Observation geometric parameter acquisition method and equipment based on unmanned aerial vehicle sensor image

Abstract

The invention relates to the technical field of unmanned aerial vehicle remote sensing, in particular to an observation geometric parameter acquisition method and device based on an unmanned aerial vehicle sensor image, and the method can accurately calculate the observation geometric parameter of each observation pixel according to the data during the shooting of the unmanned aerial vehicle sensor image. Comprise a solar zenith angle, a solar azimuth angle, an unmanned aerial vehicle zenith angle, an unmanned aerial vehicle azimuth angle and a relative azimuth angle. According to the technical scheme, the calculation accuracy is high, the result precision of unmanned aerial vehicle image inversion can be improved when the calculation result is substituted into a corresponding model for inversion, and technical support is provided for application of unmanned aerial vehicle remote sensing to crops.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) remote sensing, and particularly to a method and device for obtaining observation geometric parameters based on UAV sensor images. Background Art

[0002] In the field of remote sensing, the geometric parameter information of the sun and the sensor is an important basis for ground object remote sensing inversion, image correction, and data interpretation. In remote sensing radiation transfer models, these geometric parameters are important input parameters. For example, in radiation transfer models such as the 6S atmospheric correction model and the PROSAIL model, they determine the transmission behavior of radiation energy on different paths and affect the radiation characteristics of ground objects and the atmosphere in remote sensing observations. If accurate observation geometric parameters of the sun and the UAV sensor are provided to reduce the radiation error caused by changes in the sun angle, the accuracy and reliability of remote sensing inversion will be significantly improved.

[0003] Currently, the calculation of observation geometric parameters is mostly applied to satellites, and there is less research on the geometric parameters of UAVs. Some well-known satellite sensors, such as the Sentinel series of the European Space Agency and the Landsat series of the United States Geological Survey, provide corresponding sensor geometric parameter data, which are written into engineering files. Users can directly obtain these parameters for parameter input of radiation transfer models based on satellite sensors. However, these parameters are usually calculated by treating the satellite and the observed pixel as a whole and are based on the observation surface. A single image has only a set of geometric parameter information.

[0004] However, in the inversion of radiation transfer models based on UAV sensors, there is a lack of actual observation geometric parameters for each observed pixel. Existing methods usually fix the parameters as empirical values or obtain reference values from papers for input. Since there are differences between empirical values or reference values and the true values, errors will occur in the inversion results after being introduced into the model, which cannot meet the requirements of high-precision inversion in UAV remote sensing. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method and device for obtaining observation geometric parameters based on UAV sensor images, so as to solve the problem that in the inversion based on UAV sensors in the prior art, there are usually no actual observation geometric parameters and the parameter fineness is insufficient, resulting in low inversion accuracy.

[0006] According to the first aspect of the embodiments of the present invention, a method for obtaining observation geometric parameters based on UAV sensor images is provided, including: Obtain UAV sensor images, and take each grid pixel of the UAV sensor images as an individual observed pixel; Obtain the date and time data when the UAV sensor image is taken, the position data of the UAV, the flight altitude of the UAV, and the position data of each observation pixel. According to the date and time data and the central longitude and latitude data of each observation pixel position data, obtain the solar zenith angle and solar azimuth angle corresponding to each observation pixel. According to the UTM coordinates in the UAV position data, the flight altitude of the UAV, and the UTM coordinates in the position data of each observation pixel, calculate the UAV zenith angle corresponding to each observation pixel. According to the UTM coordinates in the UAV position data and the UTM coordinates in the position data of each observation pixel, calculate the UAV azimuth angle corresponding to each observation pixel. According to the solar azimuth angle and UAV azimuth angle corresponding to each observation pixel, calculate the relative azimuth angle corresponding to each observation pixel.

[0007] Preferably, according to the date and time data and the central longitude and latitude data of each observation pixel position data, obtaining the solar zenith angle and solar azimuth angle corresponding to each observation pixel includes: Convert the date and time data to UTC format, and convert the central longitude and latitude data to decimal degree data. Input the converted date and time data and central longitude and latitude data into the PVLIB library to obtain the solar zenith angle and solar azimuth angle corresponding to the observation pixel.

[0008] Preferably, for each observation pixel, calculate the UAV zenith angle corresponding to the observation pixel through the following formula:

[0009]

[0010] Among them, is the UAV altitude angle; is the transverse Mercator projection coordinate in the UAV position data; is the flight altitude of the UAV; is the transverse Mercator projection coordinate in the position data of the observation pixel; is the UAV zenith angle.

