CN107437267B - Vegetation region high spectrum image analogy method - Google Patents
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Abstract
The present invention relates to a kind of vegetation region high spectrum image analogy methods, the calculating of vegetation biochemical parameter is carried out for multispectral data, obtain vegetation biochemical parameter image, and then typical vegetation radiative transfer model PROSAIL is utilized, vegetation biochemical parameter is input to model, realizes high spectrum image simulation, high-precision spectrum picture can be obtained, information content can be improved, low manufacture cost is high-efficient.
Description
Technical field
The invention belongs to a kind of spectrum picture analogy methods, and in particular to a kind of vegetation region high spectrum image analogy method.
Background technique
High spectrum image simulation is the image data that high spectral resolution is calculated by model construction, existing method
Be mainly based upon multispectral image data classified, abundance inverting, and object light modal data in combination carries out spectrum charting or line
Property mixing obtain high spectrum image, this method requires to obtain high-precision synchronous spectrum data, to ensure the precision of image simulation,
And limited by algorithm, Subsection spectrum is mainly realized in multispectral data simulation hyperspectral image data, but there is no true
It is positive to improve information content.
Summary of the invention
The present invention, which is directed to, simulates high-spectral data process based on multispectral data currently with the methods of classification, abundance inverting
Present in synchrodata be difficult to obtain problem, devise a kind of high spectrum image analogy method for vegetation area, first
The calculating of vegetation biochemical parameter is carried out for multispectral image data, obtains vegetation biochemical parameter image, and then utilize typical vegetation
Vegetation biochemical parameter figure is input to model by radiative transfer model PROSAIL, is realized high spectrum image simulation, can be obtained high-precision
The spectrum picture of degree, can improve information content, and low manufacture cost is high-efficient.
In order to achieve the above object, the present invention has following technical solution:
A kind of vegetation region high spectrum image analogy method of the invention, there is following steps:
For a M row, N column, L wave band multispectral image data Rmulti, for the high spectrum image of vegetation area
Steps are as follows for analogy method:
(1), sample data generation step:
1) value: blade construction parameter N: value range 0.5~4, step-length 0.5；Chlorophyll content Cab: value range 5~
80, step-length 10, the unit of chlorophyll content Cab is μ g/cm2；Dry matter weight of leaf content Cm: value range 0.003~0.033,
The unit of step-length 0.003, dry matter weight of leaf content Cm is g/cm2；Carotenoid content Car: value range 0.5~16, step-length
1, the unit of carotenoid content Car is μ g/cm2；Leaf area index LAI: value range 1.5~6, step-length 0.5.2) to upper
5 parameter values for stating step 1) carry out 9860 groups of biochemical parameters that permutation and combination obtains, and following fixed input parameter is arranged,
Form 9860 groups of input datas:
The fixed input parameter:
Brown cellulose content is 0, water content 0.024cm, and hot spot-effect parameter is 0.01, and soil lightness parameter is 1, the sun
Zenith angle is 30 °, and view zenith angle is 10 °, and relative bearing is 0 °；
3) above-mentioned 9860 groups of input datas are directed to, is calculated using PROSAIL model, acquires corresponding canopy reflectance spectrum
Data form matrix；
4) the above-mentioned canopy reflectance spectrum data got are directed to, according to the spectral response functions of the multispectral data, are carried out
Equivalent Calculation obtains corresponding multispectral reflectivity data, and i-th of multispectral data corresponding for kth group input data
The reflectivity ρ of wave bandmulti(k, λ (i)) are as follows:
Wherein fλ(i)It (1, j) is the corresponding wave-length coverage of i-th of wave band, nw (i) is corresponding number, fλ(i)(2, j) and the
The corresponding spectral response functions of the wave-length coverage of i wave band, ρ (k, fλ(i)(1, j)) be kth group input data in i-th
The corresponding canopy reflectance spectrum data of the wave-length coverage of wave band obtain matrix after carrying out Equivalent Calculation to all samples and wave band
ρmulti(ns, L), ns correspond to sample group number 9860, and L corresponds to multispectral data wave band number；
