CN105738293B - The remote sensing quantitative inversion method and system of a kind of crop physical and chemical parameter - Google Patents
The remote sensing quantitative inversion method and system of a kind of crop physical and chemical parameter Download PDFInfo
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Abstract
This application discloses the remote sensing quantitative inversion method and system of a kind of crop physical and chemical parameter, by LUTsEqual number of LUT is divided into according to spectral band numbersi, by each LUTsiIt is ranked up to obtain LUTsi_sort, for each LUTsi_sortUsing binary search method, the simulation Vegetation canopy reflectivity data for meeting preset requirement with true Vegetation canopy reflectivity data similarity is found out, by each simulation Vegetation canopy reflectivity data and the data in LUTsThe content of middle corresponding entry is built form in the form of correspondence and obtains LUTs_new, calculate LUTs_newThe TD of middle each simulation Vegetation canopy reflectivity data and corresponding true Vegetation canopy reflectivity data, using the corresponding crop physical and chemical parameter data of minimum value in TD as the optimal solution of lookup table algorithm.The present invention considers the size and Orientation characteristic of Vegetation canopy reflectivity data, and realize reduces computation complexity while crop physical and chemical parameter inverting numerical precision is improved.
Description
Technical field
The present invention relates to crop physical and chemical parameter technical field, more specifically, being related to a kind of remote sensing of crop physical and chemical parameter
Quantitative inversion method and system.
Background technology
Crop physical and chemical parameter is a kind of key index for characterizing crop pattern structure and physiological activity, is joined including plant physiology
Several (such as population density, plant height, leaf area index etc.) and crop biochemical parameter (such as chlorophyll content, nitrogen content, class are recklessly
Radish cellulose content etc.), crop physical and chemical parameter the crop groups Growing state survey in precision agriculture field, quality estimation, production forecast,
Pestforecasting and the formulation of crop field management strategy and implementation etc., there is extensive and important theoretical significance
And application value realistic.
At present, the remote sensing quantitative inversion method of crop physical and chemical parameter is broadly divided into statistics class method and mechanism class method, system
Count the statistical relationship structure quantitative inversion model between class method Main Basiss crop physical and chemical parameter and remote sensing observations data, the party
Method is easy to use, but universality is poor, portable weak；Mechanism class method is chosen from physical mechanisms such as crop spectral responses
The mechanism class model such as vegetation radiative transfer model or crop growth model, the morphosis and physiological activity of quantitative description crop,
Such method explicit physical meaning, clear mathematical logic, universality are strong.The most commonly used is vegetation radiation transmissions in mechanism class method
Model PROSAIL reverse modes, and realize the classics the most easy to use of vegetation radiative transfer model PROSAIL reverse modes
Numerical algorithm is to look for table algorithm, and lookup table algorithm receives the concern of domestic and international researcher from proposition so far.
Principle using lookup table algorithm remote sensing quantitative inversion crop physical and chemical parameter is：First, build and larger make physics
Change Parameter lookup step；Then, the forward mode based on PROSAIL models, quantitative simulation obtain Vegetation canopy reflectivity look-up table,
Wherein, canopy reflectance spectrum data are corresponded with crop physical and chemical parameter；Finally, for real Vegetation canopy reflectivity data,
Search the most similar simulation Vegetation canopy reflectivity data, and using its corresponding crop physical and chemical parameter as quantitative inversion therewith
As a result, the result is also referred to as the optimal solution of crop physical and chemical parameter quantitative inversion.The final goal of lookup table algorithm is to find work
The optimal solution of physico parameter quantitative inverting, key point and difficult point are the lookup precision of optimal solution and lookup speed, wherein,
Search precision is influenced by the similitude quantitative assessing index of Vegetation canopy reflectivity data, is searched speed and is searched by the data of optimal solution
Suo Fangfa influences.In existing lookup table algorithm, most common data similarity quantitative assessing index is root-mean-square error.It should
The true difference in size between simulation Vegetation canopy reflectivity data of index quantification description, but ignore Vegetation canopy reflectivity number
Direction difference between, therefore it is not very high to search precision；Most common data search method is full traversal search method.Entirely
Traversal search method can find globally optimal solution, but the computation complexity of this method is higher, and required time is longer, so can not
Meet the needs of crop physical and chemical parameter timely fast quantification inverting, larger limitation is subject in practical application.
To sum up, how a kind of remote sensing quantitative inversion for the crop physical and chemical parameter that inverting numerical precision is high and complexity is low is provided
Method and system are the technical issues of those skilled in the art are urgently to be resolved hurrily.
The content of the invention
In view of this, the present invention provides a kind of the remote sensing quantitative inversion method and system of crop physical and chemical parameter, to realize
Computation complexity is reduced while improving inverting numerical precision, so as to shorten search time, improves inverting efficiency.
