CN113155740A - Calibration reference field BRDF characteristic analysis method and system - Google Patents

Calibration reference field BRDF characteristic analysis method and system Download PDF

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CN113155740A
CN113155740A CN202010014497.8A CN202010014497A CN113155740A CN 113155740 A CN113155740 A CN 113155740A CN 202010014497 A CN202010014497 A CN 202010014497A CN 113155740 A CN113155740 A CN 113155740A
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brdf
observation
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reflectivity
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CN113155740B (en
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胡秀清
杨磊库
陈林
陶炳成
张璐
张鹏
张督锋
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Beijing Anzhou Technology Co ltd
Henan University of Technology
National Satellite Meteorological Center
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Henan University of Technology
National Satellite Meteorological Center
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    • 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
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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    • G01C21/24—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for cosmonautical navigation
    • 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
    • G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
    • G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
    • G01N21/274—Calibration, base line adjustment, drift correction
    • 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
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Abstract

The embodiment of the invention provides a method and a system for analyzing BRDF characteristics of a calibration reference field, comprising the following steps: the unmanned aerial vehicle acquires multi-angle observation geometric data and spectral data of a target area; performing difference between the observed reference plate spoke brightness, and supplementing the reference plate spoke brightness data during the flight; acquiring multi-angle reflectivity by combining the sun angle of each observation angle according to the multi-angle observation geometric data and the spectrum data; selecting a BRDF model according to the earth surface type of the target area; and analyzing the multi-angle reflectivity based on the BRDF model to obtain the bidirectional reflectivity characteristic of the target area. According to the calibration reference field BRDF characteristic analysis method and system, the unmanned aerial vehicle is used for carrying the spectrometer to carry out multi-angle data observation, the influence of the change of the sun position on measurement is reduced, meanwhile, the non-contact measurement on the BRDF characteristic of an outdoor field is realized, the influence of the instrument and equipment on the surface spectral characteristic is reduced, and the observation precision is improved.

Description

Calibration reference field BRDF characteristic analysis method and system
Technical Field
The invention relates to the technical field of satellite remote sensing, in particular to a calibration reference field BRDF characteristic analysis method and system.
Background
The high-precision radiometric calibration of the remote sensing data is an important prerequisite for the quantitative application of the remote sensing data, wherein the radiometric calibration of the near infrared band imager is generally preferably completed by adopting an on-satellite calibration device. For a satellite remote sensor without a satellite absolute radiometric calibration device, an alternative calibration method needs to be adopted to complete radiometric calibration work in a work cycle.
Currently, the international common method is an on-orbit radiation calibration method based on a satellite remote sensor, and the method evaluates the radiation response difference between the satellite remote sensors by comparing earth observation images and ground test data of different satellites in a preselected stable ground target area, thereby realizing the absolute radiation calibration of the satellite remote sensor. One of the keys for realizing site radiometric calibration is to determine the ground surface Bidirectional Reflectance (BRDF) characteristics of a selected stable target site, which is generally used as a satellite site calibration area, the atmospheric conditions of which are generally stable, and satellite observation signals mainly include information from the ground surface. Due to different conditions such as transit time and observation attitude of different satellites, observation data cannot be directly verified and calibrated due to the influence of the BRDF (ground surface broadcast distribution function). Therefore, the directional reflection characteristics of the satellite calibration site need to be measured, and an earth surface BRDF model is established, so that the objective requirements of multi-satellite and multi-parameter calibration are met.
At present, the BRDF model is established and verified by the following main steps: selecting a stable ground target as a satellite on-orbit calibration site in advance; according to the requirements of atmospheric conditions, satellite remote sensor observation scale, planetary synchronous observation and the like, multi-angle reflectivity observation is carried out on a target area; selecting a proper BRDF model, and fitting model parameters by using observation data; and verifying the BRDF product of the satellite remote sensing target according to the simulation result.
Because the measuring mode mainly depends on the multi-angle measuring frame to realize multi-angle observation of the earth surface, on one hand, the multi-angle measuring system has more components and has higher difficulty in transportation and field assembly; on the other hand, the external motor is used for driving the positions of the track and the observation vehicle so as to change the observation angle, and the accuracy of angle positioning is influenced by the vibration of the motor and the installation error of the track; furthermore, since the observation equipment is directly located on the earth's surface, the spectral characteristics of the earth's surface are destroyed to some extent.
Therefore, it is desirable to provide a multi-angle measurement technique and a corresponding data processing method, which can reduce the damage of the observation equipment to the optical characteristics of the earth surface as much as possible and reduce the workload of the test site on the premise of meeting the basic technical requirements.
Disclosure of Invention
Aiming at various defects in the traditional measuring method, the embodiment of the invention provides a calibration reference field BRDF characteristic analysis method and system, which realize non-contact measurement of BRDF characteristics of an outdoor field by utilizing an unmanned aerial vehicle and matching with a corresponding observation scheme and a data processing method, reduce the influence of instrument and equipment on measurement of surface spectral characteristics and improve the observation precision.
