CN111487200A - Vegetation index product calculation method and device - Google Patents
Vegetation index product calculation method and device Download PDFInfo
- Publication number
- CN111487200A CN111487200A CN202010341448.5A CN202010341448A CN111487200A CN 111487200 A CN111487200 A CN 111487200A CN 202010341448 A CN202010341448 A CN 202010341448A CN 111487200 A CN111487200 A CN 111487200A
- Authority
- CN
- China
- Prior art keywords
- target pixel
- observation
- reflectivity
- remote sensing
- angle
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004364 calculation method Methods 0.000 title claims abstract description 43
- 238000002310 reflectometry Methods 0.000 claims abstract description 134
- 238000000034 method Methods 0.000 claims abstract description 34
- 238000012545 processing Methods 0.000 claims description 31
- 238000004891 communication Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 4
- 150000001875 compounds Chemical class 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000001579 optical reflectometry Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/55—Specular reflectivity
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N2021/1793—Remote sensing
- G01N2021/1797—Remote sensing in landscape, e.g. crops
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Biochemistry (AREA)
- Mathematical Physics (AREA)
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Analytical Chemistry (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Algebra (AREA)
- Investigating Or Analysing Materials By Optical Means (AREA)
Abstract
The application provides a vegetation index product calculation method and a vegetation index product calculation device, wherein the vegetation index product calculation method comprises the following steps: acquiring a plurality of remote sensing observation images shot by a satellite at different moments; respectively taking each type of pixel in the remote sensing observation image as a target pixel to execute the following processes: calculating the value of the angle parameter of the target pixel as the near-infrared observation reflectivity under the corresponding preset value according to the near-infrared observation reflectivity of the target pixel in the multiple remote sensing observation images; calculating the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is a corresponding preset value according to the red light observation reflectivity of the target pixel in the multiple remote sensing observation images; and calculating the vegetation index of the target pixel under the condition that the value of the angle parameter is the corresponding preset value according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value, and taking the vegetation index as the vegetation index of the target pixel. The method and the device can improve the accuracy and the space-time consistency of vegetation index products.
Description
Technical Field
The application relates to the field of remote sensing image processing, in particular to a vegetation index product calculation method and device.
Background
In practice, the vegetation index product can be applied to vegetation growth monitoring, land surface classification, drought monitoring, agricultural management and the like, and has important application value.
Currently, methods for calculating vegetation index products include: firstly, calculating to obtain vegetation indexes of pixels under the observation reflectivity at each shooting moment in a preset time range by using the observation reflectivity of a remote sensing observation image shot in the preset time range; then, using a certain statistical criterion (such as a maximum value) to process the vegetation index of the pixel under the observation reflectivity at each shooting moment to obtain the final synthetic vegetation index of the pixel, and further obtain a vegetation index product.
However, the accuracy and the space-time consistency of the vegetation index product obtained by calculation are low.
Disclosure of Invention
The application provides a vegetation index product calculation method and a vegetation index product calculation device, and aims to solve the problem that the accuracy and the space-time consistency of a vegetation index product obtained through calculation are low.
In order to achieve the above object, the present application provides the following technical solutions:
the application provides a vegetation index product calculation method, which comprises the following steps:
acquiring a plurality of remote sensing observation images shot by a satellite at different moments; the values of the angle parameters corresponding to the pixels with the same area in the multiple remote sensing observation images are different; the angle parameters include: a solar zenith angle, an observation zenith angle, and a relative azimuth angle between a solar direction and an observation direction;
and respectively taking each type of pixel in the remote sensing observation image as a target pixel to execute the following processing flow:
calculating the value of the target pixel under the corresponding preset value as the near-infrared observation reflectivity of the target pixel according to the near-infrared observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the value of the angle parameter of the target pixel as the red light observation reflectivity under the corresponding preset value according to the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the vegetation index of the target pixel under the corresponding preset value as the vegetation index of the target pixel according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the corresponding preset value as the value of the angle parameter; and calculating the vegetation index of each type of pixel in the plurality of remote sensing observation images, wherein the values of the angle parameters adopted by the calculation of the vegetation index of each type of pixel in the plurality of remote sensing observation images are the corresponding preset values.