A kind of remote sensing quantitative inversion method of crop physical and chemical parameter, including：
It will simulation Vegetation canopy reflectivity look-up table LUTsN is divided into according to spectral bandbA Vegetation canopy reflectivity is searched
Table LUTsi, wherein, NbRepresent the LUTsThe spectral band number of middle Vegetation canopy reflectivity data, the LUTsAccording to
The forward mode of PROSAIL models simulate with crop physical and chemical parameter look-up table LUTpCorresponding simulation Vegetation canopy reflection
Rate look-up table；
According to each LUTsiIn the size of the corresponding spectral band reflectivity values of Vegetation canopy reflectivity that includes, press
According to predetermined order requirement to each LUTsiIt is ranked up, the Vegetation canopy reflectivity look-up table LUT after being sortedsi_sort；
For LUT each describedsi_sort, using binary search method, find out NLiIt is a anti-with true Vegetation canopy
Penetrate rate data Rm(i) similarity meets the simulation Vegetation canopy reflectivity data of preset requirement, wherein, the NLiAccording to utilization
Simulation Vegetation canopy the reflectivity data sum TNL and weight W that binary search method is found outiIt is calculated, 0≤Wi≤ 1,1
≤i≤Nb；
By each simulation Vegetation canopy reflectivity data and the simulation Vegetation canopy reflectivity data in the LUTs
The content of middle corresponding entry builds form in the form of correspondence, obtains dimensionality reduction simulation Vegetation canopy reflectivity look-up table
LUTs_new；
Calculate the LUTs_newMiddle each simulation Vegetation canopy reflectivity data and with the simulation Vegetation canopy reflectivity
The reflectivity comprehensive differences index TD of the corresponding true Vegetation canopy reflectivity data of data；
Find out minimum value from all TD being calculated, and by the corresponding crop physical and chemical parameter of the minimum value
Optimal solution of the data as lookup table algorithm.
Preferably, the NLiCalculating process include formula (1) and formula (2)：
NLi=round (Wi×TNL)(1)；
In formula, the WiRepresent weight, 0≤Wi≤ 1,1≤i≤Nb, TNL expression looked into using binary search method
The simulation Vegetation canopy reflectivity data sum found out, and the TNL ＜ N, the N represent the LUTsIn entry number,
Round represents bracket function；
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rm(k) R is representedmIn the reflection of k-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, σs(k) R is representeds(k) standard deviation, 1≤i≤Nb, 1≤k≤Nb。
Preferably, the calculating process of the TD includes formula (3), formula (4) and formula (5)：
TD=α × NRMSE+ (1- α) × DCC (3)；
In formula, α represents weight, and 0≤α≤1, NRMSE represent normalization root-mean-square error；DCC represents direction change coefficient；
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rs(i) R is representedsIn the reflection of i-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, 1≤i≤Nb；
In formula, RCm=(Rm(2)-Rm(1), Rm(3)-Rm(2) ... ..., Rm(Nb)-Rm(Nb- 1)), RCs=(Rs(2)-Rs
(1), Rs(3)-Rs(2) ... ..., Rs(Nb)-Rs(Nb- 1)), 1≤i≤Nb, sum represent summing function, abs expression take absolute value
Function, sgn represent sign function.
A kind of remote sensing quantitative inversion system of crop physical and chemical parameter, including：
Cutting unit, for Vegetation canopy reflectivity look-up table LUT will to be simulatedsN is divided into according to spectral bandbA vegetation
Canopy reflectance spectrum look-up table LUTsi, wherein, NbRepresent the LUTsThe spectral band number of middle Vegetation canopy reflectivity data, it is described
LUTsTo be simulated according to the forward mode of PROSAIL models and crop physical and chemical parameter look-up table LUTpCorresponding simulation is planted
By canopy reflectance spectrum look-up table；
Sequencing unit, for according to each LUTsiIn the corresponding spectral band reflectivity of Vegetation canopy reflectivity that includes
The size of numerical value, according to predetermined order requirement to each LUTsiIt is ranked up, the Vegetation canopy reflectivity after being sorted is looked into
Look for table LUTsi_sort；
First searching unit, for being directed to each described LUTsi_sort, using binary search method, find out NLiIt is a
With true Vegetation canopy reflectivity data Rm(i) similarity meets the simulation Vegetation canopy reflectivity data of preset requirement,
In, the NLiAccording to simulation Vegetation canopy the reflectivity data sum TNL and weight W found out using binary search methodi
It is calculated, 0≤Wi≤ 1,1≤i≤Nb；
Form construction unit, for each simulation Vegetation canopy reflectivity data and the simulation Vegetation canopy to be reflected
Rate data are in the LUTsThe content of middle corresponding entry builds form in the form of correspondence, obtains dimensionality reduction simulation Vegetation canopy
Reflectivity look-up table LUTs_new；
Computing unit, for calculating the LUTs_newMiddle each is simulated Vegetation canopy reflectivity data and is planted with the simulation
By the reflectivity comprehensive differences index TD of the corresponding true Vegetation canopy reflectivity data of canopy reflectance spectrum data；
Second searching unit, for finding out minimum value from all TD being calculated, and by the minimum value
Optimal solution of the corresponding crop physical and chemical parameter data as lookup table algorithm.