In a first aspect, an embodiment of the present invention provides a method for analyzing BRDF characteristics of a calibration reference field, including:
acquiring multi-angle observation geometric data and corresponding spectral data of a target area by using an unmanned aerial vehicle; performing difference between the observed reference plate spoke brightness, and supplementing the reference plate spoke brightness data during the flight; acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle; selecting a BRDF model according to the earth surface type of the target area; analyzing the multi-angle reflectivity based on a BRDF model to obtain the bidirectional reflectivity characteristic of a target area; and comparing the bidirectional reflectivity characteristic with remote sensing data acquired by the satellite remote sensor to acquire the remote sensing product precision verification of the satellite remote sensor.
Further, the above-mentioned multi-angle observation geometric data and the corresponding spectral data that utilize unmanned vehicles to obtain the target area includes:
acquiring multi-angle observation geometric data by using a holder and navigation equipment carried on the unmanned aerial vehicle, wherein the multi-angle observation geometric data comprises a multi-angle observation altitude angle and an observation azimuth angle; and acquiring spectral measurement data of a target area under multiple angles and spectral standard data of a reference white board by using a spectrometer carried on the unmanned aerial vehicle.
Further, before acquiring the multi-angle reflectivity, the method further comprises:
sorting the multi-angle observation geometric data and the sun angle according to standard angle definition; and supplementing the reference plate radiance data missing during the flight by using the existing observation data.
Further, the selecting the BRDF model according to the surface type of the target region includes: and selecting one of a Ross-Li Kernel Model or a Rahman-Pinty-Verstraete Model according to different surface types of the target area.
Further, the calculation formula of the Ross-Li Kernel Model is as follows:
Figure BDA0002358359850000031
wherein R is a Ross-Li Kernel Model function, kisoIs an isotropic kernel function, kgeoAs geometric optical nuclear functionNumber, kvolAs a function of the volume scatter sum fisoIs an isotropic kernel function coefficient, fgeoIs a coefficient of a geometric optical kernel function, fvolIs the volume scattering and the function coefficient, lambda is the incident light wavelength, thetaiIs the solar altitude angle thetarIn order to observe the altitude angle,
Figure BDA0002358359850000032
is the azimuth angle of the sun
Figure BDA0002358359850000033
And observation azimuth angle
Figure BDA0002358359850000034
Relative azimuth angle therebetween.
Further, the calculation formula of the above-mentioned Rahman-Pinty-Verstraete model is as follows:
Figure BDA0002358359850000035
Figure BDA0002358359850000036
Figure BDA0002358359850000037
Figure BDA0002358359850000038
wherein R is a Rahman-Pinty-Verstraete model function rho0Representing the overall reflectivity observation value, M being a Minnarter diffuse reflection model, k representing the spherical anisotropy, F being a Henyey Greenstein phase function, ξ being the scattering angle between the incident ray and the emergent ray vector, g being an empirical parameter describing the scattering phase function>0 denotes forward scattering dominance, g<0 represents the backscatter dominance; h is used for expressing the hot spot effect, delta is the correction quantity of the model, rhocFor the width of the hot spot, D is the distance between the incoming and outgoing ray vectors.
Further, after analyzing the multi-angle reflectivity based on the BRDF model, the method further comprises the following steps:
inverting the optimal coefficient combination of the fitting observation parameters of the BRDF model by using a least square method; according to the coefficient combination, obtaining the bidirectional reflectivity characteristic of the target area under the conditions of any solar incident angle and observation angle; wherein the minimized error function of the least squares method is:
Figure BDA0002358359850000041
wherein d is the number of observed samples minus the model coefficient; ω represents a weighting factor for the observation, and n represents the corresponding nth observation; e.g. of the type2I.e. the solution of the error function, e2The smaller the size, the more accurate the fitting effect.
In a second aspect, an embodiment of the present invention provides a calibration reference field BRDF characterization system, including: data acquisition module, reflectivity operation module, model selection module and BRDF characteristic operation module, wherein:
the data acquisition module is used for acquiring multi-angle observation geometric data and corresponding spectral data of a target area through the unmanned aerial vehicle; the reflectivity operation module is used for acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle; the model selection module is used for selecting the BRDF model according to the earth surface type of the target area; and the BRDF characteristic operation module is used for analyzing the multi-angle reflectivity based on the BRDF model and acquiring the two-way reflectivity characteristic of the target area.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the calibration reference field BRDF characterization method according to any one of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the calibration reference field BRDF characterization method according to any one of the above first aspects.
According to the BRDF characteristic analysis method and system for the calibration reference field, provided by the embodiment of the invention, the unmanned aerial vehicle is used for carrying the spectrometer to carry out multi-angle data observation, the influence of the change of the sun position on measurement is reduced, meanwhile, the BRDF characteristic of the outdoor field is measured in a non-contact manner, the influence of the instrument and equipment on the measurement of the surface spectral characteristic is reduced, and the observation precision is improved.
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In order to more clearly illustrate the embodiments of the present invention 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, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a calibration reference field BRDF characterization method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of each angle in a BRDF characterization method based on a calibration reference field according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a calibration reference field BRDF characterization system according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an electronic device according to an embodiment of the invention;
FIG. 5 is a schematic diagram of a reflectivity simulation in a normal direction of a test field according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a comparison between a reflectivity simulation result and an actual observation value of a remote sensor at a 555nm band according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a comparison of a reflectivity simulation result with an actual observation of a remote sensor at 645nm according to an embodiment of the present disclosure;
FIG. 8 is a table illustrating the results of BRDF model simulations over the entire 400-2400nm observation wavelength range;
FIG. 9 is a table showing the relative deviation of the results of the BRDF model simulation over the entire observed wavelength range of 400-2400 nm.
