A multi-machine combined real-time bidirectional reflectance distribution function modeling system and method

Abstract

The application provides a kind of multi-machine joint real-time bidirectional reflectance distribution function modeling system and method, comprising: planning the flight height and spatial distribution of multi-machine formation relative to target, controlling the synchronous collection of multi-angle spectral radiance, solar downward irradiance and solar zenith angle and solar azimuth angle data of target by formation, and using data to calculate and output the BRDF model of target in real time.The application effectively improves the BRDF modeling accuracy by measuring the solar downward irradiance on the aircraft in real time.At the same time, multi-unmanned aerial vehicle and ground-air cooperative observation are used, and multi-angle data can be obtained synchronously in a single flight mission, replacing the traditional ground point distribution mode, greatly improving the data acquisition efficiency and greatly reducing the labor and time cost.

Description

Technical Field

[0001] This invention relates to the field of remote sensing data processing technology, and in particular to a multi-machine collaborative real-time bidirectional reflectance distribution function modeling system and method. Background Technology

[0002] The bidirectional reflectance distribution function of a ground target describes the reflectance characteristics of the target in different directions, and has very important applications in remote sensing sensor calibration and high-precision quantitative remote sensing inversion.

[0003] Existing ground-based bidirectional reflection distribution function (BRDF) observation systems typically employ a dual-track adjustment method to adjust the observation angle, enabling BRDF data acquisition at altitudes below 3 meters. However, this process is cumbersome, difficult to maneuver, requires significant manual intervention, and is generally non-imaging, making data matching with imaging platforms such as satellites or UAVs difficult. With the miniaturization of UAV technology and spectral imagers, the use of UAV platforms equipped with spectral imagers for BRDF data acquisition is becoming increasingly widespread. However, current acquisition methods primarily rely on single-machine point-by-point scanning, and solar irradiance must be obtained using a standard reference plate. This method not only results in low acquisition efficiency but also makes it difficult to avoid measurement errors introduced by changes in solar irradiance during the acquisition process.

[0004] In recent years, the continuous decline in UAV costs and the maturity of multi-UAV swarm control technology have made multi-UAV collaborative observation an effective way to improve mission efficiency. Meanwhile, the payload capacity of UAVs has significantly increased from the hundred-gram level to the kilogram level, laying the foundation for simultaneously mounting multiple observation devices on a single platform. Furthermore, driven by both enhanced computing power and miniaturization, edge computing devices are now capable of supporting real-time data processing and modeling in field environments. However, existing BRDF observation systems have not yet implemented joint data acquisition using UAVs.

[0005] Therefore, given the limitations mentioned above, it is necessary to propose a joint real-time bidirectional reflectance distribution function modeling system and method that can effectively support near-real-time quantitative remote sensing modeling and inversion of spectral imagers. Summary of the Invention

[0006] This invention provides a multi-machine collaborative real-time bidirectional reflectance distribution function modeling system and method to overcome the deficiencies in the existing technology and effectively support near-real-time quantitative remote sensing modeling and inversion of spectral imagers.

[0007] In a first aspect, the present invention provides a multi-machine collaborative real-time bidirectional reflection distribution function modeling system, comprising:

[0008] The system includes an edge intelligent computing module, a solar position measurement module, a network relay module, a synchronization trigger module, and at least one space-based data acquisition module, wherein the network relay module is connected to the other modules respectively.

[0009] The edge intelligent computing module is used for comprehensive system control, receiving and processing data from the network relay module in real time, and modeling the target's bidirectional reflection distribution function (BRDF) in real time.

[0010] The solar position measurement module is used to measure the solar zenith angle and solar azimuth angle of the target area in real time.

[0011] The network relay module is used for communication and data transmission between modules within the system;

[0012] The synchronization triggering module is used to control the synchronous acquisition of data from each airborne data acquisition module within the system.

[0013] The airborne data acquisition module is used to acquire the directional reflectivity data of the target.