Preferably, the NLiCalculating process include formula (1) and formula (2)：
NLi=round (Wi×TNL)(1)；
In formula, the WiRepresent weight, 0≤Wi≤ 1,1≤i≤Nb, TNL expression looked into using binary search method
The simulation Vegetation canopy reflectivity data sum found out, and the TNL ＜ N, the N represent the LUTsIn entry number,
Round represents bracket function；
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rm(k) R is representedmIn the reflection of k-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, σs(k) R is representeds(k) standard deviation, 1≤i≤Nb, 1≤k≤Nb。
Preferably, the calculating process of the TD includes formula (3), formula (4) and formula (5)：
TD=α × NRMSE+ (1- α) × DCC (3)；
In formula, α represents weight, and 0≤α≤1, NRMSE represent normalization root-mean-square error；DCC represents direction change coefficient；
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rs(i) R is representedsIn the reflection of i-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, 1≤i≤Nb；
In formula, RCm=(Rm(2)-Rm(1), Rm(3)-Rm(2) ... ..., Rm(Nb)-Rm(Nb- 1)), RCs=(Rs(2)-Rs
(1), Rs(3)-Rs(2) ... ..., Rs(Nb)-Rs(Nb- 1)), 1≤i≤Nb, sum represent summing function, abs expression take absolute value
Function, sgn represent sign function.
It can be seen from the above technical scheme that the present invention provides a kind of remote sensing quantitative inversion sides of crop physical and chemical parameter
Method and system will simulate Vegetation canopy reflectivity look-up table LUTsAccording to it includes Vegetation canopy reflectivity data spectrum ripple
Hop count is divided into and the equal number of Vegetation canopy reflectivity look-up table LUT of spectral band numbersi, by each LUTsiAccording to default row
Sequence requirement sequence obtains LUTsi_sort, for each LUTsi_sortUsing binary search method, find out and be preced with true vegetation
Layer reflectivity data similarity meets the simulation Vegetation canopy reflectivity data of preset requirement, by each simulation Vegetation canopy reflectivity
Data and the data are in LUTsThe content of middle corresponding entry builds form in the form of correspondence, obtains dimensionality reduction simulation vegetation hat
Layer reflectivity look-up table LUTs_new, calculate LUTs_newMiddle each simulation Vegetation canopy reflectivity data and with the reflectivity number
According to the reflectivity comprehensive differences index TD of corresponding true Vegetation canopy reflectivity data, and by the corresponding crop of minimum value in TD
Optimal solution of the physical and chemical parameter data as lookup table algorithm.As can be seen that the present invention makes full use of remote sensing, mathematics, computer etc.
Multidisciplinary advantage considers the size and Orientation characteristic of Vegetation canopy reflectivity data, the high-precision true vegetation hat of structure
Layer reflectivity data and simulation Vegetation canopy reflectivity data similitude quantitative assessing index, from data search angle, choosing
With suitable mathematical model optimizing data search strategy, introduce feasible computerized algorithm and complete data search process, improving
Computation complexity is reduced while inverting numerical precision, so as to shorten search time, inverting efficiency is improved, meets work
The remote sensing quantitative inversion high-precision of physico parameter and quick demand.
Description of the drawings
It in order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
The embodiment of invention, for those of ordinary skill in the art, without creative efforts, can also basis
The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of remote sensing quantitative inversion method flow chart of crop physical and chemical parameter disclosed by the embodiments of the present invention；
Fig. 2 is a kind of structural representation of the remote sensing quantitative inversion system of crop physical and chemical parameter disclosed by the embodiments of the present invention
Figure.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without making creative work
Embodiment belongs to the scope of protection of the invention.
It is crop to meet the application demands such as precision agriculture Growing state survey, quality estimation, production forecast, pestforecasting
Field management policy development is supported with implementing etc. to provide data and theoretical method guidance, builds a kind of high-precision, quick and pervasive
The remote sensing quantitative inversion method of the crop physical and chemical parameter of property is necessary.
In crop physical and chemical parameter quantitative inversion field, classical lookup table algorithm is easy to use, universality is strong, but in crop
It still needs to be further improved in terms of the lookup precision of physical and chemical parameter optimal solution and lookup speed, be determined with agreeing with crop physical and chemical parameter remote sensing
Inverting is measured to high-precision and efficient practical application demand.The present invention is dissecting the true Vegetation canopy reflection of existing quantitative description
On the basis of rate data and simulation Vegetation canopy reflectivity data similarity evaluation index, proposition being capable of quantitative description Vegetation canopy
The evaluation index of reflectivity data direction difference, that is, direction change coefficient, and by it with describing Vegetation canopy reflectivity data size
The evaluation index of difference normalizes root-mean-square error and is combined, and structure comprehensive and quantitative describes Vegetation canopy reflectivity data size
With the new types of data similarity evaluation index of direction difference；Optimize data search strategy, be directed in Import computer subject orderly
The optimal solution of crop physical and chemical parameter is quantitatively searched in rapidly and efficiently data search method, that is, binary search of array.The present invention from
Theory analysis and numerical value practical term set out, and disclose the remote sensing quantitative inversion method and system of a kind of crop physical and chemical parameter, with
Computation complexity is reduced while realizing and improve inverting numerical precision, so as to shorten search time, improves inverting efficiency.
It is specific as follows that technical solution includes content：
Referring to Fig. 1, a kind of remote sensing quantitative inversion method flow chart of crop physical and chemical parameter provided in an embodiment of the present invention, bag
Include step：
It step S11, will simulation Vegetation canopy reflectivity look-up table LUTsN is divided into according to spectral bandbA Vegetation canopy is anti-
Penetrate rate look-up table LUTsi；
Wherein, NbRepresent the LUTsThe spectral band number of middle Vegetation canopy reflectivity data, the LUTsAccording to
The forward mode of PROSAIL models simulate with crop physical and chemical parameter look-up table LUTpCorresponding simulation Vegetation canopy reflection
Rate look-up table.
