Method and apparatus for generating predetermined format atmospheric transmission data with sensor height variations

Abstract

This invention relates to a method and apparatus for generating atmospheric transport data in a predetermined format based on sensor altitude changes. The method includes: determining first atmospheric transport characteristic data calculated by the sensor under first atmospheric transport observation geometric parameters; inputting a first detection distance from the first atmospheric transport observation geometric parameters into a transformation model to obtain second atmospheric transport observation geometric parameters; and generating a two-dimensional distribution map of first two-dimensional atmospheric transport data in a predetermined format based on the second atmospheric transport observation geometric parameters and the first atmospheric transport characteristic data. The technical solution of this application, by employing a transformation model, can perform a nonlinear transformation on the first detection distance that is pre-sensitized and then saturated, which is beneficial for achieving compatibility of atmospheric transport data formats before and after sensor altitude changes.

Description

Technical Field

[0001] This invention relates to the field of atmospheric transport characteristic data technology, and in particular to a method and apparatus for generating atmospheric transport data in a predetermined format based on sensor altitude changes. Background Technology

[0002] In related technologies, when the height of the target point remains constant, the detection range of atmospheric transport measurements is mainly affected by the sensor height. When the observation angle is close to horizontal, even a small change in sensor height can result in a significant change in the corresponding detection range. Drastic changes in the detector can lead to incompatibility in the data format of atmospheric transmission characteristics before and after the sensor's altitude changes. Summary of the Invention

[0003] The technical problem to be solved by the present invention is that the atmospheric transmission data format is incompatible before and after the change due to the indirect effect of the sensor's altitude change. In view of the defects in the prior art, the present invention provides a method and apparatus for generating atmospheric transmission data in a predetermined format based on sensor altitude change.

[0004] In a first aspect, this application proposes a method for generating atmospheric transport data in a predetermined format based on sensor altitude changes, comprising: Determine the first atmospheric transport characteristic data calculated by the sensor under the observed geometric parameters of the first atmospheric transport; The observation geometric parameters of the first atmospheric transmission include: observation zenith angle and first detection distance; The first detection distance from the first atmospheric transmission observation geometry parameters is input into the transformation model to obtain the second atmospheric transmission observation geometry parameters. The transformation model is used to achieve a nonlinear transformation of pre-sensitization followed by saturation. Based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data, a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated. The dimensions of the first two-dimensional atmospheric transport data two-dimensional distribution map include at least the second detection distance.

[0005] Secondly, this application proposes a device for background atmospheric transport, comprising: The first determining module is used to determine the first atmospheric transmission characteristic data calculated by the sensor under the observation geometric parameters of the first atmospheric transmission; The observation geometric parameters of the first atmospheric transmission include: observation zenith angle and first detection distance. The conversion module is used to input the first detection distance from the first atmospheric transmission observation geometric parameters into the transformation model to obtain the second atmospheric transmission observation geometric parameters. The transformation model is used to achieve a nonlinear transformation of pre-sensitization followed by saturation. The observation geometry parameters for the second atmospheric transport include: the second detection range; The image module is used to generate a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data. The dimensions of the first two-dimensional atmospheric transport data two-dimensional distribution map include at least the second detection distance.

[0006] Thirdly, this application proposes an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements a method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes as described in any of the preceding claims.

[0007] Fourthly, this application proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the steps of the method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes as described in any of the preceding claims.

[0008] Implementing this invention has the following beneficial effects: Because a transformation model is used to achieve a nonlinear transformation with pre-sensitization followed by saturation, a nonlinear transformation with pre-sensitization followed by saturation can be performed on the first detection distance. The advantage of this transformation is that when the value of the first detection distance is relatively large, the value of the first detection distance becomes relatively small after the transformation. In particular, when the value changes, the change value of the first detection distance after the transformation tends to saturate, which is conducive to achieving compatibility of atmospheric transmission characteristic data format before and after sensor altitude changes. Attached Figure Description

[0009] Figure 1 This is a flowchart of a method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes, provided by an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the change in detection distance before and after a change in sensor height, provided by an embodiment of the present invention. Figure 3 This is a flowchart of atmospheric transport characteristic data processing provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of a device for generating atmospheric transmission data in a predetermined format based on sensor altitude changes, provided in an embodiment of the present invention. Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0010] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] Current infrared scene simulation systems have high real-time requirements for infrared image simulation capabilities (no less than 50Hz), and typically employ GPU-based graphics rendering engines (OSG, UE, etc.) to achieve high-performance infrared image simulation. Due to limitations in data transfer bandwidth between the CPU and GPU, texture mapping techniques are often used to transfer datasets involved in the simulation process.

