A three-dimensional infrared cloud simulation method based on microphysical properties

Abstract

The application provides a three-dimensional infrared cloud simulation method based on microphysical properties, and relates to the technical field of infrared scene simulation.The method comprises the following steps: classifying a preset VDB file according to shape characteristics; acquiring meteorological microphysical parameters, and modulating the density of the classified VDB data according to cloud water content CWC to generate cloud density distribution data; based on water droplet effective radius LER and ice particle effective radius IER, combining complex refractive index to establish infrared absorption and scattering characteristics by using Mie scattering theory; constructing volume-related radiation amount closure, encapsulating the infrared absorption and scattering characteristics and the cloud density distribution data into a to-be-transferred radiation amount closure to realize decoupling of material characteristics and numerical integration; and performing energy conservation calculation under the integral framework by using a ray tracing algorithm to comprehensively generate an infrared radiation characteristic diagram of all elements in the field of view.The application solves the problems of insufficient physical accuracy of infrared cloud simulation and poor texture expansion at a specific angle.

Description

Technical Field

[0001] This invention relates to the field of infrared scene simulation technology, and in particular to a three-dimensional infrared cloud simulation method based on microphysical properties. Background Technology

[0002] In modern warfare systems, the importance of infrared-guided weapons continues to rise, making effectiveness assessment and boundary verification crucial. Due to variations in environmental conditions such as weather and seasons, the infrared characteristics resulting from the coupling between targets and the background differ significantly, leading to time-consuming and costly comprehensive field assessments and verifications. In air combat scenario simulations, clouds, as a typical aerial feature, are participating media with absorption, scattering, and emission effects, significantly impacting the infrared characteristics of targets and often causing tracking interruptions or guidance drift. Therefore, accurately describing and simulating the infrared characteristics of clouds based on their physical properties and dynamic processes is of significant research importance.

[0003] Currently, the closest existing technology typically employs volume-based ray casting algorithms. This technique calculates the transmittance and color values ​​of clouds at different viewing angles, stores these values ​​in a 2D texture, and then applies the texture to 2D patches for display. In addition, some solutions attempt to use ray tracing algorithms to render 3D clouds in the visible light band, or to calculate the infrared radiation characteristics of cumulus clouds through the physical process of multiple scattering.

[0004] However, the aforementioned existing technologies have revealed significant limitations in practical applications. The two-dimensional physical quantity textures obtained by ray casting algorithms are only the result under a specific viewing angle, and their scalability is severely limited. Furthermore, such algorithms require manual control of sampling settings for light sources and shadows in the scene, making the simulation process cumbersome and the calculation results potentially difficult to converge. In addition, most existing 3D cloud rendering technologies do not deeply involve the infrared band and have a low degree of focus on physical accuracy, making it difficult to meet the computational requirements for high-fidelity infrared characteristics. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, the purpose of this invention is to provide a three-dimensional infrared cloud simulation method based on microphysical properties. This invention solves the problems of insufficient physical accuracy of infrared characteristic simulation and poor texture extensibility at specific angles in existing technologies.

[0006] To achieve the above objectives, the present invention provides the following solution: A three-dimensional infrared cloud simulation method based on microphysical properties includes: Based on the shape characteristics of different types of clouds, the preset three-dimensional voxel database (VDB) files are classified to obtain classified VDB data corresponding to different cloud types. Meteorological microphysical parameters corresponding to the simulation environment are obtained, and the cloud water content (CWC) in the meteorological microphysical parameters is used to perform density modulation on the classified VDB data to obtain cloud density distribution data that reflects the spatial distribution of water mass in the cloud. Based on the effective radius of water droplets (LER) and the effective radius of ice particles (IER) in the meteorological microphysical parameters, and combined with the preset complex refractive index, the optical characteristic parameters of the cloud particle system are calculated using Mie scattering theory to establish the infrared absorption and scattering characteristics at the corresponding locations. The infrared absorption and scattering characteristics include: the mass absorption cross section, mass scattering cross section, mass extinction cross section of the particle system at different locations, and the volume asymmetry factor used to describe the spatial distribution of scattering. A volume-dependent radiance closure is constructed, using shader absorption and scattering phase functions and their parameter fields as basic units. The infrared absorption and scattering characteristics and the cloud density distribution data are encapsulated to obtain the radiance closure to be transmitted, so as to achieve decoupling of material radiometric properties and ray tracing numerical integration. The energy conservation calculation of the closure of the radiance to be transmitted is performed using a ray tracing algorithm within an integral framework. The infrared radiation of each element within the field of view is tracked, and the infrared radiation characteristic map of all elements within the field of view is generated by comprehensive calculation.

