CN112629666B - Method suitable for constructing surface temperature daily transformation model of thermal infrared unmanned aerial vehicle - Google Patents
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Abstract
The invention discloses a method for constructing a surface temperature daily transformation model of a thermal infrared unmanned aerial vehicle, and relates to the technical field of thermal infrared of unmanned aerial vehicles; the method comprises the steps of firstly measuring the temperature of a ground surface point, then constructing a temperature change model of the measured temperature, acquiring thermal infrared image data through an unmanned aerial vehicle, carrying out ground surface temperature inversion on the acquired thermal infrared image data, carrying out temperature inversion on a daily change model of the ground surface temperature based on the unmanned aerial vehicle, and finally evaluating the model precision.
Description
Technical Field
The invention relates to the technical field of thermal infrared of unmanned aerial vehicles, in particular to a method suitable for constructing a surface temperature daily transformation model of a thermal infrared unmanned aerial vehicle.
Background
The surface temperature is taken as a key parameter of a plurality of basic subjects and application fields, and plays an important role in a plurality of fields such as coal fire monitoring, climate change, agriculture, urban heat islands, natural disasters and the like.
The existing methods for acquiring the surface temperature mainly comprise three main types: the method comprises the steps of obtaining ground temperature measurement means, unmanned aerial vehicle temperature measurement and satellite remote sensing temperature. The ground measurement means mainly comprises a temperature measurement gun, a temperature sensor and the like, the temperature measurement mode mainly adopts point temperature measurement, planar temperature data cannot be obtained, and the application range of the device is greatly limited. The satellite remote sensing can acquire large-range earth surface temperature data in a short time, but the spatial resolution is low, at present, the resolution of data of a static satellite is in the kilometer level, the same area can be shot for several times in one day, and the data can be used for researching the daily change rule of the earth surface temperature. The thermal infrared technology of the unmanned aerial vehicle is high in resolution ratio, the same region can be observed repeatedly in a short time, and a new technical support is provided for the related research of the earth surface temperature.
The unmanned aerial vehicle thermal infrared technology becomes an important means for acquiring the surface temperature, and is widely applied to various fields such as coal fire monitoring, precision agriculture, city monitoring, engineering measurement, disaster monitoring, underground water measurement, biological monitoring and the like. However, the unmanned aerial vehicle thermal infrared technology has a limited collection range, and the whole temperature monitoring result in the research area is obtained by collecting and splicing for multiple times during large-range temperature monitoring. The surface temperature changes rapidly, so that the problem of temperature color difference exists in large-range monitoring, the overall evaluation of the area temperature is difficult, and the application of the area temperature is limited to a certain extent. At present, the change situation of the earth surface temperature along with the time in one day is simulated by adopting an earth surface temperature daily change model, and the temperature is subjected to time normalization, so that the problems can be better solved.
The earth surface temperature daily change model is established based on a heat conduction equation or an energy balance equation and mainly comprises four main categories: pure physical models, semi-empirical models, and statistical models.
The mathematical expressions finally presented by different models are basically the same and all contain 5-6 free parameters, which means that 5-6 surface temperature data are needed to solve the parameters to describe the change of the surface temperature along with the time, however, 5-6 times of repeated monitoring in one day in the same area consumes a great deal of manpower, material resources and financial resources. This makes the model have great limitation in practical application. How to obtain a model with continuous temperature change and less repeated observation times becomes a big problem. Therefore, an unmanned aerial vehicle thermal infrared daily transformation model building method with low cost, low time consumption and high efficiency is urgently needed.
Disclosure of Invention
The invention aims to solve the technical problems that the temperature measurement range is not wide and the temperature measurement area is limited in the background technology, and provides a method for constructing a surface temperature daily transformation model of a thermal infrared unmanned aerial vehicle.