Fig. 10 is a schematic view showing the structure of a parameter for correcting the observation geometry due to the shape of the feature.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. 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 invention.
The remote sensing digital image is two-dimensional remote sensing information recorded in a digital form, namely the content of the remote sensing digital image is obtained by a remote sensing means, and the remote sensing digital image is generally electromagnetic spectrum information of different wave bands of a ground object. The pixel values are called luminance values (or called gray-level values and DN values). The DN value (Digital Number) is the pixel brightness value of the remote sensing image, and the gray value of the recorded ground object, and the value is related to the radiation resolution, the ground object emissivity, the atmospheric transmittance, the scattering rate and the like of the sensor.
Not only is the reflection directional, but the direction also depends on the direction of incidence, i.e. the reflection on the surface of the object varies significantly with the angle of incidence of the sun and the angle of observation, which is the Bidirectional Reflection (BRDF). BRDF is used to express the law of reflection of a surface of an object against external radiation. The BRDF distribution function is defined as the ratio of the fractional increase in surface irradiance from the incident direction to the resulting reflected radiance increase in the reflected direction. Since the BRDF adopts a bi-directional definition and uniformly scales the contribution of external radiation to the brightness of the reflected radiation with the irradiance of the incident radiation, the BRDF is essentially a physical quantity purely describing the reflection characteristics of an object, regardless of the spatial distribution characteristics of the incident radiation.
In the field of remote sensing, calibration is generally divided into geometric calibration and radiometric calibration. Geometric calibration means that the geometric characteristics of the remote sensing image are corrected to restore the real situation. Radiometric calibration refers to calibrating the radiometric degree of the remote sensing image so as to realize quantitative remote sensing. Radiometric calibration, which may also be referred to as calibration, is primarily intended to ensure the accuracy of the sensor in obtaining remote sensing data. In general, each link in a system link is subjected to error correction by adopting an internal monitoring loop of the system and an external standard target method, so as to realize a radiometric calibration process.
The radiometric calibration is to convert the brightness gray value of an image into absolute radiance when a user needs to calculate the spectral reflectivity or spectral radiance of a ground object, or needs to compare images acquired by different sensors at different times. Radiometric calibration has several calibration modes according to position: laboratory calibration, spectrum calibration, on-satellite calibration and field calibration. The field calibration refers to that the satellite remote sensor selects a radiometric calibration field under the normal operation condition, and the satellite remote sensor is calibrated through ground synchronous measurement, so that the field calibration can realize calibration of full aperture, full view field and full dynamic range, and the influence of atmospheric transmission and environment is considered. The calibration method can realize absolute correction under the condition that the satellite remote sensor is completely the same as the condition of acquiring the ground image in the running state, can provide calibration in the whole service life of the satellite remote sensor, and can carry out authenticity check on the satellite remote sensor and correctness check on some models. But the ground target is a typical uniform stable target, and the ground calibration must simultaneously measure and calculate the atmospheric environment parameter and the ground object reflectivity when the satellite remote sensor passes through the top.
The key of the measurement and modeling of the surface BRDF characteristic is to carry out accurate multi-angle reflection measurement. The on-board calibration is used for checking the calibration condition of a satellite remote sensor in flight frequently, and an internal calibration method is generally adopted, namely a radiation calibration source and a calibration optical system are both arranged on an aircraft (UAV), and the irradiance of the sun can be regarded as a constant outside the atmosphere, so that the sun can be selected as a reference light source, and the satellite-borne imaging spectrometer can be calibrated absolutely through a sun calibration system. Compared with the traditional measuring method, the multi-angle reflection measurement can be conveniently carried out by using the unmanned aerial vehicle platform to carry the light optical sensor. On one hand, observation angles and pixel dimensions can be flexibly configured according to specific requirements, and on the other hand, the influence of the equipment on the surface illumination conditions and spectral characteristics can be reduced to the maximum extent.
In summary, in order to overcome many defects caused by the fact that a multi-angle measuring rack is required to realize multi-angle observation of the earth's surface when analyzing (i.e. calibrating) the BRDF characteristics of the calibration reference field in the prior art, an embodiment of the present invention provides a method for analyzing the BRDF characteristics of the calibration reference field, as shown in fig. 1, including, but not limited to, the following steps:
step S1: acquiring multi-angle observation geometric data and corresponding spectral data of a target area by using an unmanned aerial vehicle;
step S2: acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle;
step S3: selecting a BRDF model according to the earth surface type of the target area;
step S4: analyzing the multi-angle reflectivity based on a BRDF model to obtain the bidirectional reflectivity characteristic of a target area;
step S5: and comparing the bidirectional reflectivity characteristic with remote sensing data acquired by the satellite remote sensor to obtain BRDF remote sensing product verification of the satellite remote sensor.
The flight altitude of the unmanned aerial vehicle is preferably an atmospheric stratosphere, so that errors caused by external interference such as atmospheric airflow and aerosol on a final result can be reduced to the maximum extent.
In an actual observation experiment, in a target area, data to be acquired comprises two types of geometric data and spectral data observed from multiple angles. Wherein, the observation geometry data mainly includes an observation altitude angle (θ for short)r) Observed azimuth (observed azimuth)h angle, abbreviation
Figure BDA0002358359850000081
) Solar altitude angle (theta for short)i) And solar azimuth angle (short for)
Figure BDA0002358359850000082
). Namely, the observation azimuth, the solar altitude and the solar azimuth of the area are simultaneously acquired at each different observation altitude.
