CN108981617A - A kind of winter wheat inversion method of leaf area index and system - Google Patents
A kind of winter wheat inversion method of leaf area index and system Download PDFInfo
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Abstract
The present invention discloses a kind of winter wheat inversion method of leaf area index and system.This method comprises: obtaining the digital image of unmanned plane acquisition；Digital image is spliced to obtain corresponding orthography and point cloud data；A variety of vegetation indexs of visible light wave range building are calculated orthography；Any one vegetation index for meeting preset condition with leaves of winter wheat area index correlation is filtered out from a variety of vegetation indexs, obtains the first inverting index；According to the height of point cloud data inverting winter wheat, the second inverting index is obtained；Binary inverting is carried out to the first inverting index and the second inverting index, obtains regression model；Inverting is carried out to leaves of winter wheat area index using regression model.Inversion method and system of the invention is more preferable to the result of leaves of winter wheat area index inverting compared with the simple regression linear model merely with vegetation index.This method and system improve the precision of the leaf area index inverting to inverting jointing stage winter wheat by the way that the elevation information of winter wheat is added.
Description
Technical field
The present invention relates to winter wheat growing way research field, more particularly to a kind of winter wheat inversion method of leaf area index and
System.
Background technique
Leaf area index LAI (Leaf Area Index, LAI) is a crucial physiological parameter, is applied to plant growth
Monitoring and ecological Studies, photosynthesis and biochemical process research to crop play an important role, LAI oneself through being widely applied
In fields such as agricultural, ECOLOGICAL ENVIRONMENTAL MONITORINGs.But the research in terms of based on unmanned plane digital image inverting Crop leaf area index is very
It is few, and in inverting LAI mostly to consider not accounting for the elevation information of winter wheat, lead to inversion result based on spectral information
Spectral information is only relied upon, inversion accuracy is lower.
Summary of the invention
The object of the present invention is to provide a kind of winter wheat inversion method of leaf area index and systems, improve leaves of winter wheat area
The precision of index inverting.
To achieve the above object, the present invention provides following schemes:
A kind of winter wheat inversion method of leaf area index, comprising:
Obtain the digital image of unmanned plane acquisition；
The digital image is spliced to obtain corresponding orthography and point cloud data；
A variety of vegetation indexs of visible light wave range building are calculated the orthography；
It is filtered out from a variety of vegetation indexs and meets preset condition with the leaves of winter wheat area index correlation
Any one vegetation index obtains the first inverting index；
According to the height of the orthography and the point cloud data inverting winter wheat, the second inverting index is obtained；
Binary inverting is carried out to the first inverting index and the second inverting index, obtains regression model；
Inverting is carried out to leaves of winter wheat area index using the regression model.
Optionally, a variety of vegetation indexs for calculating the orthography visible light wave range building, specifically include:
It is calculated using the following equation greenery vegetation index GLA:
GLA=(2*G-R-B)/(2*G+R+B)
It is calculated using the following equation super green vegetation index ExG:
ExG=2*G-R-B
It is calculated using the following equation super green exceedingly popular difference vegetation index ExGR:
ExGR=ExG-1.4*R-G
It is calculated using the following equation plant index VEG:
VEG=G/ (R^a*B^ (1-a))
It is calculated using the following equation Woebbecke index W I:
WI=(G-B)/(R-B)
Visible light atmosphere impedance value is calculated using the following equation by index VARI:
VARI=(G-R)/(G+R-B)
It is calculated using the following equation green red vegetation index GRVI:
GRVI=(G-R)/(G+R)
Wherein G is the gray value of green channel, and R is the gray value of red channel, and B is the gray value of blue channel, and a is to be
Number, a=0.667.
Optionally, described filter out from a variety of vegetation indexs meets with the leaves of winter wheat area index correlation
Any one vegetation index of preset condition obtains the first inverting index, specifically includes:
Various vegetation indexs are calculated using the leaf area index data of actual measurement and various vegetation index data to refer to leaf area
Correlation between number；
The vegetation index that the correlation between leaf area index meets preset condition is filtered out, high correlation vegetation is obtained
Index；
A kind of vegetation index is chosen from a variety of high correlation vegetation indexs, obtains the first inverting index.