PROSAIL models are vegetation radiative transfer models more classical in the world, be mainly used for simulating soil, blade,
Road radiation transmission process between canopy.PROSAIL models include forward mode and reverse mode, the forward mode of the model：It is logical
Input spectral reflectance data, vegetation blade and canopy physical and chemical parameter data, observation geometrical condition data etc. are crossed, mould can be quantified
Intend Vegetation canopy reflectivity data.The reverse mode of the model：It, can be more accurate by inputting Vegetation canopy reflectivity data
The crops physical and chemical parameters such as ground quantitative inversion leaf area index, chlorophyll content.
It should be noted that LUTpIn the crop physical and chemical parameter entry number that includes and LUTsIn the simulation Vegetation canopy that includes
The entry number of reflectivity is identical, is positive integer N.
Step S12, according to each LUTsiIn the corresponding spectral band reflectivity values of Vegetation canopy reflectivity that include
Size, according to predetermined order requirement to each LUTsiIt is ranked up, the Vegetation canopy reflectivity look-up table after being sorted
LUTsi_sort；
Wherein, predetermined order requirement can be from big to small or from small to large, depending on concrete foundation actual needs.
Step S13, for LUT each describedsi_sort, using binary search method, find out NLiA and true plant
By canopy reflectance spectrum data Rm(i) similarity meets the simulation Vegetation canopy reflectivity data of preset requirement；
Binary search refers to：If it is desired to the root (solution of x) of known function f (x)=0, then：1) section is first found out
[a, b] so that f (a) and f (b) contrary signs.According to intermediate value theorem, which centainly includes equation root；2) ask in the section
PointAnd find out the value of f (m)；If 3) f (m) is identical with f (a) signs, [m, b] is taken as new section, otherwise
It takes [a, m]；2) and 3) 4) repeat until perfect precision.
Wherein, in this step, the NLiAccording to the simulation Vegetation canopy reflectivity found out using binary search method
Data count TNL and weight WiIt is calculated, 0≤Wi≤ 1,1≤i≤Nb。
Specifically, the NLiCalculating process include formula (1) and formula (2)：
NLi=round (Wi×TNL)(1)；
In formula, the WiRepresent weight, 0≤Wi≤ 1,1≤i≤Nb, TNL expression looked into using binary search method
The simulation Vegetation canopy reflectivity data sum found out, and the TNL ＜ N, the N represent the LUTsIn entry number,
Round represents bracket function；
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rm(k) R is representedmIn the reflection of k-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, σs(k) R is representeds(k) standard deviation, 1≤i≤Nb, 1≤k≤Nb。
Step S14, each simulation Vegetation canopy reflectivity data and the simulation Vegetation canopy reflectivity data are existed
The LUTsThe content of middle corresponding entry builds form in the form of correspondence, obtains dimensionality reduction simulation Vegetation canopy reflectivity and looks into
Look for table LUTs_new；
Step S15, the LUT is calculateds_newMiddle each is simulated Vegetation canopy reflectivity data and is preced with the simulation vegetation
The reflectivity comprehensive differences index TD of the layer true Vegetation canopy reflectivity data of reflectivity data；
Step S16, minimum value is found out from all TD being calculated, and by the corresponding crop of the minimum value
Optimal solution of the physical and chemical parameter data as lookup table algorithm.
It in summary it can be seen, the present invention will simulation Vegetation canopy reflectivity look-up table LUTsAccording to it includes Vegetation canopy
The spectral band number of reflectivity data is divided into and the equal number of Vegetation canopy reflectivity look-up table LUT of spectral band numbersi,
By each LUTsiLUT is obtained according to predetermined order requirement sequencesi_sort, for each LUTsi_sortUtilize binary search side
Method finds out the simulation Vegetation canopy reflectivity data for meeting preset requirement with true Vegetation canopy reflectivity data similarity,
By each simulation Vegetation canopy reflectivity data and the data in LUTsThe content of middle corresponding entry is built in the form of correspondence
Form obtains dimensionality reduction simulation Vegetation canopy reflectivity look-up table LUTs_new, calculate LUTs_newMiddle each simulation Vegetation canopy is anti-
The reflectivity comprehensive differences index TD of rate data and true Vegetation canopy reflectivity data corresponding with the reflectivity data is penetrated, and
Using the corresponding crop physical and chemical parameter data of minimum value in TD as the optimal solution of lookup table algorithm.As can be seen that the present invention is fully
Using the multidisciplinary advantage such as remote sensing, mathematics, computer, the size and Orientation characteristic of Vegetation canopy reflectivity data is considered,
High-precision true Vegetation canopy reflectivity data and simulation Vegetation canopy reflectivity data similitude quantitative assessing index are built,
From data search angle, suitable mathematical model optimizing data search strategy is selected, it is complete to introduce feasible computerized algorithm
Into data search process, computation complexity is reduced while inverting numerical precision is improved, so as to shorten search time, is carried
High inverting efficiency, meets the remote sensing quantitative inversion high-precision of crop physical and chemical parameter and quick demand.