The spectral data (also called spectral library) can be a set of various ground feature reflection spectral data measured by a hyperspectral imaging spectrometer under certain conditions, and by analyzing the spectral data, the information of remote sensing images can be accurately interpreted, the matching of unknown ground features can be quickly realized, and the remote sensing classification recognition level can be improved.
The reflectivity is the ratio of the reflection energy of the surface of the ground object to the incident energy reaching the surface of the ground object, and is the core of optical remote sensing. The dichroism reflectivity distribution function describes the spectral and directional characteristics of the terrestrial object reflectivity, so the dichroism reflectivity distribution function is widely applied to the estimation research of terrestrial biophysical parameters.
In the embodiment of the invention, after the observation azimuth angle is determined by using the unmanned aerial vehicle, the observation altitude angle, the solar altitude angle and the solar azimuth angle of each observation azimuth angle are obtained, wherein the related data obtained by each observation azimuth angle can be regarded as a group; and further, calculating the reflectivity of the angle by combining the acquired related data set of each observation azimuth angle and the acquired spectral data of the angle in the target area. Therefore, the reflectivity of multiple angles can be acquired through the observation of multiple angles.
In the embodiment of the present invention, according to the overall reflection characteristic of the target area, a suitable BRDF model is selected to comprehensively analyze the multi-angle reflectivity obtained in step S2 to obtain the two-way reflectivity characteristic of the target area.
According to the BRDF characteristic analysis method and system for the calibration reference field, provided by the embodiment of the invention, the unmanned aerial vehicle is used for carrying the spectrometer to carry out multi-angle data observation, the influence of the change of the sun position on the measurement is reduced, meanwhile, the BRDF characteristic of the outdoor field is measured in a non-contact manner, the influence of the instrument and equipment on the surface spectral characteristic is reduced, and the observation precision is improved.
Based on the content of the foregoing embodiments, as an alternative embodiment, the step S1 of using the unmanned aerial vehicle to acquire the multi-angle observation geometric data and the corresponding spectrum data of the target area includes, but is not limited to, the following ways:
acquiring multi-angle observation geometric data by using a holder and navigation equipment carried on the unmanned aerial vehicle, wherein the multi-angle observation geometric data comprises a multi-angle observation altitude angle and an observation azimuth angle; meanwhile, the spectrometer carried on the unmanned aerial vehicle is used for obtaining spectral measurement data of the target area under multiple angles and spectral standard data of the reference white board.
Specifically, in the embodiment of the invention, an intelligent tripod head system (including a camera on a tripod head) and a multifunctional spectrometer are firstly installed and fixed on each unmanned aerial vehicle, and meanwhile, each unmanned aerial vehicle is provided with a navigation device. The camera fixed on the pan-tilt can adjust the horizontal angle and the pitching angle, and is provided with a locking adjusting mechanism after reaching a preset working posture. This cloud platform can communicate with ground to control the signal and carry out the adjustment of different shooting angles according to ground.
The positioning work of the unmanned aerial vehicle navigation equipment is mainly completed by a combined positioning and orientation navigation system, the combined navigation equipment can output position and attitude information in a closed loop mode in real time, accurate direction reference and position coordinates are provided for the unmanned aerial vehicle, and meanwhile, the flight state of the unmanned aerial vehicle is predicted in real time according to the attitude information. The integrated navigation equipment can be composed of a laser gyro strapdown inertial navigation system, a satellite positioning system receiver, an integrated navigation computer, a speedometer, an altitude meter, a base station radar system and the like. Meanwhile, the positioning precision and the autonomy of SAR image navigation and the attitude determination precision of a starlight navigation system of a star sensor can be combined, so that the autonomous flight of the unmanned aircraft is ensured.
Furthermore, the observation angle mainly comprises an observation altitude angle and an observation azimuth angle, and can be obtained by a holder and navigation equipment carried by the unmanned aerial vehicle; the solar angle mainly comprises a solar altitude angle and a solar azimuth angle, and can be obtained by calculation according to the longitude and latitude of an observation area and corresponding observation time.
Further, the spectral data mainly comprises observation data of a spectrometer carried on the unmanned aerial vehicle on the earth surface of the target area and observation data of a reference white board.
The observation data of the reference whiteboard is acquired to provide a standard for correcting later data processing, and specifically, the reference whiteboard may be set in a target area in advance, and the reference whiteboard spectrum data of each angle is acquired while the spectrum data of the angle is performed.
According to the BRDF characteristic analysis method for the calibration reference field, provided by the embodiment of the invention, the measuring instrument arranged on the unmanned aerial vehicle is utilized to obtain the multi-angle observation geometric data and the spectrum data of each angle, so that the defect caused by arranging the multi-angle observation instrument on the ground during the traditional multi-angle data measurement is overcome, the influence of the instrument equipment on the surface spectrum characteristic is reduced, and the observation precision is improved.
Based on the content of the foregoing embodiment, as an optional embodiment, before the obtaining the multi-angle reflectivity, the method may further include: sorting the multi-angle observation geometric data and the sun angle according to the standard angle definition; and supplementing the reference plate radiance data missing during the flight by using the existing observation data.