[0014] According to the present invention, a multi-aircraft joint real-time bidirectional reflectance distribution function modeling system is provided, wherein the airborne data acquisition module includes a multi-rotor UAV, a spectral imager, a solar irradiance meter, a three-axis stabilization unit, an RTK positioning unit, a communication unit, and a flight control unit, wherein:

[0015] The spectral imager observes vertically downwards to acquire directional reflectance data of the target area;

[0016] The solar irradiance meter observes vertically upwards to obtain downward solar irradiance data;

[0017] The triaxial stabilization unit is used to provide stable observation conditions;

[0018] The RTK positioning unit is used to provide positioning information;

[0019] The communication unit is used to receive instructions and transmit data;

[0020] The flight control unit is used for the attitude control of the multi-rotor UAV and performs maneuvers after receiving flight commands.

[0021] Secondly, the present invention also provides a multi-machine joint real-time bidirectional reflection distribution function modeling method, comprising:

[0022] Laboratory radiometric calibration of all spectral imagers was performed using an integrating sphere to obtain optical distortion parameters, absolute radiometric calibration gain coefficient, and bias coefficient.

[0023] The RTK positioning unit determines the coordinates of the observed target, the flight altitude of the multi-rotor UAV during the mission, and the spatial distribution during the data acquisition mission;

[0024] The synchronization trigger module and all airborne data acquisition modules are activated to synchronously acquire BRDF modeling data of the observed targets;

[0025] All BRDF modeling data acquired by the space-based data acquisition module are transmitted back to the network relay module and the edge intelligent computing module in real time.

[0026] If the number of multi-rotor UAVs is lower than the preset threshold or the observation target has a preset complex surface condition, then keep the horizontal distance between each multi-rotor UAV and the observation target unchanged, take the observation target as the center, change the observation azimuth angle according to the circular arc maneuver, and repeat the steps of synchronous acquisition of BRDF modeling data and real-time transmission of BRDF modeling data to supplement the data.

[0027] The edge intelligent computing module completes data processing and BRDF modeling based on the final BRDF modeling data, and outputs the BRDF modeling results.

[0028] According to the present invention, a multi-machine joint real-time bidirectional reflectance distribution function modeling method is provided, which utilizes an integrating sphere to perform laboratory radiometric calibration on all spectral imagers to obtain optical distortion parameters, absolute radiometric calibration gain coefficients, and bias coefficients, including:

[0029] The response nonuniformity of all spectral imagers was corrected by relative radiometric calibration;

[0030] The absolute radiometric calibration gain coefficient and bias coefficient of all spectral imagers were obtained through absolute radiometric calibration.

[0031] Geometric calibration of all spectral imagers was performed using a standard checkerboard reference plate to obtain the optical distortion parameters of all spectral imagers.

[0032] According to the present invention, a multi-machine collaborative real-time bidirectional reflection distribution function modeling method is provided to determine the spatial distribution during the execution of a data acquisition task, including:

[0033] Keep all spectral imagers vertically downward to obtain the imaging field of view, and use the flight altitude and imaging field of view to obtain the farthest horizontal distance from the multi-rotor UAV to the observed target;

[0034] Before takeoff, all multi-rotor UAVs are positioned on the same straight line as the observation target. Taking the observation target as the origin, the distance between adjacent multi-rotor UAVs is obtained by the farthest horizontal distance and the number of multi-rotor UAVs.

[0035] Take off all multi-rotor drones to their flight altitude, and obtain the observation zenith angle of any multi-rotor drone relative to the observation target based on the distance between adjacent multi-rotor drones;

[0036] Keep the horizontal distance between all multi-rotor UAVs and the observation target constant, and set the angular intervals evenly in a circular motion with the observation target as the center. The angular interval between adjacent multi-rotor UAVs is obtained by dividing the circumferential angle by the number of multi-rotor UAVs.

[0037] Once all multi-rotor UAVs have maneuvered into position and maintained a stable position, obtain the observation azimuth angles of all multi-rotor UAVs relative to the observation target.

[0038] According to the multi-machine joint real-time bidirectional reflection distribution function modeling method provided by the present invention, the synchronization triggering module and all space-based data acquisition modules are activated to synchronously acquire BRDF modeling data of the observed target, including:

[0039] Adjust all spectral imagers to the same parameters as those used in the laboratory radiometric calibration, and once the equipment is stable, synchronously acquire spectral images of the observed targets;

[0040] Once the solar irradiance meter is confirmed to be unobstructed and the equipment is fully stable, the downward solar irradiance at the location of the drone will be collected synchronously.