Optionally, the calculating, according to the near-infrared observation reflectivity of the target pixel in each remote sensing observation image, the near-infrared observation reflectivity of the target pixel under the condition that the value of the angle parameter is a corresponding preset value includes:
fitting model parameters of a preset nuclear driving model by adopting the near-infrared observation reflectivity of the target pixel in the multiple remote sensing observation images to obtain first model parameters;
and obtaining the near-infrared observed reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the first model parameter and the corresponding preset value of the angle parameter according to a calculation formula of the observed reflectivity of the nuclear driving model.
Optionally, the calculating, according to the red light observation reflectivity of the target pixel in the multiple remote sensing observation images, the red light observation reflectivity of the target pixel under the corresponding preset value as the value of the angle parameter includes:
fitting the model parameters of the nuclear driving model by adopting the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images to obtain second model parameters;
and obtaining the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the second model parameter and the corresponding preset value of the angle parameter according to the calculation formula of the observation reflectivity of the nuclear driving model.
Optionally, in the angle parameter, the preset value of the solar zenith angle is 45 degrees, the preset value of the observation zenith angle is 0 degree, and the preset value of the relative azimuth angle is 0 degree.
The present application also provides a vegetation index product calculation device comprising:
the acquisition module is used for acquiring a plurality of remote sensing observation images shot by the satellite at different moments; the values of the angle parameters corresponding to the pixels with the same area in the multiple remote sensing observation images are different; the angle parameters include: a solar zenith angle, an observation zenith angle, and a relative azimuth angle between a solar direction and an observation direction;
the processing module is used for respectively taking each type of pixel in the remote sensing observation image as a target pixel to execute the following processing flow:
calculating the value of the target pixel under the corresponding preset value as the near-infrared observation reflectivity of the target pixel according to the near-infrared observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the value of the angle parameter of the target pixel as the red light observation reflectivity under the corresponding preset value according to the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the vegetation index of the target pixel under the corresponding preset value as the vegetation index of the target pixel according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the corresponding preset value as the value of the angle parameter; and calculating the vegetation index of each type of pixel in the plurality of remote sensing observation images, wherein the values of the angle parameters adopted by the calculation of the vegetation index of each type of pixel in the plurality of remote sensing observation images are the corresponding preset values.
Optionally, the processing module is configured to calculate, according to the near-infrared observation reflectivity of the target pixel in each remote sensing observation image, the value of the target pixel in the angle parameter as the near-infrared observation reflectivity under a corresponding preset value, and includes:
the processing module is specifically used for fitting model parameters of a preset nuclear driving model by adopting the near-infrared observation reflectivity of the target pixel in the multiple remote sensing observation images to obtain first model parameters; and obtaining the near-infrared observed reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the first model parameter and the corresponding preset value of the angle parameter according to a calculation formula of the observed reflectivity of the nuclear driving model.
Optionally, the processing module is configured to calculate, according to the red light observation reflectivity of the target pixel in the multiple remote sensing observation images, a value of the angle parameter of the target pixel as the red light observation reflectivity under the corresponding preset value, and includes:
the processing module is specifically used for fitting the model parameters of the nuclear driving model by adopting the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images to obtain second model parameters; and obtaining the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the second model parameter and the corresponding preset value of the angle parameter according to the calculation formula of the observation reflectivity of the nuclear driving model.
Optionally, in the angle parameter in the processing module, the preset value of the solar zenith angle is 45 degrees, the preset value of the observation zenith angle is 0 degree, and the preset value of the relative azimuth angle is 0 degree.
The present application also provides a storage medium comprising a stored program, wherein the program performs any of the methods of vegetation index product calculation described above.
The application also provides a device, which comprises at least one processor, at least one memory connected with the processor, and a bus; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory to execute any one of the vegetation index product calculation methods.
According to the calculation method and device for the vegetation index product, a plurality of remote sensing observation images which are obtained by shooting a satellite at different moments are obtained; respectively taking each pixel in the remote sensing observation image as a target pixel, and executing the following processing procedures:
calculating the value of the angle parameter of the target pixel to be the near-infrared observed reflectivity under the corresponding preset value according to the near-infrared observed reflectivity of the target pixel in each remote sensing observed image, wherein the values of the angle parameters respectively corresponding to a plurality of remote sensing observed images shot at different moments are different, so that the value of the angle parameter of the target pixel is the near-infrared observed reflectivity under the corresponding preset value according to the value of the angle parameter of the target pixel; similarly, according to the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is different, the value of the angle parameter of the target pixel is calculated to be the red light observation reflectivity under the corresponding preset value.