[0012] In infrared scene simulation systems, atmospheric transport characteristics are a key factor to consider. Since atmospheric transport characteristics require significant computation time, they need to be pre-calculated, and the results input into the shader for infrared characteristic calculations. For each vertex in the scene, the atmospheric transport characteristic parameters (path radiation, transmittance) differ depending on the light transmission path. Therefore, infrared scene simulation requires real-time acquisition of atmospheric transport characteristic data under different conditions. To ensure data transmission and extraction performance, atmospheric transport characteristic parameters under different conditions are often transferred from CPU memory to the GPU cache in the form of a 2D texture.

[0013] Currently, atmospheric transport texture data is primarily organized around the sensor, storing atmospheric transport parameters in two dimensions: different sensing pitch angles and sensor height. While this can meet the real-time extraction requirements of atmospheric transport parameters in infrared scene simulations, it also has many limitations, mainly in two aspects: 1) When the pitch angle is very small, even a small change in sensor height can lead to a significant change in the corresponding detection distance, resulting in huge differences in atmospheric transport characteristics and obvious unreasonable atmospheric coupling effects in the simulation image; 2) Current atmospheric transport textures use a linear storage method for sensor height. If the range of sensor heights stored is too large, it cannot meet the accuracy requirements of small-scene infrared simulation data. If the height range used is too small, it cannot cover the sensor height range in large scenes, making the atmospheric transport texture formats used in large scenes incompatible with those used in small scenes.

[0014] The incompatibility between small-scene and large-scene formats is essentially due to the observation geometry parameters, such as sensor height and detection distance. Minor adjustments to these parameters cause nonlinear abrupt changes in the detection range, target proportion, and atmospheric transmission path length, exceeding the parameter adaptation range of the original scene and thus leading to data consistency failure.

[0015] The incompatibility of observation geometry between small and large scenes stems from the fact that, with the height of the target point in the background remaining constant, even a small adjustment to the sensor height, such as an increase of 1 meter, can cause a significant non-linear increase in detection distance if the target height and sensor position are on the same order of magnitude. This leads to core parameters such as atmospheric transmission path length and observation angle exceeding the design thresholds of the original scene. The atmospheric transmission model, sensor calibration coefficients, and data processing algorithms in the original scene were all optimized based on specific geometric conditions. After a sudden change in parameters, the model's adaptability fails, and data consistency and measurement accuracy cannot be guaranteed, resulting in incompatibility between the two scenes. Scene design thresholds are pre-set allowable ranges / critical values ​​for core parameters such as detection distance, sensor height, and observation angle to ensure measurement accuracy, algorithm adaptability, and data validity in a specific scene.

[0016] Small scenes and large scenes each correspond to an independent set of design thresholds, and the threshold ranges of the two are significantly different. When the parameters cross from the threshold range of one scene to the threshold range of another scene, the original scene's model and algorithm will fail, resulting in incompatibility between the two scenes.

[0017] For example, regarding detection distance, the design threshold for small-scale scenarios is 5-50 meters for close-range detection. The design threshold for large-scale scenarios is 50-500 meters for medium- to long-range detection. An example of an incompatible trigger point is as follows: when the sensor is raised by 1 meter, the detection distance increases from 40 meters to 70 meters, exceeding the critical value of 50 meters.

[0018] To solve the above technical problems, see Appendix Figure 1 This application proposes a method for generating atmospheric transport data in a predetermined format based on sensor altitude changes, comprising: In step S102, the sensor determines the first atmospheric transport characteristic data calculated under the observation geometric parameters of the first atmospheric transport.

[0019] The observation geometric parameters of the first atmospheric transmission include: observation zenith angle and first detection distance.