[0007] The present invention discloses the following technical effects: This invention provides a three-dimensional infrared cloud simulation method based on microphysical properties. It selects cloud VDB files, commonly used in artistic expression and animation production, to ensure the visual quality of the simulation results. Satellite data is directly imported into the three-dimensional rendering engine, making the simulation of infrared clouds based on real physical parameters. Using a ray tracing algorithm based on radiance closure, while ensuring the simulation results strictly adhere to the physical process, the invention separates the optical material settings of the cloud from the rendering integral calculation, bypassing the complex knowledge barrier of computer graphics and allowing researchers to focus on the study of physical properties. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A flowchart of a three-dimensional infrared cloud simulation method based on microphysical properties provided for an embodiment of the present invention; Figure 2 This is a schematic diagram of the outline of cumulus clouds provided in an embodiment of the present invention, wherein, Figure 2 (a) is a schematic diagram of the first outline. Figure 2 (b) is a schematic diagram of the second outline; Figure 3 This is a schematic diagram of the outline of a stratocumulus cloud provided in an embodiment of the present invention, wherein, Figure 3 (a) is a schematic diagram of the third contour. Figure 3 (b) is a schematic diagram of the fourth contour; Figure 4 This is a schematic diagram of the cirrus cloud outline provided in an embodiment of the present invention, wherein, Figure 4 (a) is a schematic diagram of the fifth contour. Figure 4 (b) is a schematic diagram of the sixth contour; Figure 5 This is a diagram illustrating the effect of a cumulus cloud vdb file provided in an embodiment of the present invention. Figure 5 (a) is the first rendering. Figure 5 (b) is the second rendering; Figure 6 This is a diagram illustrating the effect of a stratocumulus cloud VDB file provided in an embodiment of the present invention. Figure 6 (a) is the third rendering. Figure 6 (b) is the fourth rendering; Figure 7 This is a diagram illustrating the effect of a cumulus cloud vdb file provided in an embodiment of the present invention. Figure 7 (a) is the fifth rendering. Figure 7 (b) is the sixth rendering; Figure 8 A diagram illustrating the effect of VDB data provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the microphysical properties of cumulus clouds provided in an embodiment of the present invention. Figure 9 (a) is a schematic diagram of the first microphysical property. Figure 9 (b) is a schematic diagram of the second microphysical property; Figure 10 This is a schematic diagram of the complex refractive index of water provided in an embodiment of the present invention. Figure 10 (a) is a schematic diagram of the first complex refractive index. Figure 10 (b) is a schematic diagram of the second complex refractive index; Figure 11 This is a schematic diagram of the single-particle scattering efficiency factor of water droplets with radii of 5-24 μm in the 3-5 μm wavelength range provided in an embodiment of the present invention. Figure 11 (a) is a schematic diagram of the first scattering efficiency factor. Figure 11 (b) is a schematic diagram of the second scattering efficiency factor; Figure 12 This is a schematic diagram of the single-particle absorption efficiency factor of water droplets with radii of 5-24 μm in the 3-5 μm wavelength range provided in an embodiment of the present invention. Figure 12 (a) is a schematic diagram of the first absorption efficiency factor. Figure 12 (b) is a schematic diagram of the second absorption efficiency factor; Figure 13 This is a schematic diagram of the single-particle asymmetry factor of water droplets with radii of 5-24 μm within the 3-5 μm wavelength range provided in an embodiment of the present invention. Figure 13 (a) is a schematic diagram of the first asymmetric factor. Figure 13 (b) is a schematic diagram of the second asymmetric factor; Figure 14 This is a schematic diagram of the mass scattering cross section and mass extinction interface of cumulus clouds provided in an embodiment of the present invention. Figure 14 (a) is a schematic diagram of the first mass scattering cross section and the mass extinction interface. Figure 14 (b) is a schematic diagram of the second mass scattering cross section and the mass extinction interface; Figure 15 A schematic diagram of the volume asymmetry factor of cumulus provided in an embodiment of the present invention; Figure 16 Infrared simulation images of cumulus clouds in the 3-5μm band, considering only self-emission, are provided for embodiments of the present invention. Figure 16 (a) is a simulated infrared image of the first cumulus cloud's self-radiation. Figure 16 (b) is a simulated infrared image of the second cumulus cloud's self-radiation. Figure 16 (c) is a simulated infrared image of the third cumulus cloud's self-radiation. Figure 16 (d) is a simulated infrared image of the fourth cumulus cloud self-radiation; Figure 17 The infrared simulation image in the 3-5μm band under cumulus cloud and solar coupling conditions provided in this embodiment of the invention, wherein, Figure 17 (a) is an infrared simulation image of cumulus cloud coupled with the sun at the first micrometer. Figure 17 (b) is an infrared simulation image of cumulus clouds coupled with the sun at the second micrometer. Figure 17 (c) is an infrared simulation image of cumulus clouds coupled with the sun at the third micrometer. Figure 17 (d) is an infrared simulation image of cumulus cloud coupled with the sun at the fourth micrometer. Figure 18 Infrared simulation images in the 3-5μm band under cumulus cloud-solar-ocean coupling conditions provided in this embodiment of the invention, wherein... Figure 18 (a) is an infrared simulation image of the first cumulus cloud coupled with the sun and ocean. Figure 18 (b) is an infrared simulation image of the second cumulus cloud coupled with the sun and ocean. Figure 18 (c) is an infrared simulation image of the third cumulus cloud coupled with the sun and ocean. Figure 18 (d) is an infrared simulation image of the fourth cumulus cloud coupled with the sun and ocean; Figure 19The image provided in the 3.7-4.8 μm band is a real-world image of cloud infrared radiation, as shown in the embodiment of the present invention. Figure 20 The simulated cloud infrared radiation image in the 3.7-4.8 μm band is provided for the embodiments of the present invention. Detailed Implementation