Optionally, described that binary inverting is carried out to the first inverting index and the second inverting index, it is returned
Model specifically includes:
Regression model is established using following formula:
LAI=m*VI+n*CHM+p
Wherein LAI indicates leaf area index, and VI is the first inverting index, and CHM indicates the height of inverting, and m is vegetation index,
N is the coefficient of the winter wheat height value of inverting, and p is the deviation constant of regression model.
Optionally, it is anti-to obtain second for the height according to the orthography and the point cloud data inverting winter wheat
Index is drilled, is specifically included:
The orthography is split using the multi-scale division of eCognition software, by the orthography point
At winter wheat object and non-winter wheat object；
Centered on the winter wheat object, the non-winter wheat point cloud data of winter wheat objects perimeter is searched for, until the non-winter
Wheat point cloud data number is greater than 100；
Calculate separately the average value Hv-mean of winter wheat point cloud data and the average value Hnv- of non-winter wheat point cloud data
Hv-mean is subtracted Hnv-mean and obtains the relative altitude CHM of winter wheat by mean.
Invention additionally discloses a kind of leaves of winter wheat area index Inversion Systems, comprising:
Image acquiring module, for obtaining the digital image of unmanned plane acquisition；
Splicing module obtains corresponding orthography and point cloud data for being spliced to the digital image；
Vegetation index computing module, for calculating the orthography a variety of vegetation indexs of visible light wave range building；
Exponent extracting module is related to the leaves of winter wheat area index for filtering out from a variety of vegetation indexs
Property meets any one vegetation index of preset condition, obtains the first inverting index；
Height inverting module is obtained for the height according to the orthography and the point cloud data inverting winter wheat
Second inverting index；
Model building module is obtained for carrying out binary inverting to the first inverting index and the second inverting index
To regression model；
Inverting module, for carrying out inverting to leaves of winter wheat area index using the regression model.
Optionally, the vegetation index computing module, specifically includes:
First exponent calculation unit, for being calculated using the following equation greenery vegetation index GLA:
GLA=(2*G-R-B)/(2*G+R+B)
Second exponent calculation unit, for being calculated using the following equation super green vegetation index ExG:
ExG=2*G-R-B
Third exponent calculation unit, for being calculated using the following equation super green exceedingly popular difference vegetation index ExGR:
ExGR=ExG-1.4*R-G
4th exponent calculation unit, for being calculated using the following equation plant index VEG:
VEG=G/ (R^a*B^ (1-a))
5th exponent calculation unit, for being calculated using the following equation Woebbecke index W I:
WI=(G-B)/(R-B)
6th exponent calculation unit, for being calculated using the following equation visible light atmosphere impedance value by index VARI:
VARI=(G-R)/(G+R-B)
7th exponent calculation unit, for being calculated using the following equation green red vegetation index GRVI:
GRVI=(G-R)/(G+R)
Wherein G is the gray value of green channel, and R is the gray value of red channel, and B is the gray value of blue channel, and a is to be
Number, a=0.667.
Optionally, the exponent extracting module, specifically includes:
Correlation calculations unit, it is various for being calculated using the leaf area index data and various vegetation index data surveyed
Correlation between vegetation index and leaf area index；
High correlation screening unit, the correlation for filtering out between leaf area index meet the vegetation of preset condition
Index obtains high correlation vegetation index；
It is anti-to obtain first for choosing a kind of vegetation index from a variety of high correlation vegetation indexs for index selection unit
Drill index.
Optionally, the model building module, specifically includes:
Modeling unit, for establishing regression model using following formula:
LAI=m*VI+n*CHM+p
Wherein LAI indicates leaf area index, and VI is the first inverting index, and CHM indicates the height of inverting, and m is vegetation index,
N is the coefficient of the winter wheat height value of inverting, and p is the deviation constant of regression model.