The invention adopts the following technical scheme for solving the technical problems:
the method for constructing the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle is characterized by comprising the following steps of:
step 2, simulating temperature data of a ground temperature measuring point by using a daily variation model;
step 4, inverting the earth surface temperature of the collected thermal infrared image data;
step 5, performing temperature inversion on the earth surface temperature daily change model based on the unmanned aerial vehicle;
and 6, evaluating the precision of the model.
As a further preferable scheme of the construction method of the daily earth surface temperature conversion model suitable for the thermal infrared unmanned aerial vehicle, the earth surface temperature is measured in the step 1, an earth surface temperature measuring instrument is adopted and fixed to a certain ground temperature measuring point, sampling intervals are set as required, the sampling intervals are 30min, the temperature measuring points are ensured to be under sunlight irradiation all day long, and meanwhile, inaccurate temperature measurement caused by artificial heat source influence is avoided.
As a further preferable scheme of the method for constructing the thermal infrared unmanned aerial vehicle surface temperature daily variation model, in the step 2, the daily variation model is used for simulating the temperature data of the ground temperature measuring point, and the GOT01 model is used for solving the parameters:
in the formula, ToIs the residual temperature; t isaIs the temperature amplitude; omega is the day length of the day and can be inquired through a meteorological network; t is tmThe time of maximum temperature; t is tsIs the time of temperature decay; delta T temperature difference, ToAnd T (T → ∞); k is the attenuation coefficient.
After simulation, a daily temperature change model of the ground temperature measuring point can be obtained, and the model parameters obtained by fitting are only applicable to the point and cannot be utilized in the temperature image data of the unmanned aerial vehicle.
Because the weather conditions in the same area are the same, the simulated meteorological parameters are the same, namely the maximum temperature moment tmAnd the time t of temperature decaymApplicable in whole unmanned aerial vehicle image data.
As a further preferable scheme of the construction method of the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle, in the step 3, the unmanned aerial vehicle is used for collecting infrared image data, the adopted unmanned aerial vehicle model is a DJI M210V2 unmanned aerial vehicle, and a Zenmuse XT2 dual-photo thermal infrared lens is carried, and the specific steps are as follows:
after the thermal infrared unmanned aerial vehicle camera is assembled, selecting course overlapping degree and side direction overlapping degree to be 90% according to the condition of a research area, setting an acquisition range, planning a route, setting acquisition parameters, and starting acquisition operation after the unmanned aerial vehicle and the camera are tested to be intact on the ground;
meanwhile, heat absorbing materials such as rectangular aluminum foils, rectangular black foams and the like can be arranged in a test area according to actual conditions, and the central coordinate of the heat absorbing materials is measured by using a GPS RTK technology to serve as a ground control point;
according to the invention, the earth surface temperature day change model can be obtained only by shooting the same area for 2-3 times, the GOT01 model is adopted for example display, the day temperature change model can be obtained only by shooting two scenes of day temperature images, the whole day temperature change model can be obtained by adding one night temperature image, the images are shot for 3 times of day time in the same area, two scenes are used for obtaining the day temperature change model, and the other scene is used for verifying the model precision condition.
As a further preferable scheme of the method for constructing the daily earth surface temperature transform model of the thermal infrared unmanned aerial vehicle, in the step 4, the acquired earth surface temperature of the thermal infrared image data is inverted, and the specific steps are as follows:
firstly, preprocessing collected thermal infrared data, covering and deducting to remove fuzzy images, eliminating non-airline images, converting image formats and the like;
and carrying out radiometric calibration on the temperature image. The DN value in the temperature image has a certain difference from the real surface temperature, and the corresponding conversion is needed according to different camera parameters, and the mathematical relationship is as follows:
Ts＝mDN+n
in the formula, DN represents DN value of thermal infrared image pixel; m and n represent fitting coefficients, respectively. In the example, the instrument parameters are that m is 0.04 and n is-273;
importing the unmanned aerial vehicle image data after radiometric calibration and ground control point data into unmanned aerial vehicle aerial data processing special software such as PIX4D, 3C and photoscan, and searching homonymous image points for a stereopair based on a visual stereo dense matching algorithm and a motion recovery structure algorithm; using automatic aerial triangulation and block adjustment algorithm to adjust the exact x (north), y (east), h (elevation) of all images,
(course inclination angle), omega (sidewise inclination angle) and k (picture rotation angle), the process can simultaneously complete the work of calibrating the distortion parameters of the cameraMaking; and then generating three-dimensional point clouds for all pixels of all images by using an image cross-correlation method, generating a Digital Surface Model (DSM) by using an irregular triangulation network method, and generating a temperature orthophoto map of a research area by using a digital orthorectification technology and a reverse texture mapping mode by using the DSM and all oriented images.