[0041] After ensuring the solar position measurement module is placed completely horizontally and pointed towards the sun, the solar zenith angle and solar azimuth angle of the observation target are collected simultaneously.

[0042] According to the present invention, a multi-machine collaborative real-time bidirectional reflection distribution function (BRDF) modeling method is provided, in which an edge intelligent computing module completes data processing and BRDF modeling based on the final BRDF modeling data, and outputs the BRDF modeling results, including:

[0043] Radiometric correction of the spectral image of the observed target is performed using the absolute radiometric calibration gain coefficient and the bias coefficient to obtain the radiance of the observed target.

[0044] Based on the solar downlink irradiance and the spectral response functions of each spectral band of the spectral imager, the equivalent solar downlink irradiance of the spectral band is calculated.

[0045] The directional reflectivity of the observed target is calculated from the equivalent downward solar irradiance and radiance of the spectral band.

[0046] The relative azimuth angle between the multi-rotor UAV and the sun is calculated from the solar zenith angle and the solar azimuth angle.

[0047] Using the least squares approach, a Ross-Li kernel-driven BRDF model is constructed by comprehensively observing the zenith angle, solar zenith angle, and relative azimuth angle, and the BRDF modeling results are output in real time.

[0048] Thirdly, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multi-machine joint real-time bidirectional reflection distribution function modeling method as described above.

[0049] Fourthly, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-machine joint real-time bidirectional reflection distribution function modeling method as described above.

[0050] The multi-drone joint real-time bidirectional reflectance distribution function modeling system and method provided by this invention has a high degree of independence and real-time performance. It can autonomously complete the entire process of data acquisition and processing in the field, significantly improving work efficiency and timeliness. By controlling the synchronous acquisition of each data acquisition module in the system through a synchronous triggering module, it effectively avoids the additional errors introduced by the time difference of data acquisition. The proposed method measures the solar irradiance at the location of the UAV in real time through a solar irradiance meter, effectively avoiding the errors caused by changes in sunlight during the data acquisition process of traditional methods. By using a multi-UAV network and ground-to-air synchronous acquisition method, it effectively improves the data acquisition efficiency while reducing the intensity of manual work. The UAV formation maintains the same flight altitude and adjusts the observation angle by changing the relative distance to the target. This method is more consistent with the satellite Earth observation method, thereby significantly improving the matching degree of the acquired data with satellite data. Furthermore, a three-axis stabilization unit provides stable observation support for the spectral imager and the solar irradiance meter, effectively avoiding the observation errors introduced by the tilt and jitter of the UAV during flight. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0052] Figure 1 This is a schematic diagram of the multi-machine joint real-time bidirectional reflection distribution function modeling system provided by the present invention;

[0053] Figure 2 This is a flowchart illustrating the multi-machine collaborative real-time bidirectional reflection distribution function modeling method provided by the present invention;

[0054] Figure 3 This is a schematic diagram of the spatial distribution of each UAV during mission execution provided by the present invention;

[0055] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0057] Figure 1 This is a schematic diagram of the structure of the multi-machine joint real-time bidirectional reflection distribution function modeling system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, it includes:

[0058] The system includes an edge intelligent computing module, a solar position measurement module, a network relay module, a synchronization trigger module, and a space-based data acquisition module, with the network relay module connected to the other modules respectively.

[0059] The edge intelligent computing module is used for comprehensive control of the system, and to receive data from the network relay module in real time for processing and real-time BRDF modeling of the target.

[0060] The solar position measurement module is used to measure the solar zenith angle and solar azimuth angle data of the target area in real time.

[0061] The network relay module is responsible for communication and data transmission between modules within the system;

[0062] The synchronization triggering module is used to control the synchronous acquisition of data by each data acquisition module within the system.