Optionally, the height inverting module, specifically includes:
Cutting unit will for being split using the multi-scale division of eCognition software to the orthography
The orthography is divided into winter wheat object and non-winter wheat object；
Perimeter unit, for searching for the non-winter wheat of winter wheat objects perimeter centered on the winter wheat object
Point cloud data, until non-winter wheat point cloud data number is greater than 100；
Height calculation unit, for calculating separately the average value Hv-mean and non-winter wheat point cloud of winter wheat point cloud data
Hv-mean is subtracted Hnv-mean and obtains the relative altitude CHM of winter wheat by the average value Hnv-mean of data.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: leaves of winter wheat of the invention
Area index inversion method and system, by vertical structure, i.e. height factor is fused in leaf area index refutation process, relative to
Carrying out inverting to leaf area index using single factors has higher inversion accuracy.
Detailed description of the invention
It in order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, below will be to institute in embodiment
Attached drawing to be used is needed to be briefly described, it should be apparent that, the accompanying drawings in the following description is only some implementations of the invention
Example, for those of ordinary skill in the art, without any creative labor, can also be according to these attached drawings
Obtain other attached drawings.
Fig. 1 is the method flow diagram of one winter wheat inversion method of leaf area index of the embodiment of the present invention；
Fig. 2 is the system construction drawing of three leaves of winter wheat area index Inversion System of the embodiment of the present invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The object of the present invention is to provide a kind of winter wheat inversion method of leaf area index and systems, improve leaves of winter wheat area
The precision of index inverting.
In order to make the foregoing objectives, features and advantages of the present invention clearer and more comprehensible, with reference to the accompanying drawing and specific real
Applying mode, the present invention is described in further detail.
Embodiment one:
Fig. 1 is the method flow diagram of one winter wheat inversion method of leaf area index of the embodiment of the present invention.
Referring to Fig. 1, the winter wheat inversion method of leaf area index, comprising:
Step 101: obtaining the digital image of unmanned plane acquisition；
Step 102: the digital image being spliced to obtain corresponding orthography and point cloud data；
Step 103: calculating the orthography a variety of vegetation indexs of visible light wave range building；
It specifically includes:
It is calculated using the following equation greenery vegetation index GLA (Green leaf algorithm):
GLA=(2*G-R-B)/(2*G+R+B)
It is calculated using the following equation super green vegetation index ExG (Excess green index):
ExG=2*G-R-B
It is calculated using the following equation super green exceedingly popular difference vegetation index ExGR (Excess green minus excess
Red index):
ExGR=ExG-1.4*R-G
It is calculated using the following equation plant index VEG (Vegetativen):
VEG=G/ (R^a*B^ (1-a))
It is calculated using the following equation Woebbecke index W I (Woebbecke index):
WI=(G-B)/(R-B)
Visible light atmosphere impedance value is calculated using the following equation by index VARI (Visible atmospherically
Resistant index):
VARI=(G-R)/(G+R-B)
It is calculated using the following equation green red vegetation index GRVI (Green red vegetation index):
GRVI=(G-R)/(G+R)
Wherein G is the gray value of green channel, and R is the gray value of red channel, and B is the gray value of blue channel, and a is to be
Number, a=0.667.
Step 104: filtering out from a variety of vegetation indexs and meet in advance with the leaves of winter wheat area index correlation
If any one vegetation index of condition obtains the first inverting index；It specifically includes:
Various vegetation indexs are calculated using the leaf area index data of actual measurement and various vegetation index data to refer to leaf area
Correlation between number；
The vegetation index that the correlation between leaf area index meets preset condition is filtered out, high correlation vegetation is obtained
Index；
A kind of vegetation index is chosen from a variety of high correlation vegetation indexs, obtains the first inverting index.