[0010] 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, and 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] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] like Figure 1 As shown, this invention provides a three-dimensional infrared cloud simulation method based on microphysical properties, including: Step 100: Classify the preset three-dimensional voxel database (VDB) files according to the shape characteristics of different types of clouds to obtain classified VDB data corresponding to different cloud types. Step 200: Obtain the meteorological microphysical parameters corresponding to the simulation environment, and perform density modulation on the classified VDB data according to the cloud water content (CWC) in the meteorological microphysical parameters to obtain cloud density distribution data reflecting the spatial distribution of water mass in the cloud. Step 300: Based on the effective radius of water droplets (LER) and the effective radius of ice particles (IER) in the meteorological microphysical parameters, and combined with the preset complex refractive index, calculate the optical characteristic parameters of the cloud particle system using Mie scattering theory to establish the infrared absorption and scattering characteristics at the corresponding location. The infrared absorption and scattering characteristics include: infrared absorptivity, infrared scattering rate, infrared emissivity, and a volume asymmetry factor used to describe the spatial distribution of scattering. Step 400: Construct a volume-dependent radiance closure. Using shader absorption and scattering phase functions and their parameter fields as basic units, encapsulate the infrared absorption and scattering characteristics with the cloud density distribution data to obtain the radiance closure to be transmitted, so as to achieve decoupling of material radiometric properties and ray tracing numerical integration. Step 500: Using a ray tracing algorithm, perform energy conservation calculations on the closure of the radiance quantity to be transferred within an integral framework, track the infrared radiation of each element within the field of view, and comprehensively calculate and generate an infrared radiation characteristic map of all elements within the field of view.

[0013] Specifically, the logical flow of the method of the present invention is as follows: There is a large amount of cloud VDB data on the Internet used in the fields of film, animation and other art. The VDB data is classified according to the effect diagram and meteorological definition. By consulting meteorological data, microphysical parameters for different cloud types under different conditions were obtained, including cloud water content, effective water droplet radius, and effective ice particle radius. Cloud water content was used to modulate the VDB density data for different cloud types, while the effective water droplet radius and effective ice particle radius were used to calculate the absorption and scattering characteristics of cloud particle systems at different locations, for use by the 3D rendering engine. Using a ray tracing algorithm based on radiance closure, the infrared absorption and scattering characteristics of clouds in the infrared band can be accurately described, while the infrared radiation of other objects in the scene can be tracked, and the infrared radiation characteristics of each element in the field of view can be calculated comprehensively.