(2), the biochemical parameter and the corresponding multispectral reflectivity data ρ acquired obtained for said combinationmulti(ns,
L), the step of progress model construction, building are as follows:
1) sample data is divided into a part as training data, another part is as test data, for above-mentioned steps
(1) data of value, the number of training randomly selected are 7888, and test sample number is 1972；
2) it is directed to training data, is supported vector machine model and kernel function setting, supporting vector machine model is using recurrence
Common epsilon-SVR model, penalty parameter c are its relevant parameters in modeling；Kernel function uses RBF kernel function, and g is its pass
Join parameter；
3) after setting supporting vector machine model and kernel function, for epsilon-SVR model and RBF kernel function,
Relevant parameter is c and g, and training data is inputted support vector machines, can obtain biochemistry using the relevant parameter value of system default
Parameter computation model；
(3) for blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content Cm, carotenoid content Car,
Corresponding relevant parameter c and g value is calculated according to above-mentioned model and kernel function setting in leaf area index LAI, in turn, for
Multispectral image data are based on epsilon-SVR model, gaussian radial basis function RBF kernel function and corresponding c and g value, utilize
Support vector machines carries out Parameter Map calculating respectively, obtains blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content
The Parameter Map of Cm, carotenoid content Car, leaf area index LAI；
(4) blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content Cm, carotenoid content are being obtained
On the basis of Car, leaf area index LAI Parameter Map, following fixed input parameter is arranged to pixel each on image:
Brown cellulose content is 0, water content 0.024cm, and hot spot-effect parameter is 0.01, and soil lightness parameter is 1, the sun
Zenith angle is 30 °, and view zenith angle is 10 °, and relative bearing is 0 °；
In turn, in conjunction with the Parameter Map of above-mentioned 5 be calculated kind parameter, calculate by pixel by PROSAIL model
It realizes high spectrum image simulation, obtains vegetation area high spectrum image.
Wherein, the step (2) further include: in order to obtain optimal biochemical parameter computation model, for the test number
According to optimizing the value of relevant parameter c and g using grid-search algorithms.
Wherein, the value of the relevant parameter c and g of the optimization are provided that the value range of c, g are disposed as 2-8~28,
Step-size in search is set as 2i, wherein the value of i is set as 1.
Due to taking above technical scheme, the present invention has the advantages that
The present invention carries out the calculating of vegetation biochemical parameter for multispectral data, obtains vegetation biochemical parameter image, Jin Erli
With typical vegetation radiative transfer model PROSAIL, vegetation biochemical parameter is input to model, realizes high spectrum image simulation, energy
High-precision spectrum picture is obtained, information content can be improved, low manufacture cost is high-efficient.
Detailed description of the invention
Fig. 1 is general flow chart of the invention；
Fig. 2 is sample of the present invention data generation module flow chart；
Fig. 3 is model construction module flow chart of the present invention；
Fig. 4 is that biochemical parameter figure of the present invention constructs module flow diagram；
Fig. 5 is high spectrum image analog module flow chart of the present invention.
Specific embodiment
The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention..
Referring to Fig. 1-5:
A kind of vegetation region high spectrum image analogy method of the invention,
Overall procedure of the invention is divided into four parts: " sample data generation module ", " model construction module ", " biochemistry
Parameter Map computing module ", " high-spectral data analog module ", referring to Fig. 1,
Data explanation:
1, multispectral image RmultiIt is a three-dimensional matrice: M row, N column, L wave band；
2, multispectral image central wavelength array λ (L), wherein λ (i) indicates the central wavelength of i-th of wave band；
3, the total L of the spectral response functions of multispectral image, fλ(i)(2, nw (i)) for i-th wave band (1 (i≤L's)
Spectral response functions are the arrays of one two column, and first is classified as wavelength, and second is classified as spectral response value, and nw (i) rings for the spectrum
Answer the corresponding line number of function.