Step 105: according to the height of the orthography and the point cloud data inverting winter wheat, obtaining the second inverting and refer to
Number；It specifically includes:
The orthography is split using the multi-scale division of eCognition software, by the orthography point
At winter wheat object and non-winter wheat object；
Centered on the winter wheat object, the non-winter wheat point cloud data of winter wheat objects perimeter is searched for, until the non-winter
Wheat point cloud data number is greater than 100；
Calculate separately the average value Hv-mean of winter wheat point cloud data and the average value Hnv- of non-winter wheat point cloud data
Hv-mean is subtracted Hnv-mean and obtains the relative altitude CHM of winter wheat by mean.
Step 106: binary inverting being carried out to the first inverting index and the second inverting index, obtains returning mould
Type；It specifically includes:
Regression model is established using following formula:
LAI=m*VI+n*CHM+p
Wherein LAI indicates leaf area index, and VI is the first inverting index, and CHM indicates inverting height, and m is vegetation index, n
For the coefficient of the winter wheat height value of inverting, p is the deviation constant of regression model.
Step 107: inverting being carried out to leaves of winter wheat area index using the regression model.
Embodiment two:
The embodiment is illustrated by for the domestic leaves of winter wheat area index inverting in Cangzhou, Hebei Province Cang County.
The breeding time of the domestic winter wheat in Cang County is the mid-June of annual October to next year, is divided into jointing stage, booting
Phase, florescence, pustulation period, milk stage, the changing rule of purpose and the entire breeding time LAI of winter wheat, whole by winter wheat according to the study
A breeding time is divided into 3 stages (being shown in Table 1): wheat booting period (boot stage pervious growth phase), florescence, (boot stage arrived
Before grouting) and the pustulation period (growth phase after grouting).Using in each breeding time for institute survey region winter wheat is almost the same
Fertilising and irrigation conditions.Test collected data according to this, to the leaf area index inverting situation in winter wheat boot stage into
Row research.
Table 1 studies area winter wheat phenological calendar
The acquisition of initial data: the acquisition of RGB image and winter wheat actual measurement height and actual measurement leaf area index.In observation
Hold: the unmanned plane image of leaves of winter wheat area index, winter wheat height, survey region, measurement data is for establishing prediction model.
Instrument: the instrument for measuring LAI is a kind of new smart phone LAI measuring system LAI Smart, with existing system ratio
Compared with the measurement mode of operation of LAI Smart will be more convenient, can adapt to the vegetation pattern of different height, can complete not
With the image taking under zenith angle, LAI Smart measurement method is divided into blue band index measurement and green degree index measurement, works as crop
When lower and growth is unprosperous, green degree index measurement can be used, but crop is higher, when growing densely, using indigo plant
Band index measurement is more accurate.Elevation carrection uses tape measure, and unmanned plane image is by the smart 3 professional version satellite unmanned planes of big boundary
Acquisition of taking photo by plane.The acquisition of leaves of winter wheat area index measured value and height measured value: experimental plot sets 21 sample prescriptions, each sample altogether
Aspect accumulates 1m*1m, and the leaf area index value of winter wheat in sample prescription is measured using LAI Smart leaf area index measuring instrument；With
Tape measure selects the small sampling sample prescription in quadrangle and 5, center in each square sample prescription to measure the height value of winter wheat, takes every 5
The average value of height in a small sampling side is the equal height of sample prescription (1m*1m) winter wheat.
Unmanned plane digital image obtains: unmanned plane during flying data obtain simultaneously with field measurement value, are based on unmanned plane during flying
Height is at 60 meters of relative altitude.In addition, believing to be obtained when unmanned plane image is spliced with practical more matched height
Breath, has laid multiple ground control points (quantity at control point is determined according to the area of winter wheat) with red brick, wherein one
Dividing control point is the photo control point for image mosaic, and the point generated after image joint is verified as check post in remaining control point
The precision of cloud data.