[0014] Furthermore, the element object includes: Clouds, sun, ocean, and target.

[0015] Furthermore, the shape characteristics of different types of clouds include: Cumulus, stratocumulus, and cirrus.

[0016] Specifically, such as Figure 2-4 As shown, cumulus clouds mostly have a clear outline, developing vertically in the form of rising mounds, domes, or towers, with their protruding upper parts often resembling cauliflower. In the visible light spectrum, the sunlit portions of these clouds are mostly white, while their lower parts are relatively dark and almost horizontal.

[0017] Stratocumulus clouds (Sc) are often gray or white in the visible light band. The cloud structure is relatively loose, consisting of checkerboard-shaped cloud blocks, sheets, or layers, appearing as strips or clumps.

[0018] Cirrus clouds exhibit diverse forms, including fibrous, linear, and feather-like shapes, and possess a strong sense of fluidity. Due to the shearing effect of airflow, cirrus clouds often display curved and twisted shapes.

[0019] Furthermore, such as Figure 5-7 As shown, VDB is an abbreviation for "Voxel Database," a file format for storing three-dimensional volume data. It can record information such as density and temperature at different coordinate points in three-dimensional space. There are a large number of publicly available cloud VDB files with different geometric characteristics used for artistic expression and animation production on the Internet. Based on the VDB files from online resources, the density distribution is modulated according to the microphysical properties of different clouds, and then imported into the scene.

[0020] Furthermore, by combining the preset complex refractive index with Mie scattering theory, the optical characteristic parameters of the cloud particle system are calculated to establish the infrared absorption and scattering characteristics at the corresponding locations, including: The size factor is determined based on the wavelength, the effective radius LER of the water droplet, and the effective radius IER of the ice particle, and the extinction efficiency factor, scattering efficiency factor, absorption efficiency factor, and asymmetry factor of a single spherical particle are calculated. Based on the extinction efficiency factor, scattering efficiency factor and absorption efficiency factor of the individual spherical particles, and in conjunction with the cloud water content CWC and the preset particle size probability density distribution function, integral calculations are performed to obtain the mass extinction cross section, mass scattering cross section and mass absorption cross section of the cloud particle system. The volume asymmetry factor is obtained by weighted averaging the asymmetry factor of the individual spherical particles in combination with the particle size probability density distribution function. Specifically, by consulting meteorological data, microphysical parameters for different conditions and cloud types are set, including cloud water content, effective radius of water droplets, and effective radius of ice particles. Among them, cloud water content is used to modulate VDB data for different types of clouds. Based on the temperature and particle phase characteristics within clouds, atmospheric clouds can be classified into water clouds composed of liquid water droplets, ice clouds composed of ice crystal particles, and mixed-phase clouds composed of liquid water droplets and ice crystals. The attenuation of infrared radiation in clouds is mainly caused by absorption and scattering by particles within the cloud cluster. This absorption and scattering characteristic depends primarily on the particle size and the water content per unit volume. Therefore, the cloud water content (CWC), liquid effective radius (LER), and ice effective radius (IER) are the main cloud microphysical properties considered. The physical definitions of these three properties are introduced below.

[0021] CWC represents the total mass of liquid water and ice crystals contained in a unit volume of air, and is commonly used to characterize the water vapor content of clouds. The unit is 1000 liters. It is a parameter that directly reflects the mass distribution of liquid or solid water within a cloud, defined as follows: ; In the formula, LWC and IWC represent the total mass of liquid water droplets or solid ice crystals per unit volume of air, respectively; Indicates air volume The total mass of liquid water and ice crystals contained therein.

[0022] LER represents the average radius of liquid water droplets in a cloud, which is a weighted average based on surface area. It reflects the average characteristics of the cloud droplet size distribution and is an important parameter for measuring the microphysical structure of clouds. Its definition is as follows: ; In the formula, The density of liquid water is usually expressed as . ; It represents the total surface area of ​​all water droplets in a unit volume of air.