[0063] The airborne data acquisition module is used to collect directional reflectivity data of the target. Multiple modules are typically deployed to form a multi-UAV observation network, including a multi-rotor UAV, a spectroscopic imager, a solar radiometer, a three-axis stabilization unit, a real-time kinematic (RTK) positioning unit, a communication unit, and a flight control unit. The spectroscopic imager observes vertically downwards to acquire directional reflectivity data of the target area; the solar radiometer observes vertically upwards to acquire downward solar irradiance data; the three-axis stabilization unit provides stable observation conditions; the RTK positioning unit provides high-precision positioning information; the communication unit receives commands and transmits data; and the flight control unit is responsible for the attitude control of the UAV and performs maneuvers upon receiving flight commands.

[0064] It is understood that the complete system proposed in this embodiment of the invention includes an edge intelligent computing module, a solar position measurement module, a network relay module, a synchronization triggering module, and a module containing n (usually n) 3) A multi-rotor UAV observation network with an airborne data acquisition module.

[0065] Figure 2 This is a flowchart illustrating the multi-machine collaborative real-time bidirectional reflection distribution function modeling method provided in this embodiment of the invention, as shown below. Figure 2 As shown, it includes:

[0066] S1. Perform laboratory radiometric calibration on the spectral imagers in the n spatial data acquisition modules of the system using an integrating sphere.

[0067] S101. Correct the response nonuniformity of each spectral imager through relative radiometric calibration.

[0068] S102. Obtain the absolute radiometric calibration gain coefficient for each spectral imager through absolute radiometric calibration. and bias coefficient ;

[0069] S2. Geometrically calibrate the spectral imagers in each of the n space-based data acquisition modules in the system using a standard checkerboard reference board to obtain the optical distortion parameters of each spectral imager. ;

[0070] S3. Determine the location of the target and the flight altitude of the multi-rotor UAV;

[0071] S301. In an embodiment of the present invention, the coordinates of the observed target point ( The location is determined by the RTK positioning unit, and manual input can be used if necessary; at the same time, the target to be measured should avoid being a vegetation canopy or man-made object (such as an iron tower, chimney or building) that is too tall or has a large undulation.

[0072] S302. To avoid the impact of the downwash airflow from the multi-rotor UAV on the target area, the minimum flight altitude is limited to 20 meters; at the same time, to avoid the influence of atmospheric absorption or scattering caused by excessive flight altitude, the maximum flight altitude is limited to 100 meters; within this range, the flight altitude H of multiple multi-rotor UAVs can be arbitrarily selected according to the actual situation, and once determined, it will not change during the mission.

[0073] S4. Determine the spatial distribution of multiple multi-rotor UAVs performing data acquisition tasks. ;

[0074] The spectral imager carried by the S401 multi-rotor UAV always maintains a vertically downward observation, and its imaging field of view... Given that the farthest horizontal distance D from the target point to the multi-rotor UAV can be calculated by the following formula;

[0075]

[0076] S402. Before takeoff, place all n multi-rotor drones and the target point on the same straight line. With the target point as the origin, the distance d between each adjacent multi-rotor drone is determined by the following formula.

[0077]

[0078] S403. Simultaneously launch all drones to the set altitude H. Based on the results in step S402, record any... ( The zenith angle observed by the UAVs at the target point ∈(0,n)) Specifically, it is determined by the following formula;

[0079]

[0080] S404. Ensure that the horizontal distance between all multi-rotor UAVs and the target point remains constant, and that they are evenly distributed at angular intervals in a circular maneuver centered on the target point, with the angular interval between adjacent multi-rotor UAVs being... Determined by the following formula;

[0081]

[0082] S405. Once all UAVs have maneuvered to their positions and maintained stability, record the azimuth angles of all UAVs relative to the target point. ;

[0083] like Figure 3 As shown, taking six multi-rotor drones as an example, the spatial distribution of each multi-rotor drone during mission execution is displayed.

[0084] S5. Start the synchronization trigger module and all data acquisition modules, and synchronously acquire the BRDF modeling data of the target point;

[0085] S501. Adjust the spectral imager to the same parameters as the laboratory radiation calibration in step S1. After the equipment is completely stable, synchronously acquire the spectral image of the target.

