automatically Extracting Surface Vegetation Phenology Information

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  • A Method of Automatically Extracting Surface Vegetation Phenology Information Based on Gaussian Function Fitting Variance
  • A Method of Automatically Extracting Surface Vegetation Phenology Information Based on Gaussian Function Fitting Variance
  • A Method of Automatically Extracting Surface Vegetation Phenology Information Based on Gaussian Function Fitting Variance

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[0041] Below in conjunction with accompanying drawing and embodiment the present invention is described in further detail:

[0042] A method for automatically extracting surface vegetation phenology information based on Gaussian function fitting variance, comprising the following steps:

[0043] Step 1. Raw remote sensing image preprocessing

[0044] A. Perform radiometric calibration and atmospheric correction on the visible light band of remote sensing images through the FLAASH model under the remote sensing software ENVI platform;

[0045] B. Calculate the surface vegetation index (NDVI) through the band ratio, and cut the calculated result image according to the scope of the research area;

[0046] C, repeat above-mentioned A-B steps, obtain the NDVI remote sensing image dataset of the monthly value sequence (or the ten-day value sequence of 24 days in 1 year) of 1 year and 12 months;

[0047] Step 2. Determine the goodness of fit

[0048] D. Write an algorithm on the R...

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Abstract

The invention relates to a method for automatically extracting phenology information of earth surface vegetation based on the fitting variance of a Gaussian function. A range which is not only a space concept but also a time concept is applied to operation, the range is calculated, the obtained range represents range values of the whole NDVI time sequence set, the range values represent a period in which the variance function changes most, and the change period represents the growth period of the surface land vegetation of a research area. The method comprises the steps of 1) preprocessing initial remote sensing images; 2) determining the fitting goodness; 3) determining the range reasonableness; and 4) completing extraction of the phenology information. The method can be used to effectively solve the problems including that the fitting parameters are too many, the calculation process is complex and manual determination is subjective, results are obtained via calculation based on computer in the whole course, subjectivity of manual determination is eliminated, and it is proved that result data is highly correlated to the practical phenology information of the earth surface vegetation in the research area and is quite indicative and representative.

Description

[0001] Technical field: [0002] The invention relates to a method for extracting surface vegetation phenology information from remote sensing images, in particular to a method for automatically extracting surface vegetation phenology information by Gaussian function fitting variance [0003] Background technique: [0004] In terms of agricultural production, the prediction of crop spring sowing and crop disaster prevention and mitigation are closely related to the study of crop phenology, so the study of crop phenology has become one of the hotspots in geographical research. Taking the surface vegetation in mainland China as the research object, Wu Yongfeng et al. established a remote sensing monitoring model for the greenness period of land surface vegetation, namely the Logistic fitting model, and verified the reliability and superiority of the model results. Taking the surface vegetation in Northeast China as the research object, Guo Zhixing et al. chose the piecewise Logis...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00
CPCG06T7/11G06T2207/10032G06T2207/30188
Inventor 李晓东姜琦刚李远华李相坤
Owner JILIN UNIV
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