Specifically, in the embodiment of the invention, earth surface data are observed mainly by using an unmanned aerial vehicle, and the bidirectional reflectivity characteristic of a target area is obtained; and comparing the dichroic reflectivity characteristic obtained by analysis with the observation data of the satellite remote sensor to obtain the decay analysis result of the satellite remote sensor.As shown in fig. 2, the position of the unmanned aerial vehicle is set on the light path of the incident light of the observation data acquired by the remote micro-signaling sensor, and the multi-angle observation geometric data and the solar angle are sorted according to the standard angle definition. Wherein the solid line represents the solar zenith angle θ0And satellite zenith angle thetas(measured from zenith Z) and the dotted line indicates the relative azimuth
Figure BDA0002358359850000101
(measured from the sun's extended azimuth), the dotted line represents the scattering angle Θ (measured from the direction of extension of the direct beam). The zenith angle refers to an included angle between a light incident direction and a zenith direction, the scattering angle refers to an included angle between an incident light and an emergent light in a three-dimensional space, the solar altitude angle refers to an included angle between the incident direction of sunlight and a ground plane, the solar altitude angle and the solar zenith angle are complementary angles, the altitude angle is defined as an angle relative to a ground surface normal line, namely the altitude angle in the normal direction is 0 degree; the azimuth is calculated from the true north direction (the same as the north direction of the central meridian in the same geographical division/zone) of the target object, i.e. the azimuth in the true north direction is defined as 0 ° and is rotated clockwise, and the azimuth in the true south direction is 180 °. In practical calculation, only the solar azimuth angle needs to be acquired
Figure BDA0002358359850000102
And observation azimuth angle
Figure BDA0002358359850000103
Relative azimuth angle therebetween
Figure BDA0002358359850000104
The reflectivity in different observation angle directions can be calculated by combining the observation of the spectrometer on the earth surface and the reference white board.
According to the BRDF characteristic analysis method for the calibration reference field, which is provided by the embodiment of the invention, the characteristic of the bidirectional reflectivity of the target area at the same observation angle is obtained by analyzing and comparing the multi-angle observation values obtained by the unmanned aerial vehicle and the satellite remote sensor at the same observation angle, so that a quantitative analysis model of decay of the remote sensor is effectively simplified, and the detection precision is improved.
Based on the content of the foregoing embodiment, as an alternative embodiment, the selecting a BRDF model according to the surface type of the target area includes: and selecting one of a Ross-Li Kernel Model or a Rahman-Pinty-Verstraete Model according to different surface types of the target area.
Generally, the reflectivity of bare soil is the highest, the second in forest or bush, and the reflectivity of the earth surface in urban areas is low as a whole; and the reflectivity fluctuates due to the difference in moisture content of each region, such as bare soil.
At present, common BRDF models comprise a Ross-Li Kernel Model (AMBRALS) and a Rahman-Pitty-Verstrate (RPV) Model used in MODIS BRDF/Albedo products, and the multi-angle reflectivity can be analyzed by utilizing the Ross-Roujean Model according to the surface type of a target area.
The AMBRALS model, the RPV model and the Ross-Roujean model can better simulate the spectral data of various landmark types, but no model can keep the error minimum in all surface type fitting.
Where the Ross-Li Kernel Model is a linear Model that models the shape of the distribution of the two-way reflections by using different combinations of kernels. For example, in the fitting to forest types, the data fitting capability of the RossThick-LiSpareR model is stronger, the inversion result is closer to the true value, and the stability is high. The RPV model is a nonlinear BRDF model that minimizes the fitting error to urban and shrub types. And the RPV model is simpler and more stable in the process of performing parameter-dependent inversion on the reflectivity of cities and shrubs.
According to the BRDF characteristic analysis method for the calibration reference field, different BRDF models are selected according to the difference of the surface types of the target area, so that the bidirectional reflectivity characteristic of the target area is obtained by inverting the obtained multi-angle reflectivity, and the accuracy of the result is effectively improved.
Based on the content of the above embodiment, as an alternative embodiment, the calculation formula of the Ross-Li Kernel Model is as follows:
Figure BDA0002358359850000111
wherein R is a Ross-Li Kernel Model function, kisoIs an isotropic kernel function, kgeoIs a geometric optical kernel function, kvolAs a function of the volume scatter sum, kiso、kgeoAnd kvolAs a kernel function of the angle of incidence and reflection, fisoIs an isotropic kernel function coefficient, fgeoIs a coefficient of a geometric optical kernel function, fvolIs the volume scattering and the function coefficient, lambda is the incident light wavelength, thetaiIs the solar altitude angle thetarIn order to observe the altitude angle,
Figure BDA0002358359850000121
is the azimuth angle of the sun
Figure BDA0002358359850000122
And observation azimuth angle
Figure BDA0002358359850000123
Relative azimuth angle therebetween.