For the remote sensing quantitative inversion method of crop physical and chemical parameter, the present invention also provides a specific embodiment, specifically
It is as follows：
By taking target in hyperspectral remotely sensed image EnMAP as an example, carry out crop physical and chemical parameter, that is, leaf area index and chlorophyll content
Quantitative inversion numerical experiment.
Wherein, the size of EnMAP remote sensing images arranges × 244 spectral bands for 200 rows × 200；Based on PROSAIL model structures
The simulation Vegetation canopy reflectivity look-up table LUT builtsIn the entry number of simulation Vegetation canopy reflectivity that includes for 100000, instead
The value for penetrating the α in rate comprehensive differences index TD is 0.5, dimensionality reduction simulation Vegetation canopy reflectivity look-up table LUTs_newIn include
The entry number TNL for simulating Vegetation canopy reflectivity is 1000；For carrying out the computer running environment of numerical experiment as Windows
7 64 systems, Intel Core I5 CPU, 8G memories.
Experimental result is：
Prior art is based on using root-mean-square error as similitude quantitative assessing index and using full traversal search to be several
Numerical experiment results according to the classical lookup table algorithm of searching method are：It is the time required to crop physical and chemical parameter quantitative inversion
63350.2 seconds, the root-mean-square error of leaf area index and Quantitative inversion of chlorophyll content was respectively 1.2 and 18.8g/cm2。
The numerical experiment results of technical scheme are：It is 466.7 seconds the time required to crop physical and chemical parameter quantitative inversion,
Leaf area index and the root-mean-square error of Quantitative inversion of chlorophyll content are respectively 1.1 and 16.5 μ g/cm2。
From experimental result as can be seen that the remote sensing quantitative inversion method of crop physical and chemical parameter provided by the invention improves work
The numerical precision of physico parameter quantitative inverting and inverting efficiency.
Difference in size quantitative assessing index NRMSE and direction difference quantitative assessment based on Vegetation canopy reflectivity data refer to
DCC is marked, the present invention constructs new types of data similitude quantitative assessing index i.e. reflectivity comprehensive differences index TD.Reflectivity integrates
The calculating process of difference index TD is referring to formula (3)：
TD=α × NRMSE+ (1- α) × DCC (3)；
In formula, α represents weight, with the research specific vegetation pattern in area and kind, landform and landforms, observation geometrical condition
Etc. related, 0≤α≤1, NRMSE represents normalization root-mean-square error；DCC represents direction change coefficient.
On the basis of reflectivity comprehensive differences index TD, to ask for the optimal solution of crop physical and chemical parameter, Develop Data is needed
Search.In lookup table algorithm, most common data search method is full traversal search, and the computation complexity of this method is O (N
× log (N)), N represents the entry number in look-up table, and higher computation complexity and larger memory space occupy so that the party
Method need to pay higher time cost when computer is realized.For meet the high-precision of crop physical and chemical parameter remote sensing quantitative inversion and
High efficiency practical application demand, the present invention construct the new types of data searching method based on binary search.In Computer Subject
In, binary search is the rapidly and efficiently data search method for subordinate ordered array, and the computation complexity of this method is O (log
(N)), for compared to full traversal search method, there is more significant reduction in terms of computation complexity and time complexity.
In the prior art, root-mean-square error RMSE is that the evaluation of quantitative description Vegetation canopy reflectivity data difference in size refers to
Mark, which characterizes Vegetation canopy reflectivity data each spectral band antipode, but due to not considering in calculating process
The data value range of each spectral band reflectivity, therefore the result of calculation of RMSE may be caused to bias toward data value range larger
Spectral band.Consider contribution of each spectral band to Vegetation canopy reflectivity data difference in size quantitative assessment for equilibrium, this
Invention has chosen quantitative assessing index of the normalization root-mean-square error NRMSE as Vegetation canopy reflectivity data difference in size,
Normalize the calculation formula such as formula (4) of root-mean-square error NRMSE：
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rs(i) R is representedsIn the reflection of i-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, 1≤i≤Nb。
In addition, Vegetation canopy reflectivity data has size and Orientation characteristic simultaneously, so quantitative assessment is true and mould
When intending Vegetation canopy reflectivity data difference, size of data difference and direction difference should be considered, therefore the present invention proposes
It is capable of evaluation index, that is, direction change coefficient DCC of quantitative description Vegetation canopy reflectivity data direction difference, direction change system
The calculation formula such as formula (5) of number DCC：
In formula, RCm=(Rm(2)-Rm(1), Rm(3)-Rm(2) ... ..., Rm(Nb)-Rm(Nb- 1)), RCs=(Rs(2)-Rs
(1), Rs(3)-Rs(2) ... ..., Rs(Nb)-Rs(Nb- 1)), 1≤i≤Nb, sum represent summing function, abs expression take absolute value
Function, sgn represent sign function.
Corresponding with above method embodiment, the present invention also provides a kind of remote sensing quantitative inversion systems of crop physical and chemical parameter
System.