[0011] Preferably, for each observation pixel, calculate the UAV azimuth angle corresponding to the observation pixel through the following formula:

[0012] Among them, is the UAV azimuth angle; is the transverse Mercator projection coordinate in the UAV position data; It is the transverse Mercator projection coordinate in the position data of the observed pixel.

[0013] Preferably, the value range of the azimuth angle of the UAV is [0°, 360°); If , and the UAV is in the eastward direction of the observed pixel, then the value range of the azimuth angle of the UAV is (0°, 180°); If , and the UAV is in the westward direction of the observed pixel, then the value range of the azimuth angle of the UAV is (180°, 360°).

[0014] Preferably, for each observed pixel, the relative azimuth angle corresponding to the observed pixel is calculated according to the following formula:

[0015] Wherein, is the relative azimuth angle; is the solar azimuth angle; is the azimuth angle of the UAV.

[0016] Preferably, after obtaining the relative azimuth angle corresponding to the observed pixel, it further includes: Normalize the relative azimuth angle so that the relative azimuth angle is between 0° and 360°.

[0017] Preferably, the method further includes: Integrate the solar zenith angle, the solar azimuth angle, the UAV zenith angle, the UAV azimuth angle and the relative azimuth angle corresponding to each observed pixel into an observation geometry parameter set; Substitute the observation geometry parameter set into the pre-established relationship model between reflectivity and input parameters for inversion based on the UAV sensor.

[0018] According to the second aspect of the embodiments of the present invention, there is provided an observation geometry parameter acquisition device based on UAV sensor images, including: A main controller and a memory connected to the main controller; The memory stores program instructions therein; The main controller is used to execute the program instructions stored in the memory and execute the method described in any one of the above.

[0019] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects: It is understandable that the technical solution shown in the present invention can accurately calculate the observation geometric parameters of each observation pixel according to the data during the shooting of the UAV sensor image, including: solar zenith angle, solar azimuth angle, UAV zenith angle, UAV azimuth angle, and relative azimuth angle. The technical solution shown in the present invention has high calculation accuracy. When substituting it into the corresponding model for inversion, it can improve the result accuracy of UAV image inversion and provide technical support for the application of UAV remote sensing in crops.

[0020] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present invention and used together with the specification to explain the principles of the present invention.

[0022] Figure 1 It is a schematic diagram of the steps of a method for obtaining observation geometric parameters based on a UAV sensor image shown according to an exemplary embodiment.

[0023] Figure 2 It is a calculation flow chart of a method for obtaining observation geometric parameters based on a UAV sensor image shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Here, the exemplary embodiments will be described in detail, and the examples are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are only examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0025] In one embodiment, referring to Figure 1 , a method for obtaining observation geometric parameters based on a UAV sensor image is provided, including: Step S11: Obtain the UAV sensor image, and use each grid pixel of the UAV sensor image as an individual observation pixel; Step S12: Obtain the date and time data during the shooting of the UAV sensor image, the position data of the UAV, the flight altitude of the UAV, and the position data of each observation pixel; Step S13: Obtain the solar zenith angle and solar azimuth angle corresponding to each observation pixel according to the date and time data and the central longitude and latitude data of the position data of each observation pixel; Step S14: Calculate the zenith angle of the drone corresponding to each observed pixel based on the UTM coordinates in the drone position data, the flight altitude of the drone, and the UTM coordinates in the position data of each observed pixel. Step S15: Calculate the azimuth angle of the drone corresponding to each observed pixel based on the UTM coordinates in the drone position data and the UTM coordinates in the position data of each observed pixel. Step S16: Calculate the relative azimuth angle corresponding to each observed pixel based on the solar azimuth angle and the drone azimuth angle corresponding to each observed pixel.

[0026] It can be understood that the technical solution shown in the present invention can accurately calculate the observation geometric parameters of each observed pixel according to the data during the shooting of the drone sensor image, including: solar zenith angle, solar azimuth angle, drone zenith angle, drone azimuth angle, and relative azimuth angle. The technical solution shown in the present invention has high calculation accuracy. When substituting it into the corresponding model for inversion, it can improve the result accuracy of the drone image inversion, providing technical support for the application of drone remote sensing in crops.