The specific calculation formula of each parameter is as follows:
kiso＝1；
Figure BDA0002358359850000124
Figure BDA0002358359850000125
Figure BDA0002358359850000126
o is the overlap between the incident and emergent shadows, and is related to two variables, i.e., b/r (b/r is 1) and h/b (h/b is 2), in addition to the incident and emergent angles, and represents the shape of the ground object, h is the distance seen by the canopy and the ground surface in volume scattering, r is the radius of the canopy, and b is the height of the canopy. The calculation formula is as follows, where D represents the distance between the incident and the outgoing ray vectors:
Figure BDA0002358359850000127
Figure BDA0002358359850000128
Figure BDA0002358359850000129
Figure BDA00023583598500001210
Figure BDA00023583598500001211
wherein, theta ″, isrIs to thetarCorrected angle, theta ″, is carried outiIs to thetaiThe corrected angle is performed. Fig. 10 is a schematic structural diagram of parameters for correcting the observation geometry due to the shape of the ground object, wherein the interrelation among the canopy height parameter b, the canopy-to-surface observed distance parameter h in volume scattering, and the canopy radius parameter r in the embodiment of the present invention is shown in fig. 10.
Wherein the calculation formula of the Rahman-Pinty-Verstraete model is as follows:
Figure BDA00023583598500001212
Figure BDA00023583598500001213
Figure BDA00023583598500001214
Figure BDA0002358359850000131
wherein R is a Rahman-Pinty-Verstraete model function rho0Representing the overall reflectivity observed value, M being a Minnarter diffuse reflection model, k representing the spherical anisotropy, F being a Henyey Greenstein phase function responsible for explaining the scattering characteristics, ξ being the scattering angle between the incident ray and the emergent ray vector, g being an empirical parameter describing the scattering phase function>0 denotes forward scattering dominance, g<0 represents the backscatter dominance; h is used for expressing the hot spot effect, delta is the correction quantity of the model, rhocWidth representation for hot spots, which can be generally considered as ρcAnd rho0Equal, D is the distance between the incoming and outgoing ray vectors.
Based on the content of the foregoing embodiment, as an optional embodiment, after the foregoing analyzing the multi-angle reflectivity based on the BRDF model, the method may further include the following steps:
inverting the optimal coefficient combination of the fitting observation parameters of the BRDF model by using a least square method; according to the coefficient combination, obtaining the bidirectional reflectivity characteristic of the target area under the conditions of any solar incident angle and observation angle; wherein, the minimum error function of the least square method is:
Figure BDA0002358359850000132
wherein d is the number of observed samples minus the model coefficient; ω represents a weighting factor for the observation, and n represents the corresponding nth observation; e.g. of the type2I.e. the solution of the error function, e2The smaller the size, the more accurate the fitting effect.
In particular toIn the embodiment of the invention, the least square method is used for inversing and fitting the coefficient combination with the optimal observation parameters, namely the known thetai，θrAnd
Figure BDA0002358359850000133
and obtaining the reflectivity observed value rho of the angle through the solution of an equation set obtained by minimizing the error function, namely obtaining the model coefficient of the BRDF model. After the model coefficients are inverted, the bidirectional reflectivity under any solar incident angle and observation angle condition can be deduced through the model, and the BRDF characteristics of the target area (namely the calibration field) can be obtained.
And finally, comparing the bidirectional reflectivity characteristic of the calibration site with the remote sensing data of the site acquired by the satellite remote sensor to acquire the decay analysis result of the satellite remote sensor, and completing calibration and verification of different satellites.
According to the BRDF characteristic analysis method for the calibration reference field, provided by the embodiment of the invention, the unmanned aerial vehicle is used for acquiring the multi-angle observation data of the target area, so that the limitations on the selection of the calibration field in the prior art are overcome, the applicability and flexibility of the method are effectively improved, and the detection precision is further improved.
The embodiment of the invention provides a calibration reference field BRDF characteristic analysis system, as shown in FIG. 3, including but not limited to the following structures: data acquisition module 1, reflectivity operation module 2, model selection module 3, BRDF characteristic operation module 4 and decay analysis module 5, wherein:
the data acquisition module 1 is used for acquiring multi-angle observation geometric data and corresponding spectral data of a target area through an unmanned aerial vehicle;
the reflectivity operation module 2 is used for acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle;
the model selection module 3 is used for selecting a BRDF model according to the earth surface type of the target area;
the BRDF characteristic operation module 4 is used for analyzing the multi-angle reflectivity based on a BRDF model to obtain the two-way reflectivity characteristic of the target area;
and the decay analysis module 5 is used for comparing the dichroic reflectivity characteristic with the remote sensing data acquired by the satellite remote sensor to acquire a decay analysis result of the satellite remote sensor.
In a specific operation process of the calibration reference field BRDF characteristic analysis system provided in the embodiments of the present invention, any one of the calibration reference field BRDF characteristic analysis methods described in the above embodiments is executed, and details are not repeated herein.
According to the BRDF characteristic analysis system for the calibration reference field, provided by the embodiment of the invention, the unmanned aerial vehicle is used for carrying the spectrometer to carry out multi-angle data observation, the influence of the change of the sun position on the measurement is reduced, meanwhile, the BRDF characteristic of the outdoor field is measured in a non-contact manner, the influence of the instrument and equipment on the surface spectral characteristic is reduced, and the observation precision is improved.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device may include: a processor (processor)410, a communication Interface 420, a memory (memory)430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are communicated with each other via the communication bus 440. The processor 410 may call logic instructions in the memory 430 to perform the following method: acquiring multi-angle observation geometric data and corresponding spectral data of a target area by using an unmanned aerial vehicle; acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle; selecting a BRDF model according to the earth surface type of the target area; analyzing the multi-angle reflectivity based on a BRDF model to obtain the bidirectional reflectivity characteristic of the target area; and comparing the dichroic reflectivity characteristic with remote sensing data acquired by the satellite remote sensor to acquire a decay analysis result of the satellite remote sensor.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. 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.
An embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions, when the program instructions are executed by a computer, the computer can execute the methods provided by the above method embodiments, for example, the method includes: acquiring multi-angle observation geometric data and corresponding spectral data of a target area by using an unmanned aerial vehicle; acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle; selecting a BRDF model according to the earth surface type of the target area; analyzing the multi-angle reflectivity based on a BRDF model to obtain the bidirectional reflectivity characteristic of the target area; and comparing the dichroic reflectivity characteristic with remote sensing data acquired by the satellite remote sensor to acquire a decay analysis result of the satellite remote sensor.
Embodiments of the present invention provide a non-transitory computer-readable storage medium storing server instructions, where the server instructions cause a computer to execute the method provided in the foregoing embodiments, for example, the method includes: acquiring multi-angle observation geometric data and corresponding spectral data of a target area by using an unmanned aerial vehicle; acquiring multi-angle reflectivity according to the multi-angle observation geometric data and the corresponding spectrum data and by combining the sun angle of each observation angle; selecting a BRDF model according to the earth surface type of the target area; analyzing the multi-angle reflectivity based on a BRDF model to obtain the bidirectional reflectivity characteristic of the target area; and comparing the dichroic reflectivity characteristic with remote sensing data acquired by the satellite remote sensor to acquire a decay analysis result of the satellite remote sensor.
With reference to the above description, the unmanned aerial vehicle is used to carry the PSR spectrometer to perform an out-of-field experiment, wherein the observation wavelength range is 400-2400 nm. Specifically, the observation angle is configured to: observing a zenith angle: 0-36 degrees, and measuring at intervals of 6 degrees; observing an azimuth angle: 0-360, measure according to 30 intervals, in order to obtain more exact experimental data, carry out the multi-angle observation experiment of several shelves to even place target in this embodiment.
1) For each angle and corresponding reflectivity data processing:
and projecting the acquired angle and the observation data in a plane according to the distribution of the angle so as to detect the accuracy of the observation data.
Fig. 5 is a reflectance simulation diagram in the normal direction of the test site according to the embodiment of the present invention, in which the abscissa represents the wavelength of the spectral band, the ordinate represents the reflectance, and the solid line in the diagram represents the observed value of the unmanned aerial vehicle. As shown in fig. 5, the observed field reflectance is reflected, and the reliability of the observation device is demonstrated by comparison with data in the previous experiment.
2) Selecting a proper BRDF model for parameter inversion:
specifically, from the aspects of the ground surface type of the test site, the calculated amount of the model and the like, the AMBRALS algorithm in the MODIS BRDF/Albedo product is adopted, and the RossThick-LiSpareR nuclear combination mode is specifically used.
3) Regarding simulation results and verification thereof:
in the embodiment of the invention, the coefficients of the BRDF model obtained by inversion are related to the observed wave bands, and the simulation results of part of the wave bands are shown in FIGS. 6 and 7, wherein FIG. 6 is a comparison graph of the simulation results of the reflectivity at the 555nm wave band and the actual observed values of the remote sensors, and FIG. 7 is a comparison graph of the simulation results of the reflectivity at the 645nm wave band and the actual observed values of the remote sensors; wherein the horizontal axis is the observed value of the remote sensor, the vertical axis is the analog value, and the dotted line is the 1:1 line.
As can be seen from fig. 6 and 7: the decision coefficient R2 and the root mean square error RMSE both demonstrate the accuracy of the fit between the simulation results and the observed values, indicating the reliability of the model.
FIG. 8 is a schematic diagram showing a table of simulation results of the BRDF model over the entire observation wavelength range of 400-2400nm, wherein the solar zenith angle is 40.5 degrees, the solar azimuth angle is 163 degrees, the abscissa is the wavelength of incident light, the ordinate is the reflectivity, the dotted line part in the diagram is the actual observation value of the remote sensor, and the dotted line part is the simulation value; FIG. 9 is a table showing the relative deviation of the results of the BRDF model simulation over the entire observed wavelength range of 400-2400nm, wherein the ordinate is the degree of deviation (percentage); as shown in fig. 8, it can be known that: except for a partial absorption waveband, a simulation result obtained by using the BRDF model almost coincides with an observed value of a remote sensor, and the result proves that the fitting effect of multi-angle reflectivity in the vertical direction (normal direction) analyzed in the embodiment is the best, the root mean square error RMSE is 0.0041, which shows that the error between the simulation result and the observed value is very small, and the model has a good fitting effect on data. As shown in fig. 9, the fitting relative error of a single band can be obtained, and it can be known that the overall relative deviation of the model is basically within 1%, which fully proves the accuracy of the result of the BRDF characteristic analysis method for the calibration reference field provided by the embodiment of the present invention.