[0020] In this embodiment, when the target being measured is in a fixed position in the air, the altitude of the target point is fixed. For example, when measuring the target on a building or the ground, the physical quantity that affects the measurement distance is the sensor height.

[0021] See appendix Figure 2As shown, when the relative position between the sensor and the fixed measurement target is horizontal, the detection distance is L1. When the height of the sensor increases, the detection distance becomes L2. L2 is obviously greater than L1, especially when the horizontal distance between the sensor and the fixed measurement target is large, the difference between L2 and L1 will be very large.

[0022] In step S104, the first detection distance from the first atmospheric transmission observation geometric parameters is input into the transformation model to obtain the second atmospheric transmission observation geometric parameters.

[0023] The observation geometry parameters for the second atmospheric transport include: the second detection range.

[0024] In this embodiment, the first observation zenith angle is defined as the angle between the vector from the observation point to the sensor and the vector from the Earth's center to the observation point. One way to determine the observation zenith angle is to range from 0° to 180° with 10° intervals, but it is not limited to this method.

[0025] First detection range L The slant distance from the observation point to the sensor is determined using an exponential distribution. One way to determine the detection distance is as follows: The unit is km. =0,1, 2,,,,,17, a total of 18 values, but not limited to this value selection method.

[0026] The transformation model is used to achieve a nonlinear transformation of pre-sensitization followed by saturation.

[0027] In this embodiment, the transformation model can be implemented using a logarithmic function, an arctangent function, or another method. The function implementation, where k is less than 1.

[0028] In step S108, a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data. The dimensions of the first two-dimensional atmospheric transport data two-dimensional distribution map include at least the second detection distance.

[0029] In this embodiment, a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format can be generated based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data. In the aforementioned first two-dimensional atmospheric transport data two-dimensional distribution map in the predetermined format, the first dimension is the observed zenith angle, and the second dimension is the second detection distance.

[0030] The technical solution described above in this application employs a transformation model to achieve a nonlinear transformation with pre-sensitization followed by saturation. This transformation can perform a nonlinear transformation with pre-sensitization followed by saturation on the first detection distance. The advantage of this transformation is that when the first detection distance is relatively large, the value of the second detection distance becomes relatively small after the transformation. In particular, when the value of the first detection distance changes, the change value of the second detection distance after the transformation tends to saturate, which is beneficial to the compatibility of atmospheric transmission characteristic data formats before and after changes in sensor altitude.

[0031] In some embodiments, the transformation model includes: a logarithmic function, an arctangent function, or a power function; wherein the exponent of the power function is greater than zero and less than one.

[0032] In this embodiment, preferably, a logarithmic function is used, and the logarithmic function is: = .

[0033] When choosing a power function, the form of the power function is Y= Where K is a number greater than zero and less than 1.

[0034] Similar to the logarithmic and power functions mentioned above, the arctangent function also exhibits the characteristic that as the value of the x-coordinate increases, the value of the y-coordinate tends to stabilize, approaching saturation. This saturation characteristic is very suitable for the scenario requirements of this application.

[0035] For example, when a sensor changes position and its altitude increases, the detection distance obtained in the next detection will be significantly different from that of the previous detection. However, after processing with a transformation model, this variation will be reduced. This facilitates data format compatibility.

[0036] In some embodiments, see Appendix Figure 3 After determining the first atmospheric transport characteristic data, the method may further include the following steps: In step S202, the first atmospheric transport characteristic data is subjected to differential expansion processing to obtain the second atmospheric transport characteristic data, and the observation geometric parameters of the third atmospheric transport are obtained.

[0037] In this embodiment, the acquisition of atmospheric transport characteristic data, such as transmittance, extinction coefficient, and radiance, is limited by issues such as the accuracy of observation equipment, observation costs, and insufficient spatiotemporal coverage. The raw data often exhibits sparsity or discontinuity. The core purpose of interpolation extension is to fill in the missing data based on existing sparse data using mathematical methods, forming a complete and continuous dataset to meet the needs of subsequent applications.

[0038] Atmospheric transmittance was calculated at only 5 stations in a certain area. By interpolation, transmittance data for each 1km×1km grid in the area can be obtained, achieving full spatial coverage.

[0039] In this embodiment, the number of the observed geometric parameters of the third atmospheric transport is an integer power of 2.