Steps are as follows for a kind of vegetation region high spectrum image analogy method of the invention:
One, sample data generation module, referring to fig. 2,
Step 1: carrying out the setting of biochemical parameter value: blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf are contained
Cm, carotenoid content Car, leaf area index LAI are measured, carries out value according to the range and fixed step size of following table 1, it is right respectively
Answer vector Nv, Cabv, Cmv, Carv, LAIv；
1 parameter of table and its value
Step 2: permutation and combination setting PROSAIL mode input data: being carried out to vector Nv, Cabv, Cmv, Carv, LAIv
9860 groups of obtained biochemical parameters, and it is as shown in the table that other fixation input parameters are arranged, thus 9860 groups of input datas are formed,
It is denoted as matrix Para (ns, np), wherein ns=9860, np corresponds to the number of parameters of input data, is the every a line of 12, Para matrix
Represent one group of input data；
The fixed input parameter of 2 model of table
Step 3: acquiring canopy reflectance spectrum data: for above-mentioned 9860 groups of input data Para (ns, np), utilizing
PROSAIL model is calculated, and corresponding canopy reflectance spectrum data are acquired, and is formed matrix ρ (ns, nb), wherein ns=9860, right
9860 groups of input datas are answered, nb corresponds to the wavelength information of canopy reflectance spectrum data, and wavelength information is from 400nm to 2500nm, spectrum
Between be divided into 1nm, ρ (k, *) indicates the corresponding canopy reflectance spectrum data of kth group input data；
Step 4: progress spectrum is equivalent, obtains multiple-spectrum canopy reflectivity data: reflecting for the above-mentioned canopy got
Rate data carry out Equivalent Calculation, obtain corresponding multispectral reflectivity number according to the spectral response functions of the multispectral data
According to.For the reflectivity ρ of i-th of wave band of the corresponding multispectral data of kth group input datamulti(k, λ (i)) are as follows:
Wherein ρ (k, fλ(i)(1, j)) it be the wavelength of corresponding kth group input data is fλ(i)Canopy reflectance spectrum number when (1, j)
According to.Matrix ρ is obtained after carrying out Equivalent Calculation to all samples and wave bandmulti(ns, L), ns correspond to sample group number 9860, and L is corresponding
Multispectral data wave band number.
Two, model construction module, referring to Fig. 3,
Step 1: sample data prepares: input data Para (ns, np) and corresponding multiple-spectrum canopy data ρmulti
(ns, L) forms 9860 groups of sample datas, is classified as two parts, from 7888 sample datas randomly selected as training number
According to remaining 1972 data are as test data；
Step 2: supporting vector machine model and kernel function setting: supporting vector machine model is using common in regression modeling
Epsilon-SVR model, penalty parameter c are its relevant parameters；Kernel function uses RBF kernel function, and g is its relevant parameter；
Step 3: relevant parameter optimization and determine: for epsilon-SVR model and RBF kernel function, relevant parameter be c and
g；After setting support vector machines type and kernel function type, training data is inputted into support vector machines, utilizes system default
Relevant parameter value biochemical parameter computation model can be obtained.In order to obtain optimal computation model, for test data, use
Grid-search algorithms are associated parameter optimization；Grid data service is a kind of parameter optimization method more commonly used at present, first
The value range of optimizing parameter c and g and the step-length of search are determined respectively, then construct regression model respectively using each group parameter
Precision of prediction is obtained, optimal parameter combination is finally selected；The setting of relevant parameter c, g of optimization are as shown in table 3 below, wherein
C, the value range of g is disposed as 2-8~28, step-size in search is set as 2i, wherein the value of i is set as 1, i.e. value is set as 2-8、
2-7、2-6、2i…26、27、28。
Parameter setting when table 3 is based on grid data service parameter optimization
Three, biochemical parameter figure constructs module, referring to fig. 4,
Step 1: the computation model of parameter N is established: according to the model building method of module 2, being carried out to blade construction parameter N
Model construction acquires corresponding c and g value.
Step 2: the building of the computation model of parameter Cab: according to the model building method of module 2, to chlorophyll content Cab into
Row model construction acquires corresponding c and g value.
Step 3: the computation model building of parameter Car: according to the model building method of module 2, to carotenoid content
Car carries out model construction, acquires corresponding c and g value.
Step 4: the computation model building of parameter Cm: according to the model building method of module 2, to dry matter weight of leaf content Cm
Model construction is carried out, corresponding c and g value is acquired.
Step 5: the building of the computation model of parameter LAI: according to the model building method of module 2, to leaf area index LAI into
Row model construction acquires corresponding c and g value.