As a further preferable scheme of the method for constructing the thermal infrared unmanned aerial vehicle earth surface temperature daily change model, in the step 5, temperature inversion is performed on the earth surface temperature daily change model based on the unmanned aerial vehicle, and the specific steps are as follows:
the generated temperature orthographic projection image of the research area is combined with a temperature inversion model to solve the temperature amplitude T of each pixelaThe value and the temperature difference δ T;
in the formula, TnIs the nth scene temperature image; t is tnThe imaging time of the nth temperature image is obtained. To ensure and t1，t2Less than, tsThat is, the first two images are daytime images and t3Greater than tsNamely, the night temperature image;
here, it can be found that the residual temperature T of each pixel0It is not solved for because of the residual temperature T0Is a constant that is eliminated when the temperature at any time is found based on a scene temperature image. The final model is as follows:
in the formula, TtThe temperature image at the obtained moment, t is time. So far, the thermal infrared unmanned aerial vehicle temperature image model is successfully established, and the thermal infrared unmanned aerial vehicle temperature image model can be utilized based on the modelAnd (3) carrying out regional one-scene temperature image, inverting the temperature image at any time, and researching the daily change of the regional temperature.
As a further preferable scheme of the construction method of the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle, in the step 6, the precision of the model is evaluated, and the specific steps are as follows:
collecting three scenes of daytime temperature images, wherein two scenes are used for establishing a daytime temperature change model, and the remaining scene is used for evaluating the precision of the model;
and (4) simulating by using a final model to obtain a temperature image, performing difference between the temperature image and the real temperature image, and simultaneously calculating an absolute value to obtain a temperature absolute value difference image, and performing precision evaluation to obtain a final result.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
1. the invention can obtain the model with continuous temperature change and less repeated observation times;
2. the method adopts a daily change model of the earth surface temperature to simulate the change situation of the earth surface temperature along with the time in one day, and carries out time normalization on the temperature;
3. the invention discloses a thermal infrared daily change model construction method.
Drawings
FIG. 1 is an exemplary flow chart of a method for periodic daily variation simulation of surface temperature in accordance with the present invention;
FIG. 2 is a simulation graph of the periodic daily variation of the surface temperature of a certain observation area under clear sky conditions according to the present invention;
FIG. 3 is a visible light image of an analysis area according to an embodiment of the present invention;
fig. 4 shows the surface temperature products measured by the unmanned aerial vehicle at 11:20, 7, 8 and 2020;
fig. 5 shows the surface temperature products measured by the unmanned aerial vehicle at 16:30 days 7, 8 and 2020;
fig. 6 shows the surface temperature products measured by the unmanned aerial vehicle at 7/8/17: 30 in 2020;
FIG. 7 shows the data obtained by inversion according to the method of the present invention in the afternoon 17 of 7/8/2020: 30 temperature images;
fig. 8 is an absolute difference image of the model inversion image and the real image.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a method for constructing a surface temperature daily transformation model of a thermal infrared unmanned aerial vehicle is characterized by comprising the following steps:
step 2, simulating temperature data of a ground temperature measuring point by using a daily variation model;
step 4, inverting the earth surface temperature of the collected thermal infrared image data;
step 5, performing temperature inversion on the earth surface temperature daily change model based on the unmanned aerial vehicle;
and 6, evaluating the precision of the model.