Referring to Fig. 2, a kind of structure of the remote sensing quantitative inversion system of crop physical and chemical parameter provided in an embodiment of the present invention is shown
It is intended to, including：
Cutting unit 21, for Vegetation canopy reflectivity look-up table LUT will to be simulatedsN is divided into according to spectral bandbA plant
By canopy reflectance spectrum look-up table LUTsi, wherein, NbRepresent the LUTsThe spectral band number of middle Vegetation canopy reflectivity data, institute
State LUTsTo be simulated according to the forward mode of PROSAIL models and crop physical and chemical parameter look-up table LUTpCorresponding simulation
Vegetation canopy reflectivity look-up table；
Sequencing unit 22, for according to each LUTsiIn the corresponding spectral band reflection of the Vegetation canopy reflectivity that includes
The size of rate score, according to predetermined order requirement to each LUTsiIt is ranked up, the Vegetation canopy reflectivity after being sorted
Look-up table LUTsi_sort；
First searching unit 23, for being directed to each described LUTsi_sort, using binary search method, find out NLi
A and true Vegetation canopy reflectivity data Rm(i) similarity meets the simulation Vegetation canopy reflectivity data of preset requirement；
Wherein, the NLiAccording to the simulation Vegetation canopy reflectivity data sum found out using binary search method
TNL and weight WiIt is calculated, 0≤Wi≤ 1,1≤i≤Nb。
Specifically, the NLiCalculating process include formula (1) and formula (2)：
NLi=round (Wi×TNL)(1)；
In formula, the WiRepresent weight, 0≤Wi≤ 1,1≤i≤Nb, TNL expression looked into using binary search method
The simulation Vegetation canopy reflectivity data sum found out, and the TNL ＜ N, the N represent the LUTsIn entry number,
Round represents bracket function；
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rm(k) R is representedmIn the reflection of k-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, σs(k) R is representeds(k) standard deviation, 1≤i≤Nb, 1≤k≤Nb。
Form construction unit 24, for each simulation Vegetation canopy reflectivity data and the simulation Vegetation canopy is anti-
Rate data are penetrated in the LUTsThe content of middle corresponding entry builds form in the form of correspondence, obtains dimensionality reduction simulation vegetation hat
Layer reflectivity look-up table LUTs_new；
Computing unit 25, for calculating the LUTs_newMiddle each simulation Vegetation canopy reflectivity data and with the simulation
The reflectivity comprehensive differences index TD of the corresponding true Vegetation canopy reflectivity data of Vegetation canopy reflectivity data；
Second searching unit 26, for finding out minimum value from all TD being calculated, and by the minimum
It is worth optimal solution of the corresponding crop physical and chemical parameter data as lookup table algorithm.
It in summary it can be seen, the present invention will simulation Vegetation canopy reflectivity look-up table LUTsAccording to it includes Vegetation canopy
The spectral band number of reflectivity data is divided into and the equal number of Vegetation canopy reflectivity look-up table LUT of spectral band numbersi,
By each LUTsiLUT is obtained according to predetermined order requirement sequencesi_sort, for each LUTsi_sortUtilize binary search side
Method finds out the simulation Vegetation canopy reflectivity data for meeting preset requirement with true Vegetation canopy reflectivity data similarity,
By each simulation Vegetation canopy reflectivity data and the data in LUTsThe content of middle corresponding entry is built in the form of correspondence
Form obtains dimensionality reduction simulation Vegetation canopy reflectivity look-up table LUTs_new, calculate LUTs_newMiddle each simulation Vegetation canopy is anti-
The reflectivity comprehensive differences index TD of rate data and true Vegetation canopy reflectivity data corresponding with the reflectivity data is penetrated, and
Using the corresponding crop physical and chemical parameter data of minimum value in TD as the optimal solution of lookup table algorithm.As can be seen that the present invention is fully
Using the multidisciplinary advantage such as remote sensing, mathematics, computer, the size and Orientation characteristic of Vegetation canopy reflectivity data is considered,
High-precision true Vegetation canopy reflectivity data and simulation Vegetation canopy reflectivity data similitude quantitative assessing index are built,
From data search angle, suitable mathematical model optimizing data search strategy is selected, it is complete to introduce feasible computerized algorithm
Into data search process, computation complexity is reduced while inverting numerical precision is improved, so as to shorten search time, is carried
High inverting efficiency, meets the remote sensing quantitative inversion high-precision of crop physical and chemical parameter and quick demand.
Difference in size quantitative assessing index NRMSE and direction difference quantitative assessment based on Vegetation canopy reflectivity data refer to
DCC is marked, the present invention constructs new types of data similitude quantitative assessing index i.e. reflectivity comprehensive differences index TD.Reflectivity integrates
The calculating process of difference index TD is referring to formula (3)：
TD=α × NRMSE+ (1- α) × DCC (3)；
In formula, α represents weight, with the research specific vegetation pattern in area and kind, landform and landforms, observation geometrical condition
Etc. related, 0≤α≤1, NRMSE represents normalization root-mean-square error；DCC represents direction change coefficient.
On the basis of reflectivity comprehensive differences index TD, to ask for the optimal solution of crop physical and chemical parameter, Develop Data is needed
Search.In lookup table algorithm, most common data search method is full traversal search, and the computation complexity of this method is O (N
× log (N)), N represents the entry number in look-up table, and higher computation complexity and larger memory space occupy so that the party
Method need to pay higher time cost when computer is realized.For meet the high-precision of crop physical and chemical parameter remote sensing quantitative inversion and
High efficiency practical application demand, the present invention construct the new types of data searching method based on binary search.In Computer Subject
In, binary search is the rapidly and efficiently data search method for subordinate ordered array, and the computation complexity of this method is O (log
(N)), for compared to full traversal search method, there is more significant reduction in terms of computation complexity and time complexity.