Step 6: carrying out biochemical parameter based on multispectral image data and solve charting: being calculated by above-mentioned 5 biochemical parameters
Corresponding relevant parameter-c and-g value are obtained, epsilon-SVR model, gaussian radial basis function (RBF) kernel function, respectively to more are based on
Spectral image data RmultiCorresponding Parameter Map calculating is carried out, blade construction parameter N, chlorophyll content Cab, blade dry are obtained
Matter content Cm, carotenoid content Car, leaf area index LAI Parameter Map, corresponding Parameter Map matrix: para_imgN、
para_imgCab、para_imgCm、para_imgCar、para_imgLAI, matrix size is M row, N column.
Four, high spectrum image analog module, referring to Fig. 5,
Step 1: input setting: obtaining blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content Cm, class
On the basis of carotene carotene content Car, leaf area index LAI Parameter Map, other are arranged to pixel each on image and fixes input parameter
Shown in following 4:
The fixed input parameter of 4 model of table
Step 2: high spectrum image simulation: in conjunction with 5 width Parameter Maps of inverting: para_imgN、para_imgCab、para_
imgCm、para_imgCar、para_imgLAI, calculate by pixel by PROSAIL model and realize high spectrum image simulation, obtain
To vegetation area high spectrum image.EO-1 hyperion analog image matrix be a three-dimensional matrice hyper_img: wherein M row, N column,
Wave band number is 400nm to 2500nm range, spectrum interval 1nm.
PROSAIL model: PROSAIL model is to combine the vegetation radiative transfer model constituted with SAIL by PROSPECT,
It carries out Reflectivity for Growing Season spectrum generation according to vegetation structure, biochemical parameter, can calculate mainly for broad-leaved Vegetation canopy
To for 400nm to 2500nm range, spectrum interval is the hyperspectral remote sensing of 1nm.Wherein PROSPECT model be by
The leaf reflectance model that Jacquemoud and Baret is proposed first in nineteen ninety, the model improve in nineteen ninety-five.
PROSPECT model can simulate reflectivity and transmissivity of the blade within the scope of visible light to short infrared wave band, they are seen
Work is the function of blade construction parameter and biochemical parameter.SAIL model is an extension of Suits model, can preferably be embodied
The leaf area index LAI and Leaf angle inclination distribution LAD of horizontal homogeneous canopy are to bidirectional reflectance--distribution function BRDF variation tendency
It influences, is widely applied by remote sensing academia.
Epsilon-SVR model and gaussian radial basis function (RBF) kernel function: regression analysis is being carried out using support vector machines
In the process, it is necessary to be supported the selection of vector machine model and the selection of kernel function.
For regression problem, supporting vector machine model has epsilon-SVR model, nu-SVR model etc., in regression modeling
Currently used is epsilon-SVR model, and penalty parameter c is its relevant parameter；
When carrying out regression analysis using support vector machines, kernel function to calculate from lower dimensional space to higher dimensional space bring
Complexity substantially reduces, so that support vector machines completes the conversion from non-linear to linear；The linear core of common kernel function
Function, Polynomial kernel function, gaussian radial basis function (RBF) kernel function, multilayer perceptron (Sigmoid) kernel function etc..Gauss is radial
Base (RBF) kernel function is suitable for the case where linearly inseparable, and number of parameters is moderate, therefore mostly uses the kernel function, and g is its pass
Join parameter.
Blade construction parameter N is the parameter for describing blade and being divided into how many layers.
Leaf area index (leaf area index) is called leaf-area coefficient, refers to plant leaf blade in land area of one unit
The gross area accounts for the multiple of land area.
View zenith angle, observed azimuth in observation geometry can be obtained by remote sensor observation condition, usually be referred to by user
It is fixed.
Solar zenith angle, solar azimuth are calculated according to test block longitude and latitude, image acquisition time and are obtained.Due to sun height
Spending the sum of angle and solar zenith angle is 90 °, therefore calculates solar elevation and be just readily available solar zenith angle, altitude of the sun
The calculation formula at angle is as follows:
In formula,Indicate that solar elevation, δ indicate solar declination,Indicate that local latitude, t indicate solar hour angle.The sun
Azimuthal calculation formula is as follows:
In formula, A indicates solar azimuth,Indicate that solar elevation, δ indicate solar declination,Indicate local latitude.