[0086] S502. Ensure the solar irradiance meter is unobstructed. After the equipment is fully stable, synchronously collect the solar irradiance at the location of the multi-rotor UAV.

[0087] S503. Ensure the solar position measurement module is placed completely horizontally and accurately pointed at the sun, and simultaneously collect the solar zenith angle at the target location. and solar azimuth ;

[0088] S6. The spectral images, downlink solar irradiance, and observed zenith angle collected by each multi-rotor UAV in the airborne data acquisition module are processed. Observation azimuth And the solar zenith angle collected by the solar position measurement module. and solar azimuth It transmits data back to the network relay module in real time, and then further to the edge intelligent computing module;

[0089] S7. When there are few multi-rotor UAVs or the target has a complex surface, keep the horizontal distance between each multi-rotor UAV and the target point unchanged, change the observation azimuth angle in a circular maneuver with the target point as the center, and repeat steps S5 and S6 to supplement the data.

[0090] S8. All data processing and BRDF modeling are completed by the edge intelligent computing module;

[0091] S801. Perform radiometric correction on the spectral image using the absolute radiometric calibration coefficients obtained in step S102 to obtain the radiance of the target. Specifically, it is calculated using the following formula;

[0092]

[0093] in, It is the first Digital quantization values ​​of spectral images in each band. ∈ , ∈ .

[0094] S802. Perform geometric correction on the spectral image using the optical distortion parameters obtained in step S2.

[0095] S803, Based on the solar downhill irradiance obtained in step S502 And the spectral response function (SRF) of each spectral band of the spectral imager is used to calculate the equivalent down-flow solar irradiance of the spectral band. Specifically, it is calculated using the following formula;

[0096]

[0097] in, Let be the spectral response function. Indicates wavelength The differential.

[0098] S804. Calculate the directional reflectivity of the target using the equivalent solar irradiance obtained in step S803. Specifically, it is calculated using the following formula;

[0099]

[0100] S805, Calculate the relative azimuth angle between the multi-rotor UAV and the sun. Specifically, it is calculated using the following formula;

[0101]

[0102] S806. The data obtained in the preceding steps are used to construct a Ross-Li kernel-driven BRDF model using the least squares method and output it in real time. The specific formula is determined by the following formula.

[0103]

[0104] in, and Then respectively in Volume scattering kernel and geometric optics kernel under observation geometry; , as well as These represent the weight coefficients of the various kernel functions that need to be inverted.

[0105] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call the logic instructions in the memory 430 to execute a multi-machine joint real-time bidirectional reflectance distribution function modeling method. This method includes: using an integrating sphere to perform laboratory radiometric calibration on all spectral imagers to obtain optical distortion parameters, absolute radiometric calibration gain coefficients, and bias coefficients; determining the coordinates of the observed target by the RTK positioning unit, determining the flight altitude of the multi-rotor UAV during the mission, and the spatial distribution during the data acquisition mission; activating the synchronization trigger module and all airborne data acquisition modules to synchronously acquire BRDF modeling data of the observed target; transmitting the BRDF modeling data acquired by all airborne data acquisition modules back to the network relay module and the edge intelligent computing module in real time; if the number of multi-rotor UAVs is lower than a preset threshold or the observed target has a preset complex surface condition, keeping the horizontal distance between each multi-rotor UAV and the observed target unchanged, changing the observation azimuth angle according to the circular maneuver with the observed target as the center, and repeating the steps of synchronous acquisition and real-time transmission of BRDF modeling data to supplement the data; and having the edge intelligent computing module complete data processing and BRDF modeling based on the final BRDF modeling data and output the BRDF modeling results.