On one hand, according to the method, the vegetation index of the target pixel is calculated by taking the value of the angle parameter as the vegetation index of the target pixel according to the near-infrared observation reflectivity and the red-light observation reflectivity of the target pixel under the corresponding preset value of the angle parameter. The vegetation index of the target pixel is selected according to a certain criterion from the vegetation indexes of the target pixel under different values of the angle parameter, so that the problem that the accuracy of the obtained vegetation index of the target pixel is low is solved.
On the other hand, in this embodiment, the values of the angle parameters used for calculating the vegetation index of each type of pixel in the remote sensing observation image are all the corresponding preset values, so that the vegetation indexes of different pixels in the remote sensing observation image are the vegetation indexes whose values of the angle parameters are respectively unified, and the angle parameters include: the vegetation index of different pixels in the remote sensing observation image in the prior art is the vegetation index with the angle parameter of different values, so that the problem of poor space-time consistency among the vegetation indexes of different pixels is solved.
In conclusion, the accuracy and the space-time consistency of the vegetation index product obtained through calculation can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a vegetation index product calculation method disclosed in an embodiment of the present application;
FIG. 2 is a flowchart of a method for calculating a vegetation index of a target pixel disclosed in an embodiment of the present application;
FIG. 3 is a schematic diagram of a vegetation index product calculation apparatus according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of an apparatus disclosed in an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The inventor finds in research that the reasons for poor space-time consistency of vegetation indexes of different picture elements in vegetation index products of the prior art include: the vegetation indexes of different pixels reflect the vegetation indexes under the condition that the values of angle parameters are different values, wherein the angle parameters comprise the solar zenith angle. According to statistics, when satellites are imaged in different seasons, the difference of the solar zenith angles can reach 50 degrees. The sun zenith angles at different latitudes can also be greatly different during imaging. The uncertainty introduced by these angular differences is subject to further correction when generating a global vegetation index product. Namely, the sun angle during satellite imaging is not considered in the prior art, so that the angle consistency of the vegetation index product obtained by calculation is poor, and further, the application value of the vegetation index product can be reduced.
Fig. 1 is a method for calculating vegetation index product according to the present application, comprising the steps of:
s101, acquiring a plurality of remote sensing observation images shot by a satellite at different moments.
In this embodiment, the satellite photographs a preset geographic range, and obtains one remote sensing observation image of the geographic range each time, and in this step, a plurality of remote sensing observation images photographed by the satellite at different times are obtained.
For any remote sensing observation image, the remote sensing observation image comprises multiple types of pixels, wherein corresponding areas of different pixels in the remote sensing observation image are different. In this embodiment, the observed reflectivity of any pixel in one remote sensing observation image is the observed reflectivity under the angle parameter value where the satellite is located when shooting, that is, each pixel in the remote sensing observation image corresponds to one value of the angle parameter, and the values of the angle parameters corresponding to different pixels are different.
The plurality of remote sensing observation images obtained in the step are remote sensing observation images shot at different moments, so that the values of the angle parameters respectively corresponding to a plurality of pixels with the same area in the plurality of remote sensing observation images are different.
In this embodiment, the angle parameters include: the sun zenith angle, the observation zenith angle, and the relative azimuth angle between the sun direction and the observation direction.
S102, taking each type of pixel in the remote sensing observation image as a target pixel, and calculating the vegetation index of the target pixel.
In this embodiment, pixels having the same area in the plurality of remote sensing observation images are referred to as a class of pixels, that is, the pixels having the same area in the plurality of remote sensing observation images are a class of pixels. Therefore, the obtained multiple remote sensing observation images comprise multiple types of pixels. In this step, each type of pixel in the multiple types of pixels is used as a target pixel, and the vegetation index of the target pixel is calculated.
It should be noted that, in this embodiment, a type of pixel in the multiple remote sensing observation images corresponds to one pixel in the final vegetation index image, that is, in this embodiment of the present application, any type of pixel in the multiple remote sensing observation images is used as a target pixel, and the vegetation index corresponding to the type of pixel is calculated.