As a further preferable scheme of the construction method of the daily earth surface temperature conversion model suitable for the thermal infrared unmanned aerial vehicle, the earth surface temperature is measured in the step 1, an earth surface temperature measuring instrument is adopted and fixed to a certain ground temperature measuring point, sampling intervals are set as required, the sampling intervals are 30min, the temperature measuring points are ensured to be under sunlight irradiation all day long, and meanwhile, inaccurate temperature measurement caused by artificial heat source influence is avoided.
As a further preferable scheme of the method for constructing the thermal infrared unmanned aerial vehicle surface temperature daily variation model, in the step 2, the daily variation model is used for simulating the temperature data of the ground temperature measuring point, and the GOT01 model is used for solving the parameters:
in the formula, ToIs the residual temperature; t isaIs the temperature amplitude; omega is the day length of the day and can be inquired through a meteorological network; t is tmThe time of maximum temperature; t is tsIs the time of temperature decay; delta T temperature difference, ToAnd T (T → ∞); k is the attenuation coefficient.
After simulation, a daily temperature change model of the ground temperature measuring point can be obtained, and the model parameters obtained by fitting are only applicable to the point and cannot be utilized in the temperature image data of the unmanned aerial vehicle.
Because the weather conditions in the same area are the same, the simulated meteorological parameters are the same, namely the maximum temperature moment tmAnd the time t of temperature decaymApplicable in whole unmanned aerial vehicle image data.
As a further preferable scheme of the construction method of the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle, in the step 3, the unmanned aerial vehicle is used for collecting infrared image data, the adopted unmanned aerial vehicle model is a DJI M210V2 unmanned aerial vehicle, and a Zenmuse XT2 dual-photo thermal infrared lens is carried, and the specific steps are as follows:
after the thermal infrared unmanned aerial vehicle camera is assembled, selecting course overlapping degree and side direction overlapping degree to be 90% according to the condition of a research area, setting an acquisition range, planning a route, setting acquisition parameters, and starting acquisition operation after the unmanned aerial vehicle and the camera are tested to be intact on the ground;
meanwhile, heat absorbing materials such as rectangular aluminum foils, rectangular black foams and the like can be arranged in a test area according to actual conditions, and the central coordinate of the heat absorbing materials is measured by using a GPS RTK technology to serve as a ground control point;
according to the invention, the earth surface temperature day change model can be obtained only by shooting the same area for 2-3 times, the GOT01 model is adopted for example display, the day temperature change model can be obtained only by shooting two scenes of day temperature images, the whole day temperature change model can be obtained by adding one night temperature image, the images are shot for 3 times of day time in the same area, two scenes are used for obtaining the day temperature change model, and the other scene is used for verifying the model precision condition.
As a further preferable scheme of the method for constructing the daily earth surface temperature transform model of the thermal infrared unmanned aerial vehicle, in the step 4, the acquired earth surface temperature of the thermal infrared image data is inverted, and the specific steps are as follows:
firstly, preprocessing collected thermal infrared data, covering and deducting to remove fuzzy images, eliminating non-airline images, converting image formats and the like;
and carrying out radiometric calibration on the temperature image. The DN value in the temperature image has a certain difference from the real surface temperature, and the corresponding conversion is needed according to different camera parameters, and the mathematical relationship is as follows:
Ts＝mDN+n
in the formula, DN represents DN value of thermal infrared image pixel; m and n represent fitting coefficients, respectively. In the example, the instrument parameters are that m is 0.04 and n is-273;
importing the unmanned aerial vehicle image data after radiometric calibration and ground control point data into unmanned aerial vehicle aerial data processing special software such as PIX4D, 3C and photoscan, and searching homonymous image points for a stereopair based on a visual stereo dense matching algorithm and a motion recovery structure algorithm; using automatic aerial triangulation and block adjustment algorithm to adjust the exact x (north), y (east), h (elevation) of all images,
(course inclination angle), omega (sidewise inclination angle) and k (picture rotation angle), the process can simultaneously complete the work of calibrating the distortion parameters of the camera; then makeAnd generating three-dimensional point clouds for all pixels of all images by using an image cross-correlation method, generating a Digital Surface Model (DSM) by using an irregular triangulation network method, and generating a temperature orthophoto map of a research area by using a digital orthorectification technology and a reverse texture mapping mode by using the DSM and all oriented images.