[0040] The first atmospheric transport characteristic data is subjected to differential expansion processing, and the number of observed geometric parameters of the third atmospheric transport is also differentially expanded accordingly. The number of observed geometric parameters of the third atmospheric transport after differential expansion is an integer power of 2, which is to improve the efficiency of subsequent data processing and adapt to algorithms and hardware architecture.

[0041] In computers, integer powers of 2 can be quickly divided using binary shift operations, avoiding complex multiplication and division operations and significantly improving processing speed.

[0042] Frequency domain analysis of atmospheric transmission data, such as noise removal and feature extraction, often employs the Fast Fourier Transform (FFT). The FFT algorithm has the lowest computational complexity and can significantly reduce computation time when the input data length is an integer power of 2.

[0043] In step S204, a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the second atmospheric transport characteristic data and the observation geometric parameters of the third atmospheric transport. The dimensions of the second two-dimensional atmospheric transport data distribution map include at least the second detection distance after differential expansion processing.

[0044] In this embodiment, in the second two-dimensional atmospheric transport data two-dimensional distribution map of the predetermined format, one dimension is the second detection distance after difference expansion processing, and the other dimension is the observation zenith angle after difference expansion processing.

[0045] In some embodiments, step S102, determining the first atmospheric transport characteristic data calculated by the sensor under the observation geometric parameters of the first atmospheric transport, may specifically include the following steps: The sensor height and sensor zenith angle are determined based on the observed zenith angle and the first detection distance.

[0046] The aforementioned first atmospheric transmission characteristic data are determined based on the sensor height and sensor zenith angle. The first atmospheric transmission characteristic data includes transmittance and path radiation.

[0047] In this embodiment, the actual path of light through the atmosphere can be calculated using the sensor zenith angle and sensor height. The larger the zenith angle, the longer the path of light. The sensor height is used to determine the atmospheric density; the higher the altitude, the thinner the atmosphere, and the less absorption and scattering of light. With the sensor zenith angle and sensor height, combined with wavelength and atmospheric composition parameters, the transmittance is calculated. , ; Where T is the transmittance; n is the atmospheric molecular density, which is affected by the sensor height and the height of the target being measured. k is the proportionality constant of the atmospheric extinction coefficient; m is the total path of light propagation, which is affected by the detection distance and the observation zenith angle.

[0048] The path radiation is calculated based on the transmittance mentioned above.

[0049] In some embodiments, determining the sensor height and sensor zenith angle based on the observed zenith angle and the first detection distance includes: ; in, For sensor height; To observe the zenith angle; This is the first detection range.

[0050] ; in, The zenith angle of the sensor; For sensor height; L represents the first detection distance.

[0051] In some embodiments, when the transformation model is a logarithmic function, the first detection distance from the first atmospheric transport observation geometry parameters is input into the transformation model to obtain the second atmospheric transport observation geometry parameters, including: = ; Where L is the first detection distance; This is the second detection range.

[0052] In some embodiments, the second atmospheric transport characteristic data includes transmittance and path radiation.

[0053] A second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the second atmospheric transport characteristic data and the observed geometric parameters of the third atmospheric transport, including: In channel A, a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the transmittance and the observation geometric parameters of the third atmospheric transport. In the R, G, and B channels, a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the observed geometric parameters of the path radiation and the third atmospheric transport.

[0054] The following section details another method for processing background atmospheric transmission characteristics data suitable for real-time simulation of infrared scenes. The specific scheme is as follows: The first step is to determine the observation zenith angle used for texture data. (Unit: °) and detection distance L (Unit: km) Take the value and calculate the corresponding sensor height. (Unit: km) and sensor zenith angle (Unit: °).

[0055] Among them, the observed zenith angle Defined as the angle between the vector from the observation point to the sensor and the vector from the Earth's center to the observation point, ranging from 0° to 90°, with intervals of 10°; detection distance. L The slant distance from the observation point to the sensor. L = 1.5 N - 0.99 km, N =0,1, 2,,,,,17, a total of 18 values.

[0056] Using sensor height in calculations for: ; Sensor zenith angle for: .