The process of calculating the vegetation index of the target pixel is shown in fig. 2, and specifically may include the following steps a1 to A3:
and A1, calculating the value of the angle parameter of the target pixel as the near-infrared observation reflectivity under the corresponding preset value according to the near-infrared observation reflectivity of the target pixel in each remote sensing observation image.
In this embodiment, the pixel value of each remote sensing observation image is an observation reflectivity, wherein the observation reflectivity includes an observation reflectivity in a near infrared band and an observation reflectivity in a red light band.
Because the values of the angle parameters corresponding to the target pixels in each remote sensing observation image are different, in the step, the value of the target pixel in the angle parameters is calculated to be the near-infrared observation reflectivity under the corresponding preset value according to the near-infrared observation reflectivity of the target pixel in each remote sensing observation image. The specific calculation method may include the following steps B1 to B2:
and B1, fitting the model parameters of the preset nuclear driving model by adopting the near-infrared observation reflectivity of the target pixel in each remote sensing observation image to obtain first model parameters.
For example, Ross-L i-Maiignan nuclear driving model, wherein the observed reflectivity of Ross-L i-Maiignan nuclear driving model is calculated as shown in equation (1) below:
in the formula (I), the compound is shown in the specification,
representing the calculated observed reflectivity, k0,λ、k1,λAnd k2,λModel parameters representing the kernel-driven model,
representing the volume scattering nuclei corrected for hot spot effects,
representing the geometric optical kernel.
Wherein the content of the first and second substances,
is expressed as shown in the following formula (2),
the expression of (c) is shown in the following formula (3).
In the formula (I), the compound is shown in the specification,
in the step, the near-infrared observation reflectivity of a target pixel in each remote sensing observation image is adopted to fit a model parameter k of a preset nuclear driving model0,λ、k1,λAnd k2,λFor convenience of description, the model parameters obtained by fitting are referred to as first model parameters. The mode of adopting the near-infrared observation reflectivity set nuclear driving model of the target pixel in each remote sensing observation image can be fitted through a least square method, the specific implementation mode is the prior art, and details are not repeated here.
And B2, obtaining the near-infrared observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the first model parameter and the corresponding preset value of the angle parameter according to the calculation formula of the observation reflectivity of the nuclear driving model.
In this step, according to equation (1), due to the model parameter k0,λ、k1,λAnd k2,λAnd calculating to obtain the near-infrared observation reflectivity of the target pixel under the condition that the angle parameter is determined to be the corresponding preset value through the formula (1) under the condition that the angle parameter is determined to be the corresponding preset value.
The meaning that the value of the angle parameter is the corresponding preset value is that the sun zenith angle, the observation zenith angle and the relative azimuth angle respectively take the corresponding preset values. The specific values of the three angles may be set according to actual conditions, and the specific values of the three angles are not limited in this embodiment.
As an example, in this embodiment, the value of the solar zenith angle may be 45 degrees, the value of the observation zenith angle may be 0 degree, and the value of the relative azimuth angle may be 0 degree.
And A2, calculating the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value according to the red light observation reflectivity of the target pixel in each remote sensing observation image.
The specific implementation principle of the step is the same as that of calculating the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value.
Optionally, the specific implementation process may include the following steps C1 to C2:
and C1, fitting the model parameters of the nuclear driving model by adopting the red light observation reflectivity of the target pixel in each remote sensing observation image to obtain second model parameters.
For a specific implementation principle of this step, reference may be made to step B1, which is not described herein again.
The only difference between this step and step B1 is that in this step, the red light of the target pixel is used to observe the reflectivity, the model parameters of the nuclear driving model are fitted, and for the convenience of description, the model parameters obtained by fitting are called as second model parameters.
And C2, obtaining the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the value of the second model parameter and the angle parameter as the corresponding preset value according to the calculation formula of the observation reflectivity of the kernel driving model.
The implementation principle of this step can refer to step B2, and is not described here again.
In this step, the value of the angle parameter may be the same as the value of the angle parameter in step B1.
And A3, calculating the vegetation index of the target pixel under the condition that the value of the angle parameter is the corresponding preset value according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value, and taking the vegetation index as the vegetation index of the target pixel.