[0023] IER represents the average radius of solid ice crystals in clouds, and is a weighted average based on surface area. It reflects the average characteristics of the ice crystal size distribution and is an important parameter for measuring the microphysical structure of clouds. Its definition is as follows: ; In the formula, The density of ice crystals is usually expressed as . ; It represents the total surface area of ​​all water droplets in a unit volume of air.

[0024] According to Kirchhoff's laws, absorptivity equals emissivity. The absorptivity per unit volume in a volumetric cloud is equal to the ratio of absorbed power to incident power. The absorption characteristics of a particle system per unit volume can be derived from the absorption characteristics of individual particles, thus obtaining the infrared emissivity at different locations within the volumetric cloud. The absorption and scattering characteristics of individual particles in the cloud can be rigorously given by Mie theory. Combined with the particle size probability density distribution function, the absorption and scattering characteristics of the cloud particle system can be calculated.

[0025] The IER (Intensity Reflection Equivalent) of ice crystal particles in clouds was obtained by weighted averaging of surface areas with different shapes. Since the shape of liquid water droplets in clouds is nearly spherical, all particles in the cloud are considered spherical. Before calculating the scattering and absorption characteristics of the cloud particle system, it is necessary to know the extinction characteristics parameters of a single particle's single scattering, such as absorption cross section, extinction cross section, scattering cross section, and scattering phase function. For spherical particles, these parameters can be obtained using Mie scattering theory. Besides the radius, the complex refractive index of water at different wavelengths is also an important influencing factor. The complex refractive index of water is defined as: ; Wherein, the real part of the complex refractive index It is expressed as the ratio of the phase velocity of a light wave in air to its phase velocity in a particle; the imaginary part. It is related to the absorption of particles, if This indicates that the particle does not absorb light waves.

[0026] The Mie theory applies Maxwell's equations to the medium inside and outside scattering particles, ensuring that its solutions are consistent at the particle interfaces. Its extinction efficiency factor... Scattering efficiency factor Absorption efficiency factor and asymmetric factors The solution is: ; ; ; ; in, Represents the size factor, determined by wavelength. With particle radius To be determined jointly. and Depend on and related: ; ; ; and These are the half-integer order Bessel function and the Hankel function, respectively.

[0027] In reality, clouds are diffuse systems composed of numerous particles. The scattering efficiency factor of a single particle can be calculated based on fundamental parameters such as the particle's complex refractive index, incident radiation wavelength, and particle radius. Absorption efficiency factor and asymmetric factors Then, the cloud water content (CWC) and particle size probability density function are combined. The optical properties of the particle system can then be determined.

[0028] As radiation propagates through clouds, radiation attenuation and incident radiation flux... The cloud water density content (CWC) is directly proportional to the distance traveled (ds), and the corresponding radiation attenuation law is known as the Lambert-Beer law, as shown below: ; ; in, This represents the mass extinction cross section of particles at different positions in different wavelength bands (unit: ).

[0029] The scattering efficiency factor of a single particle is known. Absorption efficiency factor The scattering cross section of a single particle can be obtained by combining the particle radius. and absorption cross section : ; By comprehensively considering the absorption and scattering characteristics of individual particles and the particle size distribution within the cloud, the mass extinction cross section of the particle system in the cloud can be obtained. : ; In the formula, It represents the mass of a particle in a cloud at a certain point in three-dimensional space.

[0030] The scattering of infrared radiation by clouds is not uniform. The Henyey-Greenstein phase function can be used to describe the non-uniform distribution of scattered infrared radiation in space, and its expression is: ; in, The angle between the directions of the light rays before and after scattering; The asymmetry factor representing a particle system is called the volume asymmetry factor, and its definition is: ; Furthermore, the process of comprehensively calculating and generating infrared radiation characteristic maps of all elements within the field of view also includes: The radiometric quantity of each voxel is statistically converged using at least one method, including importance sampling, multiple importance sampling, distance sampling, and isoangular sampling.

[0031] Specifically, by using a ray tracing algorithm based on radiance closure, the infrared absorption and scattering characteristics of clouds can be accurately described, while the infrared radiation of other objects in the scene can be tracked, and the infrared radiation characteristics of each element in the field of view can be calculated comprehensively.