[0027] In specific practice, Figure 2 is a calculation flowchart of a method for obtaining observation geometric parameters based on a drone sensor image shown according to an exemplary embodiment. Refer to Figure 2 , and this embodiment will be described.

[0028] First, in step S11 of this embodiment, each grid pixel of the drone sensor image needs to be used as an individual observed pixel. The grid pixels of the image generated by the drone sensor can represent the characteristics of the observed ground. There are multiple grid units in each image. Each grid pixel is used as an individual observed pixel, and each observed pixel has a corresponding solar zenith angle, solar azimuth angle, drone zenith angle, drone azimuth angle, and relative azimuth angle.

[0029] In step S12, obtain the date and time data when the drone sensor image is taken, the position data of the drone, the flight altitude of the drone, and the position data of each observed pixel. The obtained data is used for subsequent calculation and processing. The date and time data refers to the shooting date d and shooting time t of the drone sensor image; the position data of the drone includes the UTM coordinates (transverse Mercator projection coordinates) of the drone; the position data of each observed pixel includes the central longitude lon and central latitude lat of each observed pixel, and the UTM coordinates of each observed pixel.

[0030] In step S13, obtain the solar zenith angle and solar azimuth angle.

[0031] It should be noted that according to the date-time data and the central longitude and latitude data of each observation pixel position data, the solar zenith angle and solar azimuth angle corresponding to each observation pixel are obtained, including: Convert the date-time data to UTC (Coordinated Universal Time) format, and convert the central longitude and latitude data to decimal degree data; input the converted date-time data and central longitude and latitude data into the PVLIB library to obtain the solar zenith angle and solar azimuth angle.

[0032] The solar zenith angle describes the angle of the sun relative to the top of the head of the observation pixel. Specifically, the solar zenith angle is the angle from the zenith (the point vertically upward) at the top of the head of the observation pixel to the position of the sun. It is complementary to the solar elevation angle, and the sum of the two is 90 degrees.

[0033] The solar azimuth angle refers to the angle of the projection of the sun on the ground plane relative to the due north direction as seen from the position of the observation pixel. It is usually expressed in degrees and is calculated clockwise starting from the north.

[0034] The solar zenith angle and azimuth angle are functions of the shooting date d, shooting time t, central longitude lon, and central latitude lat, that is ; where is the solar zenith angle, is the solar azimuth angle. All these data can be read from the information of the image.

[0035] After obtaining the above data, input the converted shooting date d, shooting time t, central longitude lon, and central latitude lat into the pvlib.solarposition.get_solarposition() method of the PVLIB library to obtain the corresponding solar zenith angle and azimuth angle. The PVLIB library is a toolbox developed based on the Python language, providing a set of functions and classes for simulating the performance of photovoltaic energy systems and completing related tasks.

[0036] In step S14, the calculation of the UAV zenith angle is performed.

[0037] It should be noted that the UAV zenith angle describes the angle of the UAV relative to the top of the head of the observation pixel. The UAV zenith angle corresponding to the observation pixel is calculated by the following formula:

[0038]

[0039] where is the altitude angle of the UAV; is the transverse Mercator projection coordinate in the UAV position data; is the flight altitude of the UAV; is the transverse Mercator projection coordinate in the position data of the observed pixel; is the zenith angle of the UAV, which is complementary to the altitude angle of the UAV.

[0040] The azimuth angle of the UAV is calculated in step S15.

[0041] It should be noted that the UAV azimuth angle describes the angle of the projection of the UAV on the ground plane relative to the true north direction as seen from the position of the observed pixel.

[0042] In specific practice, its calculation formula is: =

[0043] where is the true north vector, parallel to the true north direction, denoted as (0, m), where m is any real number greater than 0; is the vector from the pixel position to the UAV position, denoted as (x 1 -x 2 , y 1 -y 2 ); is the UAV azimuth angle.

[0044] Substitute the value of into the above formula to obtain the final calculation formula for the UAV azimuth angle based on the pixel coordinates and the UAV coordinates. The UAV azimuth angle corresponding to the observed pixel is calculated through the following formula:

[0045] where, is the UAV azimuth angle; is the transverse Mercator projection coordinate in the UAV position data; is the transverse Mercator projection coordinate in the position data of the observed pixel.

[0046] It should be noted that the value range of the UAV azimuth angle is [0°, 360°).