[0057] The second step is based on the sensor height. and sensor zenith angle Calculate the sensor's position at different observation zenith angles and distances in the scene coordinate system. L Atmospheric transport characteristics (transmittance, path radiation) under these conditions.

[0058] The third step is to adjust the detection distances accordingly. L Convert to logarithm of distance N , N= .

[0059] Step 4: Based on the logarithms of different zenith angles and detection distances, convert atmospheric transmission characteristics (transmittance, path radiation) data under different conditions into texture data. The texture image width stores atmospheric transmission characteristic data for different distance logarithms, the texture image height stores atmospheric transmission characteristic data for different zenith angles, the A channel of the texture pixel stores atmospheric transmittance, and the RGB channels of the texture pixel store atmospheric path radiation data. The path radiation storage accuracy is 0.001 W / (m). 2 .Sr).

[0060] This application provides a novel atmospheric transmission texture data format and generation method suitable for simulating backgrounds and ground structures in infrared scenes. By redesigning the organization of the atmospheric transmission texture data, it addresses the incompatibility issue between large-scale and small-scale scenes caused by significant variations in detection distance data at low angles. This texture, combined with a graphics rendering engine, effectively solves the problem of atmospheric transmission coupling simulation accuracy in large-scale and detailed scene simulations. This invention provides a high-precision background atmospheric transmission coupling scheme for real-time simulation of comprehensive scene infrared characteristics based on a graphics rendering engine. It supports infrared characteristic simulation of fixed facilities such as backgrounds and buildings, solving the problems of drastic changes in atmospheric transmission parameters with detector altitude at low elevation angles and the incompatibility of atmospheric transmission textures between large and small-scale scenes.

[0061] Secondly, this application proposes a device for generating atmospheric transmission data in a predetermined format based on sensor altitude changes, see appendix. Figure 4 ,include: The first determining module 21 is used to determine the first atmospheric transmission characteristic data calculated by the sensor under the observation geometric parameters of the first atmospheric transmission; The observation geometric parameters of the first atmospheric transmission include: observation zenith angle and first detection distance; The conversion module 22 is used to input the first detection distance from the first atmospheric transmission observation geometric parameters into the transformation model to obtain the second atmospheric transmission observation geometric parameters. The transformation model is used to achieve a nonlinear transformation of pre-sensitization followed by saturation. The observation geometry parameters for the second atmospheric transport include: the second detection range; Image module 23 is used to generate a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data. The dimensions of the first two-dimensional atmospheric transport data two-dimensional distribution map include at least the second detection distance.

[0062] In some embodiments, a difference processing module is further included, which, after the first determining module 21 determines the first atmospheric transport characteristic data, performs difference expansion processing on the first atmospheric transport characteristic data to obtain the second atmospheric transport characteristic data and obtain the observed geometric parameters of the third atmospheric transport. The number of the observed geometric parameters of the third atmospheric transmission are all integer powers of 2; Image module 23 is also used to generate a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format based on the second atmospheric transport characteristic data and the observation geometric parameters of the third atmospheric transport.

[0063] In some embodiments, the first determining module 21 is further configured to determine the sensor height and the sensor zenith angle based on the observed zenith angle and the first detection distance; The first atmospheric transport characteristic data is determined based on the sensor height and the sensor zenith angle.

[0064] The first determining module 21 is also used to calculate using the following formula: ; ; in, For sensor height; To observe the zenith angle; The zenith angle of the sensor; For sensor height; L represents the first detection distance.

[0065] In some embodiments, the conversion module 22 is further configured to, when the transformation model is a logarithmic function, input the first detection distance from the observation geometric parameters of the first atmospheric transport measurement into the transformation model to obtain the observation geometric parameters of the second atmospheric transport measurement, including: = ; Where L is the first detection distance; This is the second detection range.

[0066] The second atmospheric transport characteristic data includes transmittance and path radiation.

[0067] Image module 23 is also used to generate a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format in channel A based on the transmittance and the observation geometry parameters of the third atmospheric transport measurement; In the R, G, and B channels, a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the observed geometric parameters of the path radiation and the third atmospheric transport measurement.

[0068] Thirdly, see appendix. Figure 5 This application proposes an electronic device including a memory 32, a processor 31, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described in any of the preceding claims.