In this embodiment, the vegetation index of any pixel in the remote sensing observation image under a specific incidence-observation geometric condition is calculated as shown in the following formula (4).
In the formula (I), the compound is shown in the specification,
the index of the vegetation of the pixel is expressed,
represents the observed reflectivity of the ground object in the near infrared band,
representing the observed reflectivity, theta, of the ground object in the red bandsRepresenting the zenith angle of the sun, thetavRepresenting the angle of the observation zenith,
representing the relative azimuth angle between the sun direction and the observation direction.
In this step, it is assumed that the preset values respectively corresponding to the angle parameters are specifically: the zenith angle of the sun takes on the value thetas0If the preset value corresponding to the observation zenith angle is 0 and the preset value corresponding to the relative azimuth angle is 0, the calculation formula of the vegetation index of the target pixel with the value of the angle parameter corresponding to the preset value is shown in the following formula (5):
since the near-infrared observation reflectivity and the red light reflectivity of the target pixel under the condition that the angle parameter value is the corresponding preset value are respectively calculated and obtained in the step a1 and the step a2, in this step, the formula (5) is adopted, and the value of the angle parameter of the target pixel is calculated to be the vegetation index under the corresponding preset value according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the condition that the angle parameter value is the corresponding preset value. And taking the vegetation index obtained by the calculation in the step as the vegetation index of the target pixel.
In the above-mentioned step a1 to step A3, a process of calculating a vegetation index of any type of pixel in the remote sensing observation image when a value of an angle parameter of the type of pixel is a corresponding preset value is introduced, in this embodiment, a value of the angle parameter is calculated as the vegetation index of the corresponding preset value for each type of target pixel in the remote sensing observation image, wherein values of the angle parameter adopted in the process of calculating the vegetation index are corresponding preset values for different types of pixels in the remote sensing observation image, that is, values of solar zenith angles in the angle parameter of each type of pixel are corresponding preset values, values of observation zenith angles of each type of pixel are corresponding preset values, and values of relative azimuth angles of each type of pixel are corresponding preset values.
Fig. 3 is a device for calculating vegetation index product according to an embodiment of the present application, which may include: an acquisition module 301 and a processing module 302.
The acquisition module 301 is configured to acquire a plurality of remote sensing observation images captured by a satellite at different times; the values of the angle parameters corresponding to the pixels with the same area in the multiple remote sensing observation images are different; the angle parameters include: the sun zenith angle, the observation zenith angle, and the relative azimuth angle between the sun direction and the observation direction.
The processing module 302 is configured to respectively use each type of pixel in the remote sensing observation image as a target pixel to execute the following processing procedures:
calculating the value of the angle parameter of the target pixel as the near-infrared observation reflectivity under the corresponding preset value according to the near-infrared observation reflectivity of the target pixel in the multiple remote sensing observation images;
calculating the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is a corresponding preset value according to the red light observation reflectivity of the target pixel in the multiple remote sensing observation images;
calculating the vegetation index of the target pixel under the condition that the value of the angle parameter is the corresponding preset value according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value, and taking the vegetation index as the vegetation index of the target pixel; and the values of the angle parameters adopted for calculating the vegetation indexes of all types of pixels in the remote sensing observation images are corresponding preset values.
Optionally, the processing module 302 is configured to calculate, according to the near-infrared observation reflectivity of the target pixel in each remote sensing observation image, the near-infrared observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value, and includes:
the processing module 302 is specifically configured to fit model parameters of a preset nuclear driving model by using near-infrared observation reflectivity of target pixels in a plurality of remote sensing observation images to obtain first model parameters; and obtaining the near-infrared observed reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the first model parameter and the corresponding preset value of the angle parameter according to a calculation formula of the observed reflectivity of the nuclear driving model.
Optionally, the processing module 302 is configured to calculate, according to the red light observation reflectivity of the target pixel in the multiple remote sensing observation images, the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value, and includes:
the processing module 302 is specifically configured to fit model parameters of the nuclear driving model by using red light observation reflectivity of target pixels in a plurality of remote sensing observation images to obtain second model parameters; and obtaining the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the second model parameter and the corresponding preset value of the angle parameter according to the calculation formula of the observation reflectivity of the nuclear driving model.