[0032] The radiance closure only describes the optical properties of objects (including participating media and surface reflectors), while the rendering engine handles all light source cycling, ray generation, and radiance integral calculations. Clouds can be considered sparsely participating media composed of numerous suspended particles. Infrared radiation from other objects in the scene and the cloud itself is scattered, absorbed, and emitted at voxels at different locations, thus altering the spatial and directional distribution of the radiation field. This process is symbolized by constructing a volume-dependent radiance closure: shader absorption and scattering phase functions and their parameter fields are used as primitives, combined by weights to form a closure for transmission, rather than directly generating numerical colors. The resulting radiance closure decouples material radiometric properties from ray tracing numerical integration, allowing the 3D rendering engine to perform importance sampling and multi-importance sampling within the integration framework, and combining distance sampling, isoangular sampling, and other methods to perform energy-conserved and statistically convergent calculations of the radiance of each voxel. Therefore, the radiance and directional changes of infrared radiation at different locations within the cloud can be consistently, unbiasedly, and independently of the ray tracing integrator. The infrared radiation characteristics of each object within the camera's viewport are finally solved by an integrator.

[0033] Furthermore, the present invention also provides a specific embodiment: Simulations were performed using cumulus clouds in the 3-5μm band as an example: Cumulus clouds typically have a well-defined outline, developing vertically as rising mounds, domes, or towers, their protruding upper parts often resembling cauliflower. In the visible light spectrum, the sunlit portions of these clouds appear predominantly white, while their lower parts are relatively darker and almost horizontal. Therefore, the chosen VDB appearance effect... Figure 8 As shown; like Figure 9 As shown, after consulting relevant data, the CWC and particle size distribution function of cumulus clouds were obtained. As shown below, the CWC of cumulus clouds is generally smaller than that of other clouds. Furthermore, the particle size distribution of Cu is mainly concentrated in the range of lower density. The particle size distribution ranges from 5 to 24 μm, and the probability density function of the particle size distribution exhibits a relatively symmetrical unimodal distribution.

[0034] like Figure 10 As shown, the real and imaginary parts of the complex refractive index of water in the 3-5 μm wavelength range are illustrated.

[0035] like Figure 11-13 As shown, the single-particle scattering efficiency factor, absorption efficiency factor, and asymmetry factor of water droplets with radii of 5-24 μm are presented in the 3-5 μm band.

[0036] like Figure 14 As shown, the mass scattering cross section and mass absorption cross section of cumulus under different CWC conditions are displayed in the 3-5μm band range.

[0037] like Figure 15 As shown, the volume asymmetry factor of cumulus clouds in the 3-5 μm band is illustrated.

[0038] like Figure 16 As shown, an infrared simulation image of cumulus clouds in the 3-5 μm band is presented, considering only self-emission, with a quantization range of 0.3-1.0 W / m. 2 / sr.

[0039] like Figure 17 As shown, simulated infrared images in the 3-5 μm band are presented under cumulus cloud-solar coupling conditions, with a quantization range of 0.3-1.5 W / m. 2 / sr.

[0040] like Figure 18 As shown, simulated infrared images in the 3-5 μm band are presented under conditions of cumulus cloud coupling with the sun and ocean, with a quantization range of 0.3-2.0 W / m. 2 / sr.

[0041] Furthermore, this invention provides another embodiment that compares simulated infrared radiation cloud images based on this model with actual infrared radiation cloud images from two perspectives: mean radiance and structural similarity. Table 1 describes the environmental factors during the actual shooting: Table 1 Environmental factors: The quantization method and results of the real-world images are shown below: ; like Figures 19-20 As shown in the formula, This represents the grayscale value of a pixel after quantization. It is a floating-point number and takes the value [0,1]. This represents the original pixel grayscale value of a certain pixel output by the infrared camera. It is an integer and takes the value [0, 65535]. and These represent the minimum and maximum grayscale values ​​of the original pixels in the captured image, respectively.