[0069] The aforementioned electronic devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. These electronic devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that the figures are merely examples of electronic devices and do not constitute a limitation on the electronic devices. They may include more or fewer components than illustrated, or combine certain components, or different components. For example, the aforementioned electronic devices may also include input / output devices, network access devices, buses, etc.

[0070] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0071] Fourthly, this application proposes a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of any of the above methods.

[0072] 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 method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes, characterized in that, include: Determine the first atmospheric transport characteristic data calculated by the sensor under the observed geometric parameters of the first atmospheric transport; The observation geometric parameters of the first atmospheric transmission include: observation zenith angle and first detection distance; The first detection distance from the first atmospheric transmission observation geometry parameters is input into the transformation model to obtain the second atmospheric transmission observation geometry parameters. The transformation model is used to achieve a nonlinear transformation of pre-sensitization followed by saturation. The observation geometry parameters for the second atmospheric transport include: the second detection range; Based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data, a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated. The dimensions of the first two-dimensional atmospheric transport data two-dimensional distribution map include at least the second detection distance.

2. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 1, characterized in that, The transformation model includes: a logarithmic function, an arctangent function, or a power function; Wherein, the exponent of the power function is greater than zero and less than one.

3. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 1, characterized in that, After determining the first atmospheric transport characteristic data, the method further includes: The first atmospheric transport characteristic data is subjected to interpolation expansion processing to obtain the second atmospheric transport characteristic data, and the observation geometric parameters of the third atmospheric transport are obtained. A second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the second atmospheric transport characteristic data and the observation geometric parameters of the third atmospheric transport. The dimensions of the second two-dimensional atmospheric transport data distribution map include at least the second detection distance after differential expansion processing.

4. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 3, characterized in that, The number of the observed geometric parameters for the third atmospheric transport is an integer power of 2.

5. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 1, characterized in that, Determine the first atmospheric transport characteristic data calculated by the sensor under the observed geometric parameters of first atmospheric transport, including: The sensor height and sensor zenith angle are determined based on the observed zenith angle and the first detection distance. The first atmospheric transport characteristic data is determined based on the sensor height and the sensor zenith angle.

6. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 5, characterized in that, Determining the sensor height based on the observed zenith angle and the first detection distance includes: ； in, For sensor height; To observe the zenith angle; L represents the first detection distance.

7. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 6, characterized in that, Determining the sensor zenith angle based on the observed zenith angle and the first detection distance includes: ； in, The zenith angle of the sensor; For sensor height; L represents the first detection distance.

8. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 2, characterized in that, When the transformation model is a logarithmic function, the first detection distance from the first atmospheric transport observation geometry parameters is input into the transformation model to obtain the second atmospheric transport observation geometry parameters, including: = ； Where L is the first detection distance; This is the second detection range.

9. The method for generating atmospheric transmission data in a predetermined format based on sensor altitude changes according to claim 3, characterized in that, The second atmospheric transport characteristic data includes: transmittance and path radiation; A second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the second atmospheric transport characteristic data and the observed geometric parameters of the third atmospheric transport, including: In channel A, a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the transmittance and the observation geometric parameters of the third atmospheric transport. In the R, G, and B channels, a second two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format is generated based on the observed geometric parameters of the path radiation and the third atmospheric transport.

10. A device for generating atmospheric transmission data in a predetermined format based on sensor altitude changes, characterized in that, include: The first determining module is used to determine the first atmospheric transmission characteristic data calculated by the sensor under the observation geometric parameters of the first atmospheric transmission; The observation geometric parameters of the first atmospheric transmission include: observation zenith angle and first detection distance; The conversion module is used to input the first detection distance from the first atmospheric transmission observation geometric parameters into the transformation model to obtain the second atmospheric transmission observation geometric parameters. The transformation model is used to achieve a nonlinear transformation of pre-sensitization followed by saturation. The observation geometry parameters for the second atmospheric transport include: the second detection range; The image module is used to generate a first two-dimensional atmospheric transport data two-dimensional distribution map in a predetermined format based on the observed geometric parameters of the second atmospheric transport and the first atmospheric transport characteristic data. The dimensions of the first two-dimensional atmospheric transport data two-dimensional distribution map include at least the second detection distance.

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