[0047] If When the value is 1, it indicates that the azimuth angle of the UAV is 0°. At this time, the UAV is directly north of the observed pixel. When the value is -1, it means the azimuth angle of the UAV is 180°, and at this moment, the UAV is directly south of the observed pixel. When the value is between -1 and 1, there will be two values for the azimuth angle of the UAV, which are respectively in the ranges of 0° - 180° and 180° - 360°. At this time, it is necessary to determine which value is the correct azimuth angle of the UAV.

[0048] If , and the UAV is in the eastward direction of the observed pixel, then the value range of the azimuth angle of the UAV is (0°, 180°).

[0049] If , and the UAV is in the westward direction of the observed pixel, then the value range of the azimuth angle of the UAV is (180°, 360°).

[0050] In step S16, the relative azimuth angle is calculated. The relative azimuth angle is the azimuth angle of the sun relative to a certain reference direction. The reference direction can be any direction. Usually, in some applications, the reference direction is the orientation of the observer, the flight direction of the satellite, or a certain fixed direction. In this embodiment, the reference direction is the direction of the UAV at the observed pixel.

[0051] Therefore, the relative azimuth angle corresponding to the observed pixel is calculated according to the following formula:

[0052] Where is the relative azimuth angle; is the sun azimuth angle; is the azimuth angle of the UAV.

[0053] It should be noted that after obtaining the relative azimuth angle corresponding to the observed pixel, in order to ensure that the relative azimuth angle is between 0° and 360°, it also includes: performing normalization processing on the relative azimuth angle so that the relative azimuth angle is between 0° and 360°.

[0054] The normalization formula is as follows:

[0055] According to the above steps, the observation geometric parameters of each observed pixel can be accurately calculated finally.

[0056] In another embodiment, it should be noted that the method further includes: Integrate the solar zenith angle, the solar azimuth angle, the UAV zenith angle, the UAV azimuth angle, and the relative azimuth angle into an observation geometry parameter set; bring the observation geometry parameter set into the pre-established relationship model between the reflectivity and the input parameters for inversion based on the UAV sensor.

[0057] In specific practice, taking the ROSAIL radiative transfer model as an example, a relationship model trained by a simulated data set is established through this model. This simulated data set consists of input parameters and model spectra. Then, transfer this training knowledge to the actual UAV observation data, which includes spectral reflectivity and observation geometry parameters of the sensor, etc.

[0058] The PROSAIL model can be formally expressed as:

[0059] In the above formula, is the simulated canopy reflectivity, is the leaf structure parameter, Cab is the chlorophyll content, Cw is the leaf equivalent water thickness, Cm is the leaf dry matter content, LAI is the leaf area index, LAD is the average leaf inclination angle, is the observation zenith angle, is the solar zenith angle, is the relative azimuth angle between the sun and the observation.

[0060] These parameters are usually divided into three categories, namely, observation geometry parameters ( , , ), vegetation canopy parameters ( LAI , LAD , N ), and leaf biochemical parameters ( Cab , Cw , Cm ).

[0061] Vegetation canopy parameters and leaf biochemical parameters are crucial for crop yield prediction, crop growth simulation, and farmland management decision-making. Inverting vegetation canopy and leaf biochemical parameters based on the PROSAIL model is also one of the important applications in modern agricultural remote sensing.

[0062] If the vegetation LAI is inverted, it is necessary to input the ranges and step sizes of the above parameters through the PROSAIL model to obtain the simulated canopy reflectance. Then, a relationship model between the input parameters and the simulated canopy reflectance is established through machine learning methods. Finally, the LAI is predicted and inverted based on the actual observation data obtained by the sensor and the established relationship model. The same applies to the inversion of other vegetation canopy and leaf biochemical parameters.

[0063] If the LAI corresponding to each pixel is inverted from the UAV image, it is necessary to input the parameters in the parameter model except for LAI into the established relationship model for prediction and inversion to obtain the result.

[0064] However, when inputting the observation geometric parameters into the radiative transfer model for inversion based on the UAV sensor, the observation geometric parameters are usually fixed as empirical values or reference values obtained from papers. Whether the parameters are set as empirical values or reference values obtained from papers, they all differ from the true values. Bringing these non-true values into the model will definitely result in errors in the model. In this embodiment, aiming at the problem that the observation geometric parameters are difficult to accurately measure, resulting in errors in the model inversion results, by calculating the true observation geometric parameters of each pixel and substituting them into the relationship model established between the reflectance and the input parameters, the empirical values or reference values are replaced with the truly calculated observation values, calibrating the model parameters, improving the fineness and accuracy of the model geometric parameters, and thus enhancing the accuracy of remote sensing inversion.