Optionally, in the angle parameters in the processing module 302, the preset value of the solar zenith angle is 45 degrees, the preset value of the observation zenith angle is 0 degree, and the preset value of the relative azimuth angle is 0 degree.
The calculating device of the vegetation index product comprises a processor and a memory, wherein the acquiring module 301, the processing module 302 and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more than one, and the problem that the accuracy and the space-time consistency of the vegetation index product obtained by calculation are low is solved by adjusting the kernel parameters.
An embodiment of the present invention provides a storage medium having a program stored thereon, the program implementing the method of calculating a vegetation index product when executed by a processor.
The embodiment of the invention provides a processor, which is used for running a program, wherein the calculation method of the vegetation index product is executed when the program runs.
An embodiment of the present invention provides an apparatus, as shown in fig. 4, the apparatus includes at least one processor, and at least one memory and a bus connected to the processor; the processor and the memory complete mutual communication through a bus; the processor is used for calling the program instructions in the memory so as to execute the calculating method of the vegetation index product. The device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
acquiring a plurality of remote sensing observation images shot by a satellite at different moments; the values of the angle parameters corresponding to the pixels with the same area in the multiple remote sensing observation images are different; the angle parameters include: a solar zenith angle, an observation zenith angle, and a relative azimuth angle between a solar direction and an observation direction;
and respectively taking each type of pixel in the remote sensing observation image as a target pixel to execute the following processing flow:
calculating the value of the target pixel under the corresponding preset value as the near-infrared observation reflectivity of the target pixel according to the near-infrared observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the value of the angle parameter of the target pixel as the red light observation reflectivity under the corresponding preset value according to the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the vegetation index of the target pixel under the corresponding preset value as the vegetation index of the target pixel according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the corresponding preset value as the value of the angle parameter; and calculating the vegetation index of each type of pixel in the plurality of remote sensing observation images, wherein the values of the angle parameters adopted by the calculation of the vegetation index of each type of pixel in the plurality of remote sensing observation images are the corresponding preset values.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
The functions described in the method of the embodiment of the present application, if implemented in the form of software functional units and sold or used as independent products, may be stored in a storage medium readable by a computing device. Based on such understanding, part of the contribution to the prior art of the embodiments of the present application or part of the technical solution may be embodied in the form of a software product stored in a storage medium and including several instructions for causing a computing device (which may be a personal computer, a server, a mobile computing device or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Features described in the embodiments of the present specification may be replaced with or combined with each other, each embodiment is described with a focus on differences from other embodiments, and the same or similar portions among the embodiments may be referred to each other.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method of calculating a vegetation index product, comprising:
acquiring a plurality of remote sensing observation images shot by a satellite at different moments; the values of the angle parameters corresponding to the pixels with the same area in the multiple remote sensing observation images are different; the angle parameters include: a solar zenith angle, an observation zenith angle, and a relative azimuth angle between a solar direction and an observation direction;
and respectively taking each type of pixel in the remote sensing observation image as a target pixel to execute the following processing flow:
calculating the value of the target pixel under the corresponding preset value as the near-infrared observation reflectivity of the target pixel according to the near-infrared observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the value of the angle parameter of the target pixel as the red light observation reflectivity under the corresponding preset value according to the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the vegetation index of the target pixel under the corresponding preset value as the vegetation index of the target pixel according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the corresponding preset value as the value of the angle parameter; and calculating the vegetation index of each type of pixel in the plurality of remote sensing observation images, wherein the values of the angle parameters adopted by the calculation of the vegetation index of each type of pixel in the plurality of remote sensing observation images are the corresponding preset values.
2. The method according to claim 1, wherein the calculating of the near-infrared observed reflectivity of the target pixel under the condition that the value of the angle parameter is a corresponding preset value according to the near-infrared observed reflectivity of the target pixel in each remote sensing observation image comprises:
fitting model parameters of a preset nuclear driving model by adopting the near-infrared observation reflectivity of the target pixel in the multiple remote sensing observation images to obtain first model parameters;
and obtaining the near-infrared observed reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the first model parameter and the corresponding preset value of the angle parameter according to a calculation formula of the observed reflectivity of the nuclear driving model.