As a further preferable scheme of the method for constructing the thermal infrared unmanned aerial vehicle earth surface temperature daily change model, in the step 5, temperature inversion is performed on the earth surface temperature daily change model based on the unmanned aerial vehicle, and the specific steps are as follows:
the generated temperature orthographic projection image of the research area is combined with a temperature inversion model to solve the temperature amplitude T of each pixelaThe value and the temperature difference δ T;
in the formula, TnIs the nth scene temperature image; t is tnThe imaging time of the nth temperature image is obtained. To ensure and t1，t2Less than, tsThat is, the first two images are daytime images and t3Greater than tsNamely, the night temperature image;
here, it can be found that the residual temperature T of each pixel0It is not solved for because of the residual temperature T0Is a constant that is eliminated when the temperature at any time is found based on a scene temperature image. The final model is as follows:
in the formula, TtThe temperature image at the obtained moment, t is time. So far, based on the success of establishing a thermal infrared unmanned aerial vehicle temperature image model, the regional scene temperature can be utilized based on the modelAnd (5) measuring images, inverting temperature images at any time, and researching the daily change of the temperature of the area.
As a further preferable scheme of the construction method of the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle, in the step 6, the precision of the model is evaluated, and the specific steps are as follows:
collecting three scenes of daytime temperature images, wherein two scenes are used for establishing a daytime temperature change model, and the remaining scene is used for evaluating the precision of the model;
and (4) simulating by using the final model to obtain a temperature image, and calculating an absolute value while making a difference between the temperature image and the real temperature image to obtain a final result.
Examples of the embodiments
In this example, three scenes of daytime temperature images are collected, two scenes are used for establishing a daytime temperature change model, and the remaining scene is used for evaluating the precision of the model.
The final model simulation is used to obtain 17 pm 7/8/2020: the 30-temperature image is shown in fig. 4, and the absolute value is obtained by subtracting the 30-temperature image from the real temperature image, and the result is shown in fig. 5.
Δabs＝|Tt-Treal|
ΔabsIs the absolute value difference of temperature, TrealIs the real temperature image at the time t.
The simulated image and the real image have the average temperature difference of only 1.8 ℃ and the standard deviation of 1.4 ℃, which shows the reliability of the invention. On the other hand, the difference between the two is found to be 24.1 ℃ at most, but further analysis can find that 95% of pixels have a difference below 4 ℃, and pixels with larger differences are distributed in pixels with shadows, such as tree shadows and step shadows, and the pixels are not in direct sunlight and are not in accordance with the application conditions of the invention (in a direct sunlight area with clear days and no obvious change in wind speed).
Meanwhile, the difference processing of the temperatures of the analog image and the real image is carried out, and the average value is 0.03 ℃, the temperature is almost 0 ℃, the standard deviation is 2.3 ℃, 46.6 percent of pixels are negative values, 53.4 percent of pixels are positive values, the half-percentage of the positive values and the half-percentage of the negative values are approximately the same, and the half-percentage of the positive values and the half-percentage of the negative values are approximately in accordance with the normal distribution.
The simulation precision is calculated according to the following formula, wherein N represents the number of pixels in the region, and the model precision sigma is 2.3146 ℃.