The acquisition of ground data: the acquisition instrument of ground data LAI is LAISmart, to leaf area index in every sample prescription
Actual measurement LAI data of the average value as the sample prescription, the acquisition of ground data height is small to five in sample prescription using tape measure
The height of sample prescription measures, and takes the average value of plant population height in five subquadrats for the actual measurement height value of the sample prescription, makes
With the synchronous GPS coordinate for obtaining ground sampling point of RTK (Real-time kinematic) measuring instrument.
The image features of Winter Wheat Population canopy calculate: three wave of red, green, blue is obtained from unmanned plane orthography
Section calculates 7 kinds of vegetation indexs according to different mathematical algorithms, referring to table 2.7 kinds of vegetation indexs all can be in horizontal zone
The spectrum variability of crop has preferable response.
27 kinds of vegetation index calculation method tables of table
Note: G, R, B respectively refer to green, red, blue channel gray value, and a is coefficient.
Winter wheat plant height extracts: (1) using the multi-scale division of eCognition software to unmanned plane orthography
It is split, image is divided into vegetation object and non-vegetation object；(2) centered on vegetation object, vegetation objects perimeter is searched for
Non- vegetation point cloud, using non-vegetation point cloud as background dot cloud, until non-vegetation point cloud number is greater than 100；(3) plant is calculated separately
By the average value of object-point cloud and non-vegetation object-point cloud, it is respectively labeled as Hv-mean, Hnv-mean, then subtracts Hv-mean
Hnv-mean is gone to obtain the relative altitude CH (Canopy Height) of the vegetation object；(4) step (2), step (3) are repeated directly
It is completed to all vegetation object search, the canopy height MODEL C HM of vegetation can be obtained.
The association analysis of LAI and vegetation index: the vegetation index of 7 kinds of visible light wave ranges and the winter that actual measurement obtains are small
Wheat leaf area index carries out correlation analysis, obtains a result as shown in table 3 below:
The correlativity of table 3 winter wheat parameter and leaf area index
The vegetation index of this 7 kinds of visible light wave ranges of VARI, GLA, EXG, GRVI, VEG, EXGR, WI as seen from table, in addition to
WI, there are certain syntenies between remaining vegetation index, and correlation refers to all 0.749 or more from visible light wave range vegetation
Vegetation index GLA, EXG, EXGR, VEG and the correlation of LAI are higher known to several correlations with leaf area index, height H and leaf
The correlation of area index LAI is also fine.In summary each factor chooses nondimensional greenery vegetation index GLA and wheat
Two variable parameters of the height H as inverting leaves of winter wheat area index
Linear relationship is established to GLA and LAI, obtained regression model are as follows:
LAI=20.5*GLA-1.82
The Adjusted R of the regression model2=0.285*.
The building of LAI model: it is anti-that binary is carried out to leaf area index with preferred vegetation index GLA and plant height CHM
It drills, obtains regression model are as follows:
LAI=16.48*GLA+2.44*CHM-1.96
That is m=16.48, n=2.44, p=-1.96, the related coefficient of the regression model are 0.658, in the regression model
Sig be 0.006 < 0.01,
To single argument inverting leaves of winter wheat area index: visible light wave range vegetation index GLA and leaves of winter wheat area index
The evaluation result of LAI inverse model is coefficient R2=0.285* (* indicates 0.05 level of signifiance), root-mean-square error RMSE=
0.587；To the coefficient R of the regression model of binary variable inverting leaves of winter wheat area index2=0.380*, root-mean-square error
RMSE=0.546.Leaves of winter wheat area index is carried out from the above, it can be seen that combining plant height CHM and greenery vegetation index GLA
The effect that inverting carries out inverting hair to leaves of winter wheat area index using greenery vegetation index GLA merely relatively is more preferable.
The present invention has following technical effect that two parameters of height H of selection vegetation index GLA and winter wheat are small to the winter
Wheat leaf area index LAI carries out binary regression, significantly improves the precision of inverting leaves of winter wheat area index.Utilizing GLA
In the model that equal vegetation indexs return, the fitting effect of model is obviously carried out than single utilization vegetation index after addition elevation information
The effect of leaves of winter wheat area index inverting is good.