3. The method of claim 1, wherein said calculating the red observed reflectivity of the target pixel at the corresponding predetermined value of the angle parameter based on the red observed reflectivity of the target pixel in the plurality of remote sensing observed images comprises:
fitting the model parameters of the nuclear driving model by adopting the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images to obtain second model parameters;
and obtaining the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the second model parameter and the corresponding preset value of the angle parameter according to the calculation formula of the observation reflectivity of the nuclear driving model.
4. The method according to any one of claims 1 to 3, wherein the preset value of the solar zenith angle in the angle parameter is 45 degrees, the preset value of the observation zenith angle is 0 degrees, and the preset value of the relative azimuth angle is 0 degrees.
5. A vegetation index product calculation device comprising:
the acquisition module is used for acquiring a plurality of remote sensing observation images shot by the satellite at different moments; the values of the angle parameters corresponding to the pixels with the same area in the multiple remote sensing observation images are different; the angle parameters include: a solar zenith angle, an observation zenith angle, and a relative azimuth angle between a solar direction and an observation direction;
the processing module is used for respectively taking each type of pixel in the remote sensing observation image as a target pixel to execute the following processing flow:
calculating the value of the target pixel under the corresponding preset value as the near-infrared observation reflectivity of the target pixel according to the near-infrared observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the value of the angle parameter of the target pixel as the red light observation reflectivity under the corresponding preset value according to the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images;
calculating the vegetation index of the target pixel under the corresponding preset value as the vegetation index of the target pixel according to the near-infrared observation reflectivity and the red light observation reflectivity of the target pixel under the corresponding preset value as the value of the angle parameter; and calculating the vegetation index of each type of pixel in the plurality of remote sensing observation images, wherein the values of the angle parameters adopted by the calculation of the vegetation index of each type of pixel in the plurality of remote sensing observation images are the corresponding preset values.
6. The apparatus according to claim 5, wherein the processing module is configured to calculate, according to the near-infrared observed reflectivity of the target pixel in each of the remote sensing observation images, the near-infrared observed reflectivity of the target pixel at a value of the angle parameter corresponding to a preset value, and includes:
the processing module is specifically used for fitting model parameters of a preset nuclear driving model by adopting the near-infrared observation reflectivity of the target pixel in the multiple remote sensing observation images to obtain first model parameters; and obtaining the near-infrared observed reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the first model parameter and the corresponding preset value of the angle parameter according to a calculation formula of the observed reflectivity of the nuclear driving model.
7. The apparatus of claim 5, wherein the processing module, configured to calculate the red observed reflectivity of the target pixel at the corresponding preset value according to the red observed reflectivity of the target pixel in the plurality of remote sensing observation images, comprises:
the processing module is specifically used for fitting the model parameters of the nuclear driving model by adopting the red light observation reflectivity of the target pixel in the plurality of remote sensing observation images to obtain second model parameters; and obtaining the red light observation reflectivity of the target pixel under the condition that the value of the angle parameter is the corresponding preset value based on the second model parameter and the corresponding preset value of the angle parameter according to the calculation formula of the observation reflectivity of the nuclear driving model.
8. The apparatus according to any one of claims 5 to 7, wherein the preset value of the solar zenith angle in the angle parameters in the processing module is 45 degrees, the preset value of the observation zenith angle is 0 degrees, and the preset value of the relative azimuth angle is 0 degrees.
9. A storage medium comprising a stored program, wherein the program performs the method of calculating a vegetation index product of any one of claims 1 to 4.