If the difference abnormal area is removed, the elimination rule is as follows:
Δabs＞mean(Δabs)+2*std
in the formula, mean (. DELTA.)abs) Represents the average of the absolute temperature differences, i.e. 1.8 ℃; std represents the standard deviation of the absolute temperature difference, i.e., 1.4 ℃. 2460 abnormal values are removed in total, the percentage is 4.49%, according to the graph 6, most of removed areas are located in shadow areas, and the accuracy of the model is recalculated to 1.9961 ℃ after removal, which indicates that the method has better accuracy. In conclusion, the invention can obtain the day-to-day change model of the earth surface temperature with higher precision only by acquiring 2-3 views of the earth surface temperature image of the unmanned aerial vehicle in the same area.
The points to be finally explained are: first, in the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounted," "connected," and "connected" should be understood broadly, and may be a mechanical connection or an electrical connection, or a communication between two elements, and may be a direct connection, and "upper," "lower," "left," and "right" are only used to indicate a relative positional relationship, and when the absolute position of the object to be described is changed, the relative positional relationship may be changed;
secondly, the method comprises the following steps: in the drawings of the disclosed embodiments of the invention, only the structures related to the disclosed embodiments are referred to, other structures can refer to common designs, and the same embodiment and different embodiments of the invention can be combined with each other without conflict;
and finally: the above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included in the scope of the present invention.
Claims (6)
1. The method for constructing the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle is characterized by comprising the following steps of:
step 1, measuring the temperature of a ground surface point;
step 2, simulating temperature data of a ground temperature measuring point by using a daily variation model;
step 3, collecting infrared image data by using an unmanned aerial vehicle;
step 4, inverting the earth surface temperature of the collected thermal infrared image data;
step 5, performing temperature inversion on the earth surface temperature daily change model based on the unmanned aerial vehicle;
step 6, evaluating the precision of the model;
and 2, simulating the temperature data of the ground temperature measuring point by using a daily variation model, and solving parameters by using a GOT01 model:
in the formula, T0Is the residual temperature; t isaIs the temperature amplitude; omega is the day length of the day and can be inquired through a meteorological network; t is tmThe time of maximum temperature; t is tsIs the time of temperature decay; delta T is the temperature difference, T0And T (T → ∞); k is an attenuation coefficient; t is time;
after simulation, a daily temperature change model of a ground temperature measuring point can be obtained, and the model parameters obtained by fitting are only applicable to the point and cannot be utilized in the temperature image data of the unmanned aerial vehicle;
because the weather conditions in the same area are the same, the simulated meteorological parameters are the same, namely the maximum temperature moment tmAnd the time t of temperature decaysApplicable in whole unmanned aerial vehicle image data.
2. The method for constructing the daily earth surface temperature transformation model of the thermal infrared unmanned aerial vehicle according to claim 1, wherein in the step 1, the earth surface temperature is measured, an earth surface temperature measuring instrument is adopted and fixed to a certain ground temperature measuring point, a sampling interval is set as required, the sampling interval is 30min, the temperature measuring point is ensured to be under the irradiation of sunlight all day long, and meanwhile, inaccurate temperature measurement caused by artificial heat source influence is avoided.
3. The method for constructing the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle as claimed in claim 1, wherein in the step 3, the unmanned aerial vehicle is used for collecting infrared image data, the model of the unmanned aerial vehicle is DJIM210V2 unmanned aerial vehicle, and a Zenmush XT2 dual-photo thermal infrared lens is carried, and the method comprises the following specific steps:
after the thermal infrared unmanned aerial vehicle camera is assembled, selecting course overlapping degree and side direction overlapping degree to be 90% according to the condition of a research area, setting an acquisition range, planning a route, setting acquisition parameters, and starting acquisition operation after the unmanned aerial vehicle and the camera are tested to be intact on the ground;
meanwhile, heat absorbing materials can be arranged in a test area according to actual conditions, the heat absorbing materials comprise rectangular aluminum foils and rectangular black foams, and the central coordinates of the heat absorbing materials are measured by a GPS RTK technology to serve as ground control points;
the earth surface temperature day change model can be obtained only by shooting the same area for 2-3 times, the GOT01 model is adopted for example display, the day temperature change model can be obtained only by two scenes of day temperature images, the night temperature image is added, the all-day temperature change model can be obtained, the day temperature change model is shot for 3 times in the day for the same area, the two scenes are used for obtaining the day temperature change model, and the other scene is used for verifying the precision condition of the model.