[0065] In another embodiment, an observation geometric parameter acquisition device based on UAV sensor images is provided, including: A main controller, and a memory connected to the main controller; The memory, in which program instructions are stored; The main controller is used to execute the program instructions stored in the memory and execute the method described in any one of the above.

[0066] It can be understood that the same or similar parts in the above embodiments can be referred to each other, and the content not detailed in some embodiments can be seen in the same or similar content of other embodiments.

[0067] It should be noted that in the description of the present invention, terms such as "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0068] Any process or method description depicted in a flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a specific logical function or process. The scope of the preferred embodiments of the present invention includes additional implementations where functions may be performed not in the order shown or discussed, including in a substantially simultaneous manner or in the reverse order according to the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0069] It should be understood that various parts of the present invention can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0070] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program. The said program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0071] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0072] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disk, etc.

[0073] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0074] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for obtaining observation geometric parameters based on unmanned aerial vehicle sensor images, characterized in that: include: Acquire the UAV sensor image, and use each raster pixel of the UAV sensor image as a separate observation pixel; Obtain the date and time data when the drone sensor image was taken, the drone's location data, the drone's flight altitude, and the location data of each observed pixel; According to the date and time data and the central longitude and latitude data of each observed pixel position data, the solar zenith angle and solar azimuth angle corresponding to each observed pixel are obtained; The zenith angle of the drone corresponding to each observation pixel is calculated based on the UTM coordinates in the drone position data, the flight altitude of the drone, and the UTM coordinates in the position data of each observation pixel; According to the UTM coordinates in the UAV position data and the UTM coordinates in the position data of each observation pixel, the UAV azimuth corresponding to each observation pixel is calculated; According to the solar azimuth and UAV azimuth corresponding to each observation pixel, the relative azimuth corresponding to each observation pixel is calculated.

2. The method according to claim 1, characterized in that: According to the date and time data and the central longitude and latitude data of each observed pixel position data, the solar zenith angle and solar azimuth angle corresponding to each observed pixel are obtained, including: Convert the date and time data into UTC format, and convert the center longitude and latitude data into decimal degree data; The converted date and time data and central longitude and latitude data are input into the PVLIB library to obtain the solar zenith angle and solar azimuth angle corresponding to the observed pixel.

3. The method according to claim 1, characterized in that For each observation pixel, the drone zenith angle corresponding to the observation pixel is calculated by the following formula: in, is the altitude angle of the drone; is the Transverse Mercator projection coordinate in the drone position data; is the flight altitude of the drone; is the transverse Mercator projection coordinate of the observed pixel location data; is the drone’s zenith angle.

4. The method according to claim 1, characterized in that: For each observation pixel, the drone azimuth corresponding to the observation pixel is calculated by the following formula: in, is the azimuth of the UAV; is the Transverse Mercator projection coordinate in the drone position data; It is the transverse Mercator projection coordinate of the observed pixel location data.

5. The method according to claim 4, characterized in that Also includes: The value range of the drone azimuth is [0°, 360°); like , and, when the UAV is in the east direction of the observed pixel, the value range of the UAV azimuth is (0°, 180°); like , and when the UAV is in the west direction of the observed pixel, the value range of the UAV azimuth is (180°, 360°).

6. The method according to claim 1, characterized in that For each observation pixel, the relative azimuth corresponding to the observation pixel is calculated according to the following formula: in, is the relative azimuth; is the solar azimuth; is the azimuth of the drone.

7. The method according to claim 6, characterized in that After obtaining the relative azimuth corresponding to the observed pixel, it also includes: The relative azimuth angle is normalized so that the relative azimuth angle is between 0° and 360°.

8. The method according to claim 1, characterized in that Also includes: Integrate the solar zenith angle, the solar azimuth angle, the drone zenith angle, the drone azimuth angle and the relative azimuth angle corresponding to each observation pixel into an observation geometry parameter set; The observed geometric parameter set is introduced into a pre-established relationship model between reflectivity and input parameters to perform an inversion based on UAV sensors.

9. An observation geometric parameter acquisition device based on unmanned aerial vehicle sensor images, characterized in that: include: A main controller, and a memory connected to the main controller; The memory stores program instructions; The main controller is used to execute program instructions stored in the memory and execute the method according to any one of claims 1 to 8.

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