10. An apparatus comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is used for calling the program instructions in the memory to execute the calculation method of the vegetation index product as claimed in any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010341448.5A CN111487200A (en) | 2020-04-27 | 2020-04-27 | Vegetation index product calculation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010341448.5A CN111487200A (en) | 2020-04-27 | 2020-04-27 | Vegetation index product calculation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111487200A true CN111487200A (en) | 2020-08-04 |
Family
ID=71795444
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010341448.5A Pending CN111487200A (en) | 2020-04-27 | 2020-04-27 | Vegetation index product calculation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111487200A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103204A (en) * | 2011-01-26 | 2011-06-22 | 环境保护部卫星环境应用中心 | Inversion method for land aerosols optical thickness based on environment satellite 1 |
| CN103018736A (en) * | 2012-12-03 | 2013-04-03 | 北京航空航天大学 | Satellite-borne remote sensor radiation calibration method based on atmospheric parameter remote sensing retrieval |
| CN103278112A (en) * | 2013-05-17 | 2013-09-04 | 中国科学院对地观测与数字地球科学中心 | Leaf area index inversion method and device based on remote sensing images |
| US20180356339A1 (en) * | 2016-08-10 | 2018-12-13 | Fuzhou University | Method of calculating tavi based on a band ratio model and solar altitude angle |
| CN109376600A (en) * | 2018-09-20 | 2019-02-22 | 中国农业大学 | Multi-spectrum remote sensing image comprehensive characteristics cloud detection method of optic and device |
-
2020
- 2020-04-27 CN CN202010341448.5A patent/CN111487200A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102103204A (en) * | 2011-01-26 | 2011-06-22 | 环境保护部卫星环境应用中心 | Inversion method for land aerosols optical thickness based on environment satellite 1 |
| CN103018736A (en) * | 2012-12-03 | 2013-04-03 | 北京航空航天大学 | Satellite-borne remote sensor radiation calibration method based on atmospheric parameter remote sensing retrieval |
| CN103278112A (en) * | 2013-05-17 | 2013-09-04 | 中国科学院对地观测与数字地球科学中心 | Leaf area index inversion method and device based on remote sensing images |
| US20180356339A1 (en) * | 2016-08-10 | 2018-12-13 | Fuzhou University | Method of calculating tavi based on a band ratio model and solar altitude angle |
| CN109376600A (en) * | 2018-09-20 | 2019-02-22 | 中国农业大学 | Multi-spectrum remote sensing image comprehensive characteristics cloud detection method of optic and device |
Non-Patent Citations (2)
| Title |
|---|
| 赵永光等: "一种遥感图像太阳―观测几何归一化方法", 《遥感技术与应用》 * |
| 魏宏伟: "HJ-1A卫星不同CCD数据比对及归一化研究", 《CNKI硕士电子期刊全文数据库》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20220343599A1 (en) | System and methods for improved aerial mapping with aerial vehicles | |
| US9521391B2 (en) | Settings of a digital camera for depth map refinement | |
| US11625812B2 (en) | Recovering occluded image data using machine learning | |
| US10523866B2 (en) | Mobile terminal image synthesis method, mobile terminal image synthesis apparatus and mobile terminal | |
| US10757327B2 (en) | Panoramic sea view monitoring method and device, server and system | |
| Honjo et al. | Sky view factor measurement by using a spherical camera | |
| Giuliani et al. | Using crowdsourced web content for informing water systems operations in snow-dominated catchments | |
| WO2022267693A1 (en) | System and method for super-resolution image processing in remote sensing | |
| CN111487200A (en) | Vegetation index product calculation method and device | |
| CN112419424A (en) | Gun and ball linkage calibration method and device and related equipment | |
| WO2018098736A1 (en) | Remote sensing imaging-based cyanobacteria monitoring method and device | |
| CN112215186A (en) | Marsh wetland vegetation classification method, device, computer equipment and storage medium | |
| Mileva et al. | New tool for spatio-temporal image fusion in remote sensing: A case study approach using Sentinel-2 and Sentinel-3 data | |
| CN112116068A (en) | Annular image splicing method, equipment and medium | |
| CN107527323A (en) | The scaling method and device of lens distortion | |
| CN106680157B (en) | Method for determining large-particle water cloud particle spectrum distribution | |
| US10497100B2 (en) | Image cancellation from video | |
| CN112106346A (en) | Image processing method, device, unmanned aerial vehicle, system and storage medium | |
| CN112154484A (en) | Ortho image generation method, system and storage medium | |
| CN114359425A (en) | Method and device for generating ortho image, and method and device for generating ortho exponential graph | |
| CN112254812A (en) | Method, device and equipment for calculating overlapping region of camera spectral bands and storage medium | |
| CN116823936B (en) | Method and system for acquiring longitude and latitude by using camera screen punctuation | |
| CN111402322A (en) | Correction method and device for medium-resolution leaf area index product | |
| WO2022178715A1 (en) | Image processing method and device | |
| US20240062348A1 (en) | Post-processing panoramic imagery geo-rectification system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200804 |
|
| RJ01 | Rejection of invention patent application after publication |