In the prior art, root-mean-square error RMSE is that the evaluation of quantitative description Vegetation canopy reflectivity data difference in size refers to
Mark, which characterizes Vegetation canopy reflectivity data each spectral band antipode, but due to not considering in calculating process
The data value range of each spectral band reflectivity, therefore the result of calculation of RMSE may be caused to bias toward data value range larger
Spectral band.Consider contribution of each spectral band to Vegetation canopy reflectivity data difference in size quantitative assessment for equilibrium, this
Invention has chosen quantitative assessing index of the normalization root-mean-square error NRMSE as Vegetation canopy reflectivity data difference in size,
Normalize the calculation formula such as formula (4) of root-mean-square error NRMSE：
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy
Reflectivity data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rs(i) R is representedsIn the reflection of i-th of spectral band
Rate, σs(i) R is representeds(i) standard deviation, 1≤i≤Nb。
In addition, Vegetation canopy reflectivity data has size and Orientation characteristic simultaneously, so quantitative assessment is true and mould
When intending Vegetation canopy reflectivity data difference, size of data difference and direction difference should be considered, therefore the present invention proposes
It is capable of evaluation index, that is, direction change coefficient DCC of quantitative description Vegetation canopy reflectivity data direction difference, direction change system
The calculation formula such as formula (5) of number DCC：
In formula, RCm=(Rm(2)-Rm(1), Rm(3)-Rm(2) ... ..., Rm(Nb)-Rm(Nb- 1)), RCs=(Rs(2)-Rs
(1), Rs(3)-Rs(2) ... ..., Rs(Nb)-Rs(Nb- 1)), 1≤i≤Nb, sum represent summing function, abs expression take absolute value
Function, sgn represent sign function.
It should be noted that the operation principle of each component refers to embodiment of the method and corresponds to portion in system embodiment
Point, details are not described herein again.
Finally, it is to be noted that, herein, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, term " comprising ", "comprising" or its any other variant meaning
Covering non-exclusive inclusion, so that process, method, article or equipment including a series of elements not only include that
A little elements, but also including other elements that are not explicitly listed or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except also there are other identical elements in the process, method, article or apparatus that includes the element.
Each embodiment is described by the way of progressive in this specification, the highlights of each of the examples are with other
The difference of embodiment, just to refer each other for identical similar portion between each embodiment.
The foregoing description of the disclosed embodiments enables professional and technical personnel in the field to realize or use the present invention.
A variety of modifications of these embodiments will be apparent for those skilled in the art, it is as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention
The embodiments shown herein is not intended to be limited to, and is to fit to and the principles and novel features disclosed herein phase one
The most wide scope caused.
Claims (4)
1. a kind of remote sensing quantitative inversion method of crop physical and chemical parameter, which is characterized in that including：
It will simulation Vegetation canopy reflectivity look-up table LUTsN is divided into according to spectral bandbA Vegetation canopy reflectivity look-up table
LUTsi, wherein, NbRepresent the LUTsThe spectral band number of middle Vegetation canopy reflectivity data, the LUTsFor according to PROSAIL
The forward mode of model simulate with crop physical and chemical parameter look-up table LUTpCorresponding simulation Vegetation canopy reflectivity is searched
Table；
According to each LUTsiIn the size of the corresponding spectral band reflectivity values of Vegetation canopy reflectivity that includes, according to pre-
If ordering requirements are to each LUTsiIt is ranked up, the Vegetation canopy reflectivity look-up table LUT after being sortedsi_sort；
For LUT each describedsi_sort, using binary search method, find out NLiA and true Vegetation canopy reflectivity
Data Rm(i) similarity meets the simulation Vegetation canopy reflectivity data of preset requirement, wherein, the NLiAccording to two points of utilization
Simulation Vegetation canopy the reflectivity data sum TNL and weight W that method lookup method is found outiIt is calculated, 0≤Wi≤ 1,1≤i
≤Nb；
By each simulation Vegetation canopy reflectivity data and the simulation Vegetation canopy reflectivity data in the LUTsMiddle correspondence
The content of entry builds form in the form of correspondence, obtains dimensionality reduction simulation Vegetation canopy reflectivity look-up table LUTs_new；
Calculate the LUTs_newMiddle each simulation Vegetation canopy reflectivity data and with the simulation Vegetation canopy reflectivity data
The reflectivity comprehensive differences index TD of corresponding true Vegetation canopy reflectivity data；
Find out minimum value from all TD being calculated, and by the corresponding crop physical and chemical parameter data of the minimum value
Optimal solution as lookup table algorithm；
Wherein, the calculating process of the TD includes formula (3), formula (4) and formula (5)：
TD=α × NRMSE+ (1- α) × DCC (3)；
In formula, α represents weight, and 0≤α≤1, NRMSE represent normalization root-mean-square error；DCC represents direction change coefficient；
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In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy reflectivity
Data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rs(i) R is representedsIn the reflectivity of i-th of spectral band, σs
(i) R is representeds(i) standard deviation, 1≤i≤Nb；
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In formula, RCm=(Rm(2)-Rm(1), Rm(3)-Rm(2) ... ..., Rm(Nb)-Rm(Nb- 1)), RCs=(Rs(2)-Rs(1), Rs
(3)-Rs(2) ... ..., Rs(Nb)-Rs(Nb- 1)), 1≤i≤Nb, sum represents summing function, and abs expressions take absolute value function,
Sgn represents sign function.