The BRDF characteristic analysis method for the calibration reference field provided by the embodiment of the invention completely proves that: utilize unmanned vehicles to carry the spectrum appearance, carry out multi-angle observation data, the influence of reduction sun position change to measuring that can to a great extent, owing to realized the non-contact measurement to outdoor place BRDF characteristic simultaneously, reduced the influence of instrument and equipment self to earth's surface spectral characteristic, improved the observation precision.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A calibration reference field BRDF characterization method is characterized by comprising the following steps:
acquiring multi-angle observation geometric data and corresponding spectral data of a target area by using an unmanned aerial vehicle;
aiming at the observed reference plate radiance and the corresponding solar zenith angle, calculating the change of the reference plate radiance in the flight period by utilizing linear interpolation, and supplementing the reference plate spectral information missing in the flight period;
dividing the obtained earth observation result by the calculated reference plate radiance, and correcting the reference plate directivity factor to obtain multi-angle reflectivity data;
drawing the acquired multi-angle reflectivity data according to space and spectrum dimensions, analyzing the reasonability of a spectrum curve and reflectivity space distribution, and judging the quality of observation data;
selecting a BRDF model according to the earth surface type of the target area;
analyzing the multi-angle reflectivity based on the BRDF model to obtain the bidirectional reflectivity characteristic of the target area;
and comparing the bidirectional reflectivity characteristic with remote sensing data acquired by a satellite remote sensor, and verifying BRDF products of related satellite remote sensing targets.
2. The BRDF characterization method according to claim 1, wherein the acquiring of the multi-angle observation geometry data and the corresponding spectrum data of the target area using the UAV comprises:
acquiring the multi-angle observation geometric data by using a holder and navigation equipment carried on the unmanned aerial vehicle, wherein the multi-angle observation geometric data comprises multi-angle observation altitude angles and observation azimuth angles;
and acquiring spectral measurement data of the target area and spectral standard data of the reference white board at multiple angles by using the spectrometer carried on the unmanned aerial vehicle.
3. The method for BRDF characterization of a scaled reference field according to claim 2, further comprising, prior to said obtaining the multi-angle reflectivity data:
sorting the multi-angle observation geometric data and the sun angle according to standard angle definition;
and supplementing the reference plate radiance data missing during the flight by using the existing observation data.
4. The method for BRDF characterization according to claim 1, wherein said selecting a BRDF model based on the surface type of the target region comprises:
and selecting one of a Ross-Li Kernel Model or a Rahman-Pinty-Verstraete Model according to different surface types of the target area.
5. The method for BRDF characterization of a scaled reference field according to claim 4, wherein the Ross-Li Kernel Model has the formula:
Figure FDA0002358359840000021
wherein R is a Ross-Li Kernel Model function, kisoIs an isotropic kernel function, kgeoIs a geometric optical kernel function, kvolAs a function of the volume scatter sum fisoIs an isotropic kernel function coefficient, fgeoIs a coefficient of a geometric optical kernel function, fvolIs the volume scattering and the function coefficient, lambda is the incident light wavelength, thetaiIs the solar altitude angle thetarIn order to observe the altitude angle,
Figure FDA0002358359840000022
is the azimuth angle of the sun
Figure FDA0002358359840000023
And observation azimuth angle
Figure FDA0002358359840000024
Relative azimuth angle therebetween.
6. The method for BRDF characterization of a scaled reference field according to claim 5, wherein the formula for the Rahman-Pinty-Verstraete model is:
Figure FDA0002358359840000025
Figure FDA0002358359840000026
Figure FDA0002358359840000027
Figure FDA0002358359840000028
wherein R is a Rahman-Pinty-Verstraete model function rho0The method comprises the steps of representing an overall reflectivity observation value, wherein M is a Minnarter diffuse reflection model, k represents spherical anisotropy, F is a Henyey Greenstein phase function, ξ is a scattering angle between incident ray and emergent ray vectors, g is an empirical parameter describing a scattering phase function, g is more than 0 and represents forward scattering dominance, and g is less than 0 and represents backward scattering dominance; h is used for expressing the hot spot effect, delta is the correction quantity of the model, rhocFor the width of the hot spot, D is the distance between the incoming and outgoing ray vectors.
7. The method for BRDF characterization of a scaled reference field according to claim 6, further comprising, after said analyzing the multi-angle reflectivity based on the BRDF model:
inverting the coefficient combination with the optimal fitting observation parameters of the BRDF model by using a least square method;
according to the coefficient combination, obtaining the bidirectional reflectivity characteristic of the target area under the conditions of any solar incident angle and observation angle;
wherein the minimized error function of the least squares method is:
Figure FDA0002358359840000031
wherein d is the number of observed samples minus the model coefficient; ω denotes a weighting factor of the observed value, nRepresenting a corresponding nth observation; e.g. of the type2I.e. the solution of the error function, e2The smaller the size, the more accurate the fitting effect.
8. A scaled reference field BRDF characterization system, comprising:
the data acquisition module is used for acquiring multi-angle observation geometric data and corresponding spectral data of a target area through the unmanned aerial vehicle;
the reflectivity operation module is used for acquiring multi-angle reflectivity according to the multi-angle observation geometric data, the corresponding spectrum data and the reference whiteboard spectrum data and by combining the sun angles of all observation angles;
the model selection module is used for selecting a BRDF model according to the earth surface type of the target area;
the BRDF characteristic operation module is used for analyzing the multi-angle reflectivity based on the BRDF model to acquire the bidirectional reflectivity characteristic of the target area;
and the satellite remote sensing product verification module is used for comparing the bidirectional reflectivity characteristic with remote sensing data acquired by a satellite remote sensor and verifying the BRDF product precision of the satellite.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the steps of the method for BRDF characterization of a scaled reference field according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for BRDF characterization of a scaled reference field as claimed in any one of claims 1 to 7.
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