4. The method for constructing the daily earth surface temperature transform model of the thermal infrared unmanned aerial vehicle according to claim 1, wherein in the step 4, the earth surface temperature of the collected thermal infrared image data is inverted, and the method comprises the following specific steps:
firstly, preprocessing collected thermal infrared data, including removing fuzzy images, eliminating non-airline images and converting image formats;
performing radiometric calibration on the temperature image; the DN value in the temperature image has a certain difference with the real earth surface temperature, and the corresponding conversion is needed according to different camera parameters, and the mathematical relationship is as follows:
Ts＝mDN+n
in the formula, DN represents DN value of thermal infrared image pixel; m and n respectively represent fitting coefficients; the adopted instrument parameters are that m is 0.04 and n is-273;
importing the unmanned aerial vehicle image data after radiometric calibration and ground control point data into unmanned aerial vehicle aerial data processing special software, wherein the special software comprises PIX4D, 3C and photoscan, and searching homonymous image points for a stereopair based on a stereo dense matching algorithm and a motion recovery structure algorithm; the precise north coordinate x, east coordinate y, elevation h, course inclination angle phi, side direction inclination angle omega and image rotation angle k of all images are rectified by using automatic aerial triangulation and a block adjustment algorithm, and the process can simultaneously complete the work of calibrating the distortion parameters of the camera; and then generating three-dimensional point clouds for all pixels of all images by using an image cross-correlation method, generating a digital surface model DSM by using an irregular triangulation network method, and generating a temperature orthophoto map of the research area by using a digital orthorectification technology and a reverse texture mapping mode together with all oriented images by using the digital surface model DSM.
5. The method for constructing the thermal infrared unmanned aerial vehicle earth surface temperature daily change model according to claim 4, wherein in the step 5, temperature inversion is performed on the unmanned aerial vehicle earth surface temperature daily change model, and the specific steps are as follows:
the generated temperature orthographic projection image of the research area is combined with a temperature inversion model to solve the temperature amplitude T of each pixelaThe value and the temperature difference δ T;
in the formula, TnIs the nth scene temperature image; t is tnThe shooting time of the nth scene temperature image is taken; t1, when t2 is less than ts, the two foreground images are daytime images, and t3 is greater than ts, the nighttime temperature image is obtained;
here, it can be found that the residual temperature T of each pixel0It is not solved for because of the residual temperature T0Is a constant which is eliminated when the temperature at any time is found based on a scene temperature image; the final model is as follows:
in the formula, TtThe temperature image at the required moment; so far, the thermal infrared unmanned aerial vehicle temperature image model is successfully established, one scene temperature image in the area can be utilized based on the thermal infrared unmanned aerial vehicle temperature image model, the temperature image map at any moment can be inverted, and the daily change of the area temperature can be researched.
6. The method for constructing the surface temperature daily transformation model of the thermal infrared unmanned aerial vehicle according to claim 1, wherein in the step 6, the model is subjected to precision evaluation, and the method comprises the following specific steps:
collecting three scenes of daytime temperature images, wherein two scenes are used for establishing a daytime temperature change model, and the remaining scene is used for evaluating the precision of the model;
and (4) simulating by using a final model to obtain a temperature image, performing difference between the temperature image and the real temperature image, and simultaneously calculating an absolute value to obtain a temperature absolute value difference image, and performing precision evaluation to obtain a final result.
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