[0106] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0107] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the multi-machine joint real-time bidirectional reflectance distribution function modeling method provided by the above methods. This method includes: using an integrating sphere to perform laboratory radiometric calibration on all spectral imagers to obtain optical distortion parameters, absolute radiometric calibration gain coefficients, and bias coefficients; determining the coordinates of the observation target by the RTK positioning unit, determining the flight altitude of the multi-rotor UAV during the mission, and the spatial distribution during the data acquisition mission; and activating the synchronization trigger module and all empty bases. According to the acquisition module, BRDF modeling data of the observed target is acquired synchronously; all BRDF modeling data acquired by the airborne data acquisition module is transmitted back to the network relay module and the edge intelligent computing module in real time; if the number of multi-rotor UAVs is lower than the preset threshold or the observed target has a preset complex surface condition, the horizontal distance between each multi-rotor UAV and the observed target is kept constant, and the observation azimuth angle is changed according to the circular arc maneuver with the observed target as the center, and the steps of synchronous acquisition and real-time transmission of BRDF modeling data are repeated to supplement the data; the edge intelligent computing module completes data processing and BRDF modeling based on the final BRDF modeling data and outputs the BRDF modeling results.

[0108] Furthermore, the present invention also provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a multi-machine joint real-time bidirectional reflectance distribution function modeling method provided by the methods described above. This method includes: performing laboratory radiometric calibration of all spectral imagers using an integrating sphere to obtain optical distortion parameters, absolute radiometric calibration gain coefficients, and bias coefficients; determining the coordinates of the observed target using an RTK positioning unit, determining the flight altitude of the multi-rotor UAV during mission execution, and the spatial distribution during data acquisition; activating a synchronization trigger module and all airborne data acquisition modules to perform BR (Browser Reflectance Function) modeling of the observed target. BRDF modeling data is collected synchronously; all BRDF modeling data acquired by the airborne data acquisition module is transmitted back to the network relay module and the edge intelligent computing module in real time; if the number of multi-rotor UAVs is lower than a preset threshold or the observed target has a preset complex surface condition, the horizontal distance between each multi-rotor UAV and the observed target is kept constant, and the observation azimuth angle is changed according to the circular maneuver with the observed target as the center, and the steps of synchronous acquisition and real-time transmission of BRDF modeling data are repeated to supplement the data; the edge intelligent computing module completes data processing and BRDF modeling based on the final BRDF modeling data and outputs the BRDF modeling results.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-machine collaborative real-time bidirectional reflection distribution function modeling method, characterized in that, include: Laboratory radiometric calibration of all spectral imagers was performed using an integrating sphere to obtain optical distortion parameters, absolute radiometric calibration gain coefficient, and bias coefficient. The RTK positioning unit determines the coordinates of the observed target, the flight altitude of the multi-rotor UAV during the mission, and the spatial distribution during the data acquisition mission; The synchronization trigger module and all airborne data acquisition modules are activated to synchronously acquire BRDF modeling data of the observed targets; All BRDF modeling data acquired by the space-based data acquisition module are transmitted back to the network relay module and the edge intelligent computing module in real time. If the number of multi-rotor UAVs is lower than the preset threshold or the observation target has a preset complex surface condition, then keep the horizontal distance between each multi-rotor UAV and the observation target unchanged, take the observation target as the center, change the observation azimuth angle according to the circular arc maneuver, and repeat the steps of synchronous acquisition of BRDF modeling data and real-time transmission of BRDF modeling data to supplement the data. The edge intelligent computing module completes data processing and BRDF modeling based on the final BRDF modeling data, and outputs the BRDF modeling results; The method is based on a multi-machine joint real-time bidirectional reflection distribution function modeling system, the system comprising: The system includes an edge intelligent computing module, a solar position measurement module, a network relay module, a synchronization trigger module, and at least one space-based data acquisition module, wherein the network relay module is connected to the other modules respectively. The edge intelligent computing module is used for comprehensive system control, receiving and processing data from the network relay module in real time, and modeling the target's bidirectional reflection distribution function (BRDF) in real time. The solar position measurement module is used to measure the solar zenith angle and solar azimuth angle of the target area in real time. The network relay module is used for communication and data transmission between modules within the system; The synchronization triggering module is used to control the synchronous acquisition of data from each airborne data acquisition module within the system. The airborne data acquisition module is used to acquire the directional reflectivity data of the target; The airborne data acquisition module includes a multi-rotor UAV, a spectral imager, a solar irradiance meter, a three-axis stabilization unit, an RTK positioning unit, a communication unit, and a flight control unit, wherein: The spectral imager observes vertically downwards to acquire directional reflectance data of the target area; The solar irradiance meter observes vertically upwards to obtain downward solar irradiance data; The triaxial stabilization unit is used to provide stable observation conditions; The RTK positioning unit is used to provide positioning information; The communication unit is used to receive instructions and transmit data; The flight control unit is used for the attitude control of the multi-rotor UAV and performs maneuvers after receiving flight commands.