Relative bearing is the difference of solar azimuth and observed azimuth.
The parameters such as soil lightness parameter, hot spot-effect parameter, equivalent water thickness, brown pigment are according to test block concrete condition
It is set, and is specified by user.
Step-length is that calculative numerical value is uniformly divided into several sections, and the length in each section is just step-length, i.e. phase
The interval of adjacent two values, such as: the value of some parameter setting is from 0-10, is step-length with 1, then the vector formed are as follows: 0,1,
2,3,4,5,6,7,8,9,10。
Grid data service is a kind of parameter optimization method more commonly used at present, determines optimizing parameter c's and g respectively first
Then value range and the step-length of search construct regression model using each group parameter respectively and obtain precision of prediction, finally select
Optimal parameter combination.
What has been described above is only a preferred embodiment of the present invention, it is noted that for those of ordinary skill in the art
For, without departing from the concept of the premise of the invention, various modifications and improvements can be made, these belong to the present invention
Protection scope.
Claims (3)
1. a kind of vegetation region high spectrum image analogy method, it is characterised in that there is following steps:
For a M row, N column, L wave band multispectral image data Rmulti, simulated for the high spectrum image of vegetation area
Method and step is as follows:
(1), sample data generation step:
1) value: blade construction parameter N: value range 0.5~4, step-length 0.5；Chlorophyll content Cab: value range 5~80,
Step-length 10, the unit of chlorophyll content Cab are μ g/cm2；Dry matter weight of leaf content Cm: value range 0.003~0.033, step-length
0.003, the unit of dry matter weight of leaf content Cm is g/cm2；Carotenoid content Car: value range 0.5~16, step-length 1, class
The unit of carotene carotene content Car is μ g/cm2；Leaf area index LAI: value range 1.5~6, step-length 0.5；
2) to above-mentioned steps 1) 5 parameter values carry out the obtained 9860 groups of biochemical parameters of permutation and combination, and be arranged following solid
Surely parameter is inputted, 9860 groups of input datas are formed:
The fixed input parameter:
Brown cellulose content is 0, water content 0.024cm, and hot spot-effect parameter is 0.01, and soil lightness parameter is 1, sun zenith
Angle is 30 °, and view zenith angle is 10 °, and relative bearing is 0 °；
3) above-mentioned 9860 groups of input datas are directed to, is calculated using PROSAIL model, acquires corresponding canopy reflectance spectrum number
According to formation matrix；
4) the above-mentioned canopy reflectance spectrum data got are directed to, according to the spectral response functions of the multispectral data, are carried out equivalent
It calculates, obtains corresponding multispectral reflectivity data, i-th of wave band of multispectral data corresponding for kth group input data
Reflectivity ρmulti(k, λ (i)) are as follows:
Wherein fλ(i)It (1, j) is the corresponding wave-length coverage of i-th of wave band, nw (i) is the corresponding line number of the spectral response functions,
fλ(i)(2, j) spectral response functions corresponding with the wave-length coverage of i-th of wave band, ρ (k, fλ(i)(1, j)) it is that kth group inputs number
The canopy reflectance spectrum data corresponding with the wave-length coverage of i-th of wave band in carry out Equivalent Calculation to all samples and wave band
After obtain matrix ρmulti(ns, L), ns correspond to sample group number 9860, and L corresponds to multispectral data wave band number；
(2), the biochemical parameter and the corresponding multispectral reflectivity data ρ acquired obtained for said combinationmulti(ns, L), into
The step of row model construction, building are as follows:
1) sample data is divided into a part as training data, another part takes as test data for above-mentioned steps (1)
The data of value, the number of training randomly selected are 7888, and test sample number is 1972；
2) it is directed to training data, is supported vector machine model and kernel function setting, supporting vector machine model uses regression modeling
In common epsilon-SVR model, penalty parameter c is its relevant parameter；Kernel function uses RBF kernel function, and g is its association ginseng
Number；
3) after setting supporting vector machine model and kernel function, for epsilon-SVR model and RBF kernel function, association
Parameter is c and g, and training data is inputted support vector machines, can be given birth to using the relevant parameter value that support vector machines is defaulted
Change parameter computation model；
(3) blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content Cm, carotenoid content Car, blade face are directed to
Corresponding relevant parameter c and g value is calculated according to above-mentioned model and kernel function setting, in turn, for mostly light in product index LAI
Image data is composed, epsilon-SVR model, gaussian radial basis function RBF kernel function and corresponding c and g value is based on, utilizes support
Vector machine carries out Parameter Map calculating respectively, obtains blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content Cm, class
The Parameter Map of carotene carotene content Car, leaf area index LAI；
(4) blade construction parameter N, chlorophyll content Cab, dry matter weight of leaf content Cm, carotenoid content Car, leaf are being obtained
On the basis of area index LAI Parameter Map, following fixed input parameter is arranged to pixel each on image:
Brown cellulose content is 0, water content 0.024cm, and hot spot-effect parameter is 0.01, and soil lightness parameter is 1, sun zenith
Angle is 30 °, and view zenith angle is 10 °, and relative bearing is 0 °；
In turn, in conjunction with the Parameter Map of above-mentioned 5 be calculated kind parameter, calculate by pixel by PROSAIL model and realize
High spectrum image simulation, obtains vegetation area high spectrum image.