Embodiment three:
Fig. 2 is the system construction drawing of three leaves of winter wheat area index Inversion System of the embodiment of the present invention.
Referring to fig. 2, the leaves of winter wheat area index Inversion System, comprising:
Image acquiring module 201, for obtaining the digital image of unmanned plane acquisition；
Splicing module 202 obtains corresponding orthography and point cloud data for being spliced to the digital image；
Vegetation index computing module 203, a variety of vegetation for calculating visible light wave range building to the orthography refer to
Number；
Exponent extracting module 204, for being filtered out from a variety of vegetation indexs and the leaves of winter wheat area index
Correlation meets any one vegetation index of preset condition, obtains the first inverting index；
Height inverting module 205 is obtained for the height according to the orthography and the point cloud data inverting winter wheat
To the second inverting index；
Model building module 206, for carrying out binary inverting to the first inverting index and the second inverting index,
Obtain regression model；
Inverting module 207, for carrying out inverting to leaves of winter wheat area index using the regression model.
As an alternative embodiment, the vegetation index computing module 203, specifically includes:
First exponent calculation unit, for being calculated using the following equation greenery vegetation index GLA:
GLA=(2*G-R-B)/(2*G+R+B)
Second exponent calculation unit, for being calculated using the following equation super green vegetation index ExG:
ExG=2*G-R-B
Third exponent calculation unit, for being calculated using the following equation super green exceedingly popular difference vegetation index ExGR:
ExGR=ExG-1.4*R-G
4th exponent calculation unit, for being calculated using the following equation plant index VEG:
VEG=G/ (R^a*B^ (1-a))
5th exponent calculation unit, for being calculated using the following equation Woebbecke index W I:
WI=(G-B)/(R-B)
6th exponent calculation unit, for being calculated using the following equation visible light atmosphere impedance value by index VARI:
VARI=(G-R)/(G+R-B)
7th exponent calculation unit, for being calculated using the following equation green red vegetation index GRVI:
GRVI=(G-R)/(G+R)
Wherein G is the gray value of green channel, and R is the gray value of red channel, and B is the gray value of blue channel, and a is to be
Number, a=0.667.
As an alternative embodiment, the exponent extracting module 204, specifically includes:
Correlation calculations unit, it is various for being calculated using the leaf area index data and various vegetation index data surveyed
Correlation between vegetation index and leaf area index；
High correlation screening unit, the correlation for filtering out between leaf area index meet the vegetation of preset condition
Index obtains high correlation vegetation index；
It is anti-to obtain first for choosing a kind of vegetation index from a variety of high correlation vegetation indexs for index selection unit
Drill index.
As an alternative embodiment, the model building module 206, specifically includes:
Modeling unit, for establishing regression model using following formula:
LAI=m*VI+n*CHM+p
Wherein LAI indicates leaf area index, and VI is the first inverting index, and CHM indicates the height of inverting, and m is vegetation index,
N is the coefficient of the winter wheat height value of inverting, and p is the deviation constant of regression model.
As an alternative embodiment, the height inverting module 205, specifically includes:
Cutting unit will for being split using the multi-scale division of eCognition software to the orthography
The orthography is divided into winter wheat object and non-winter wheat object；
Perimeter unit, for searching for the non-winter wheat of winter wheat objects perimeter centered on the winter wheat object
Point cloud data, until non-winter wheat point cloud data number is greater than 100；
Height calculation unit, for calculating separately the average value Hv-mean and non-winter wheat point cloud of winter wheat point cloud data
Hv-mean is subtracted Hnv-mean and obtains the relative altitude CHM of winter wheat by the average value Hnv-mean of data.
The specific embodiment provided according to the present invention, the invention discloses following technical effects: leaves of winter wheat of the invention
Area index inversion method and system, by vertical structure, i.e. height factor is fused in leaf area index refutation process, relative to
Carrying out inverting to leaf area index using single factors has higher inversion accuracy.