[0042] Based on the actual shooting environment elements, the quantization method and quantization results of the simulated image are shown below: ; In the formula, This represents the grayscale value of a pixel after quantization. It is a floating-point number and takes the value [0,1]. This represents the radiance at a specific pixel in a simulated image, expressed in W / m². 2 / sr; and These represent the minimum and maximum values ​​of radiance in the simulated image, respectively.

[0043] After calibrating the real-world image, the radiance value of each pixel can be obtained. Table 2 shows the image results of the real-world image and the simulation image at the same quantization scale, as well as the similarity comparison results. The quantization of the real-world image and the simulation image is shown in the following formula: ; Table 2 .

[0044] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0045] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A three-dimensional infrared cloud simulation method based on microphysical properties, characterized in that, include: Based on the shape characteristics of different types of clouds, the preset three-dimensional voxel database (VDB) files are classified to obtain classified VDB data corresponding to different cloud types. Meteorological microphysical parameters corresponding to the simulation environment are obtained, and the cloud water content (CWC) in the meteorological microphysical parameters is used to perform density modulation on the classified VDB data to obtain cloud density distribution data that reflects the spatial distribution of water mass in the cloud. Based on the effective radius of water droplets (LER) and the effective radius of ice particles (IER) in the meteorological microphysical parameters, and combined with the preset complex refractive index, the optical characteristic parameters of the cloud particle system are calculated using Mie scattering theory to establish the infrared absorption and scattering characteristics at the corresponding locations. The infrared absorption and scattering characteristics include: the mass absorption cross section, mass scattering cross section, mass extinction cross section of the particle system at different locations, and the volume asymmetry factor used to describe the spatial distribution of scattering. A volume-dependent radiance closure is constructed, using shader absorption and scattering phase functions and their parameter fields as basic units. The infrared absorption and scattering characteristics and the cloud density distribution data are encapsulated to obtain the radiance closure to be transmitted, so as to achieve decoupling of material radiometric properties and ray tracing numerical integration. The energy conservation calculation of the closure of the radiance to be transmitted is performed using a ray tracing algorithm within an integral framework. The infrared radiation of each element within the field of view is tracked, and the infrared radiation characteristic map of all elements within the field of view is generated by comprehensive calculation.

2. The three-dimensional infrared cloud simulation method based on microphysical properties according to claim 1, characterized in that, The element objects include: Clouds, sun, ocean, and target.

3. The three-dimensional infrared cloud simulation method based on microphysical properties according to claim 1, characterized in that, The shape characteristics of different types of clouds include: Cumulus, stratocumulus, and cirrus.

4. The three-dimensional infrared cloud simulation method based on microphysical properties according to claim 1, characterized in that, The optical properties of the cloud particle system are calculated using Mie scattering theory based on a pre-defined complex refractive index, to establish the infrared absorption and scattering characteristics at the corresponding locations, including: The size factor is determined based on the wavelength, the effective radius LER of the water droplet, and the effective radius IER of the ice particle, and the extinction efficiency factor, scattering efficiency factor, absorption efficiency factor, and asymmetry factor of a single spherical particle are calculated. Based on the extinction efficiency factor, scattering efficiency factor and absorption efficiency factor of the individual spherical particles, and in conjunction with the cloud water content CWC and the preset particle size probability density distribution function, integral calculations are performed to obtain the mass extinction cross section, mass scattering cross section and mass absorption cross section of the cloud particle system. Based on the mass extinction cross section, mass scattering cross section, and mass absorption cross section, the volume asymmetry factor is obtained by weighted averaging using the asymmetry factor of the individual spherical particle in combination with the particle size probability density distribution function.

5. The three-dimensional infrared cloud simulation method based on microphysical properties according to claim 4, characterized in that, The calculation of the infrared radiation characteristics map of all elements within the field of view also uses the Henyey-Greenstein phase function to describe the non-uniform distribution of the infrared absorption and scattering characteristics in space. The parameters of the Henyey-Greenstein phase function are determined by the volume asymmetry factor.

6. The three-dimensional infrared cloud simulation method based on microphysical properties according to claim 4, characterized in that, The process of comprehensively calculating and generating infrared radiation characteristic maps of all elements within the field of view also includes: The radiometric quantity of each voxel is statistically converged using at least one method, including importance sampling, multiple importance sampling, distance sampling, and isoangular sampling.

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