2. a kind of vegetation region high spectrum image analogy method as described in claim 1, it is characterised in that the step (2) is also wrapped
It includes: in order to obtain optimal biochemical parameter computation model, for the test data, optimizing pass using grid-search algorithms
Join the value of parameter c and g.
3. a kind of vegetation region high spectrum image analogy method as described in claim 1, it is characterised in that the relevant parameter c and g
Value be provided that the value range of c, g are disposed as 2-8~28, step-size in search is set as 2i, wherein the value of i is set as 1.
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| CN108196266B (en) * | 2017-12-29 | 2021-07-02 | 中山大学 | Vegetation canopy three-dimensional radiation transmission simulation method based on Lidar data |
| CN108195770B (en) * | 2018-01-03 | 2020-03-31 | 电子科技大学 | Semi-empirical estimation method for chlorophyll content based on PROSAIL model |
| CN110544277B (en) * | 2019-08-12 | 2023-01-10 | 蔡建楠 | Method for inverting subtropical vegetation leaf area index by unmanned aerial vehicle-mounted hyperspectral imager |
| CN110579186B (en) * | 2019-08-26 | 2020-07-21 | 中国农业大学 | Crop growth monitoring method based on inversion of leaf area index by inverse Gaussian process |
| CN110827368B (en) * | 2019-10-29 | 2021-08-10 | 中国科学院遥感与数字地球研究所 | Hyperspectral image simulation method under cloud condition |
| CN111965117A (en) * | 2020-08-04 | 2020-11-20 | 中国水利水电科学研究院 | Winter wheat moisture monitoring method and system based on PROSPECT model |
| CN113340825B (en) * | 2021-06-17 | 2022-02-15 | 重庆大学 | Method for measuring and calculating chlorophyll a concentration under high-turbidity background interference |
| CN113722990B (en) * | 2021-08-23 | 2023-10-10 | 中国地质大学（武汉） | Leaf dry matter content inversion method and system based on vegetation index three-dimensional matrix |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102878957A (en) * | 2012-09-26 | 2013-01-16 | 安徽大学 | Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters |
| CN105352895A (en) * | 2015-11-02 | 2016-02-24 | 北京理工大学 | Hyperspectral remote sensing data vegetation information extraction method |
Family Cites Families (1)
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-
2016
- 2016-05-26 CN CN201610361984.5A patent/CN107437267B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102878957A (en) * | 2012-09-26 | 2013-01-16 | 安徽大学 | Leaf area index and chlorophyll content inversion method based on remote sensing image optimization PROSAIL model parameters |
| CN105352895A (en) * | 2015-11-02 | 2016-02-24 | 北京理工大学 | Hyperspectral remote sensing data vegetation information extraction method |
Non-Patent Citations (2)
| Title |
|---|
| Comparison of radiative transfer model inversions to estimate vegetation physiological status based on hyperspectral data;Sebastian Preidl等;《2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)》;20111118;第1-4页 * |
| 基于PROSAIL辐射传输模型的毛竹林叶面积指数遥感反演;谷成燕等;《应用生态学报》;20130831;第24卷(第8期);第2248-2256页 * |
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