For the system disclosed in the embodiment, since it is corresponded to the methods disclosed in the examples, so the ratio of description
Relatively simple, reference may be made to the description of the method.
Used herein a specific example illustrates the principle and implementation of the invention, and above embodiments are said
It is bright to be merely used to help understand method and its core concept of the invention；At the same time, for those skilled in the art, foundation
Thought of the invention, there will be changes in the specific implementation manner and application range.In conclusion the content of the present specification is not
It is interpreted as limitation of the present invention.
Claims (10)
1. a kind of winter wheat inversion method of leaf area index characterized by comprising
Obtain the digital image of unmanned plane acquisition；
The digital image is spliced to obtain corresponding orthography and point cloud data；
A variety of vegetation indexs of visible light wave range building are calculated the orthography；
It is filtered out from a variety of vegetation indexs and meets any of preset condition with the leaves of winter wheat area index correlation
A kind of vegetation index obtains the first inverting index；
According to the height of the orthography and the point cloud data inverting winter wheat, the second inverting index is obtained；
Binary inverting is carried out to the first inverting index and the second inverting index, obtains regression model；
Inverting is carried out to leaves of winter wheat area index using the regression model.
2. a kind of winter wheat inversion method of leaf area index according to claim 1, which is characterized in that it is described to it is described just
Projection picture calculates a variety of vegetation indexs of visible light wave range building, specifically includes:
It is calculated using the following equation greenery vegetation index GLA:
GLA=(2*G-R-B)/(2*G+R+B)
It is calculated using the following equation super green vegetation index ExG:
ExG=2*G-R-B
It is calculated using the following equation super green exceedingly popular difference vegetation index ExGR:
ExGR=ExG-1.4*R-G
It is calculated using the following equation plant index VEG:
VEG=G/ (R^a*B^ (1-a))
It is calculated using the following equation Woebbecke index W I:
WI=(G-B)/(R-B)
Visible light atmosphere impedance value is calculated using the following equation by index VARI:
VARI=(G-R)/(G+R-B)
It is calculated using the following equation green red vegetation index GRVI:
GRVI=(G-R)/(G+R)
Wherein G is the gray value of green channel, and R is the gray value of red channel, and B is the gray value of blue channel, and a is coefficient, a
=0.667.
3. a kind of winter wheat inversion method of leaf area index according to claim 1, which is characterized in that described from a variety of institutes
Any one vegetation index for filtering out in vegetation index and meeting preset condition with the leaves of winter wheat area index correlation is stated,
The first inverting index is obtained, is specifically included:
Using the leaf area index data of actual measurement and various vegetation index data calculate various vegetation indexs and leaf area index it
Between correlation；
The vegetation index that the correlation between leaf area index meets preset condition is filtered out, high correlation vegetation is obtained and refers to
Number；
A kind of vegetation index is chosen from a variety of high correlation vegetation indexs, obtains the first inverting index.
4. a kind of winter wheat inversion method of leaf area index according to claim 1, which is characterized in that described to described
One inverting index and the second inverting index carry out binary inverting, obtain regression model, specifically include:
Regression model is established using following formula:
LAI=m*VI+n*CHM+p
Wherein LAI indicates leaf area index, and VI is the first inverting index, and CHM indicates the height of inverting, and m is vegetation index, and n is
The coefficient of the winter wheat height value of inverting, p are the deviation constant of regression model.
5. a kind of winter wheat inversion method of leaf area index according to claim 1, which is characterized in that described according to
The height of orthography and the point cloud data inverting winter wheat obtains the second inverting index, specifically includes:
The orthography is split using the multi-scale division of eCognition software, the orthography is divided into the winter
Wheat object and non-winter wheat object；
Centered on the winter wheat object, the non-winter wheat point cloud data of winter wheat objects perimeter is searched for, until non-winter wheat
Point cloud data number is greater than 100；
The average value Hv-mean of winter wheat point cloud data and the average value Hnv-mean of non-winter wheat point cloud data are calculated separately,
Hv-mean is subtracted into Hnv-mean and obtains the relative altitude CHM of winter wheat.