2. remote sensing quantitative inversion method according to claim 1, which is characterized in that the NLiCalculating process include formula
(1) and formula (2)：
NLi=round (Wi×TNL) (1)；
In formula, the WiRepresent weight, 0≤Wi≤ 1,1≤i≤Nb, TNL expression found out using binary search method
Simulation Vegetation canopy reflectivity data sum, and TNL ＜ N, the N represent the LUTsIn entry number, round tables
Show bracket function；
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In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy reflectivity
Data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rm(k) R is representedmIn the reflectivity of k-th of spectral band, σs
(i) R is representeds(i) standard deviation, σs(k) R is representeds(k) standard deviation, 1≤i≤Nb, 1≤k≤Nb。
3. a kind of remote sensing quantitative inversion system of crop physical and chemical parameter, which is characterized in that including：
Cutting unit, for Vegetation canopy reflectivity look-up table LUT will to be simulatedsN is divided into according to spectral bandbA Vegetation canopy
Reflectivity look-up table LUTsi, wherein, NbRepresent the LUTsThe spectral band number of middle Vegetation canopy reflectivity data, the LUTs
To be simulated according to the forward mode of PROSAIL models and crop physical and chemical parameter look-up table LUTpCorresponding simulation vegetation hat
Layer reflectivity look-up table；
Sequencing unit, for according to each LUTsiIn the corresponding spectral band reflectivity values of Vegetation canopy reflectivity that include
Size, according to predetermined order requirement to each LUTsiIt is ranked up, the Vegetation canopy reflectivity look-up table after being sorted
LUTsi_sort；
First searching unit, for being directed to each described LUTsi_sort, using binary search method, find out NLiIt is a with it is true
Real Vegetation canopy reflectivity data Rm(i) similarity meets the simulation Vegetation canopy reflectivity data of preset requirement, wherein, institute
State NLiAccording to simulation Vegetation canopy the reflectivity data sum TNL and weight W found out using binary search methodiIt calculates
It arrives, 0≤Wi≤ 1,1≤i≤Nb；
Form construction unit, for by it is each it is described simulation Vegetation canopy reflectivity data and it is described simulation Vegetation canopy reflectivity number
According in the LUTsThe content of middle corresponding entry builds form in the form of correspondence, obtains dimensionality reduction simulation Vegetation canopy reflection
Rate look-up table LUTs_new；
Computing unit, for calculating the LUTs_newMiddle each is simulated Vegetation canopy reflectivity data and is preced with the simulation vegetation
The reflectivity comprehensive differences index TD of the corresponding true Vegetation canopy reflectivity data of layer reflectivity data；
Second searching unit for finding out minimum value from all TD being calculated, and the minimum value is corresponded to
Optimal solution of the crop physical and chemical parameter data as lookup table algorithm；
Wherein, the calculating process of the TD includes formula (3), formula (4) and formula (5)：
TD=α × NRMSE+ (1- α) × DCC (3)；
In formula, α represents weight, and 0≤α≤1, NRMSE represent normalization root-mean-square error；DCC represents direction change coefficient；
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In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy reflectivity
Data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rs(i) R is representedsIn the reflectivity of i-th of spectral band, σs
(i) R is representeds(i) standard deviation, 1≤i≤Nb；
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In formula, RCm=(Rm(2)-Rm(1), Rm(3)-Rm(2) ... ..., Rm(Nb)-Rm(Nb- 1)), RCs=(Rs(2)-Rs(1), Rs
(3)-Rs(2) ... ..., Rs(Nb)-Rs(Nb- 1)), 1≤i≤Nb, sum represents summing function, and abs expressions take absolute value function,
Sgn represents sign function.
4. remote sensing quantitative inversion system according to claim 3, which is characterized in that the NLiCalculating process include formula
(1) and formula (2)：
NLi=round (Wi×TNL) (1)；
In formula, the WiRepresent weight, 0≤Wi≤ 1,1≤i≤Nb, TNL expression found out using binary search method
Simulation Vegetation canopy reflectivity data sum, and TNL ＜ N, the N represent the LUTsIn entry number, round tables
Show bracket function；
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<msub>
<mi>W</mi>
<mi>i</mi>
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<mn>2</mn>
<mo>)</mo>
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</mrow>
In formula, RmRepresent true Vegetation canopy reflectivity data, σsRepresent RsStandard deviation, RsRepresent simulation Vegetation canopy reflectivity
Data, Rm(i) R is representedmIn the reflectivity of i-th of spectral band, Rm(k) R is representedmIn the reflectivity of k-th of spectral band, σs
(i) R is representeds(i) standard deviation, σs(k) R is representeds(k) standard deviation, 1≤i≤Nb, 1≤k≤Nb。
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| CN112924401B (en) * | 2019-12-06 | 2022-09-16 | 中国科学院光电研究院 | Semi-empirical inversion method for chlorophyll content of vegetation canopy |
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