2. The multi-machine joint real-time bidirectional reflection distribution function modeling method according to claim 1, characterized in that, Laboratory radiometric calibration of all spectral imagers was performed using an integrating sphere to obtain optical distortion parameters, absolute radiometric calibration gain coefficients, and bias coefficients, including: The response nonuniformity of all spectral imagers was corrected by relative radiometric calibration; The absolute radiometric calibration gain coefficient and bias coefficient of all spectral imagers were obtained through absolute radiometric calibration. Geometric calibration of all spectral imagers was performed using a standard checkerboard reference plate to obtain the optical distortion parameters of all spectral imagers.

3. The multi-machine joint real-time bidirectional reflection distribution function modeling method according to claim 1, characterized in that, Determine the spatial distribution when performing data acquisition tasks, including: Keep all spectral imagers vertically downward to obtain the imaging field of view, and use the flight altitude and imaging field of view to obtain the farthest horizontal distance from the multi-rotor UAV to the observed target; Before takeoff, all multi-rotor UAVs are positioned on the same straight line as the observation target. Taking the observation target as the origin, the distance between adjacent multi-rotor UAVs is obtained by the farthest horizontal distance and the number of multi-rotor UAVs. Take off all multi-rotor drones to their flight altitude, and obtain the observation zenith angle of any multi-rotor drone relative to the observation target based on the distance between adjacent multi-rotor drones; Keep the horizontal distance between all multi-rotor UAVs and the observation target constant, and set the angular intervals evenly in a circular motion with the observation target as the center. The angular interval between adjacent multi-rotor UAVs is obtained by dividing the circumferential angle by the number of multi-rotor UAVs. Once all multi-rotor UAVs have maneuvered into position and maintained a stable position, obtain the observation azimuth angles of all multi-rotor UAVs relative to the observation target.

4. The multi-machine joint real-time bidirectional reflection distribution function modeling method according to claim 1, characterized in that, The synchronization trigger module and all space-based data acquisition modules are activated to synchronously acquire BRDF modeling data of the observed targets, including: Adjust all spectral imagers to the same parameters as those used in the laboratory radiometric calibration, and once the equipment is stable, synchronously acquire spectral images of the observed targets; Once the solar irradiance meter is confirmed to be unobstructed and the equipment is fully stable, the downward solar irradiance at the location of the drone will be collected synchronously. After ensuring the solar position measurement module is placed completely horizontally and pointed towards the sun, the solar zenith angle and solar azimuth angle of the observation target are collected simultaneously.

5. The multi-machine joint real-time bidirectional reflection distribution function modeling method according to claim 1, characterized in that, The edge intelligent computing module performs data processing and BRDF modeling based on the final BRDF modeling data, and outputs the BRDF modeling results, including: Radiometric correction of the spectral image of the observed target is performed using the absolute radiometric calibration gain coefficient and the bias coefficient to obtain the radiance of the observed target. Based on the solar downlink irradiance and the spectral response functions of each spectral band of the spectral imager, the equivalent solar downlink irradiance of the spectral band is calculated. The directional reflectivity of the observed target is calculated from the equivalent downward solar irradiance and radiance of the spectral band. The relative azimuth angle between the multi-rotor UAV and the sun is calculated from the solar zenith angle and the solar azimuth angle. Using the least squares approach, a Ross-Li kernel-driven BRDF model is constructed by comprehensively observing the zenith angle, solar zenith angle, and relative azimuth angle, and the BRDF modeling results are output in real time.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-machine joint real-time bidirectional reflection distribution function modeling method as described in any one of claims 1 to 5.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the multi-machine joint real-time bidirectional reflection distribution function modeling method as described in any one of claims 1 to 5.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the multi-machine joint real-time bidirectional reflection distribution function modeling method as described in any one of claims 1 to 5.

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