6. a kind of leaves of winter wheat area index Inversion System characterized by comprising
Image acquiring module, for obtaining the digital image of unmanned plane acquisition；
Splicing module obtains corresponding orthography and point cloud data for being spliced to the digital image；
Vegetation index computing module, for calculating the orthography a variety of vegetation indexs of visible light wave range building；
Exponent extracting module, it is full with the leaves of winter wheat area index correlation for being filtered out from a variety of vegetation indexs
Any one vegetation index of sufficient preset condition obtains the first inverting index；
Height inverting module obtains second for the height according to the orthography and the point cloud data inverting winter wheat
Inverting index；
Model building module is returned for carrying out binary inverting to the first inverting index and the second inverting index
Return model；
Inverting module, for carrying out inverting to leaves of winter wheat area index using the regression model.
7. a kind of leaves of winter wheat area index Inversion System according to claim 6, which is characterized in that the vegetation index
Computing module specifically includes:
First exponent calculation unit, for being calculated using the following equation greenery vegetation index GLA:
GLA=(2*G-R-B)/(2*G+R+B)
Second exponent calculation unit, for being calculated using the following equation super green vegetation index ExG:
ExG=2*G-R-B
Third exponent calculation unit, for being calculated using the following equation super green exceedingly popular difference vegetation index ExGR:
ExGR=ExG-1.4*R-G
4th exponent calculation unit, for being calculated using the following equation plant index VEG:
VEG=G/ (R^a*B^ (1-a))
5th exponent calculation unit, for being calculated using the following equation Woebbecke index W I:
WI=(G-B)/(R-B)
6th exponent calculation unit, for being calculated using the following equation visible light atmosphere impedance value by index VARI:
VARI=(G-R)/(G+R-B)
7th exponent calculation unit, for being calculated using the following equation green red vegetation index GRVI:
GRVI=(G-R)/(G+R)
Wherein G is the gray value of green channel, and R is the gray value of red channel, and B is the gray value of blue channel, and a is coefficient, a
=0.667.
8. a kind of leaves of winter wheat area index Inversion System according to claim 6, which is characterized in that the exponent extracting
Module specifically includes:
Correlation calculations unit, for calculating various vegetation using the leaf area index data and various vegetation index data surveyed
Correlation between index and leaf area index；
High correlation screening unit, the vegetation that the correlation for filtering out between leaf area index meets preset condition refer to
Number, obtains high correlation vegetation index；
Index selection unit obtains the first inverting and refers to for choosing a kind of vegetation index from a variety of high correlation vegetation indexs
Number.
9. a kind of leaves of winter wheat area index Inversion System according to claim 6, which is characterized in that the model foundation
Module specifically includes:
Modeling unit, for establishing regression model using following formula:
LAI=m*VI+n*CHM+p
Wherein LAI indicates leaf area index, and VI is the first inverting index, and CHM indicates the height of inverting, and m is vegetation index, and n is
The coefficient of the winter wheat height value of inverting, p are the deviation constant of regression model.
10. a kind of leaves of winter wheat area index Inversion System according to claim 6, which is characterized in that the height is anti-
Module is drilled, is specifically included:
Cutting unit will be described for being split using the multi-scale division of eCognition software to the orthography
Orthography is divided into winter wheat object and non-winter wheat object；
Perimeter unit, for searching for the non-winter wheat point cloud of winter wheat objects perimeter centered on the winter wheat object
Data, until non-winter wheat point cloud data number is greater than 100；
Height calculation unit, for calculating separately the average value Hv-mean and non-winter wheat point cloud data of winter wheat point cloud data
Average value Hnv-mean, Hv-mean is subtracted into Hnv-mean and obtains the relative altitude CHM of winter wheat.
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