Infrared radiation characteristic real-time simulation method for complex scene, medium and equipment

Abstract

The present application belongs to the technical field of infrared imaging simulation, and relates to a real-time simulation method for infrared radiation characteristics for complex scenes, a medium and equipment.The method comprises: obtaining geographical data, remote sensing data and meteorological parameters required for simulation; solving component temperature according to a heat conduction equation and a ground surface energy balance boundary condition to obtain equivalent temperature of a mixed pixel; calculating a directional reflection component based on a BRDF model, and calculating infrared radiation brightness according to the equivalent temperature, a directional emissivity and the directional reflection component; texture detail modulation; calculating the modulated infrared radiation brightness using a parallel computing architecture, and mapping the modulated infrared radiation brightness to a grayscale image to complete real-time rendering and output.The present application greatly improves the radiation calculation accuracy of mixed pixels, realistically simulates the directional radiation characteristics of ground surface features, significantly reduces the radiation calculation error under inclined observation conditions, ensures high-precision modeling of multi-physical field coupling, realizes real-time simulation rendering, and solves the problems of texture loss and lack of realism in simulation images.

Description

Technical Field

[0001] This invention belongs to the field of infrared imaging simulation technology, specifically relating to real-time simulation methods, media, and equipment for infrared radiation characteristics in complex scenarios. Background Technology

[0002] Infrared imaging systems are widely used in night vision, reconnaissance, and guidance. To evaluate the performance of infrared systems, train operators, and optimize system design, it is necessary to construct high-fidelity infrared scene simulation systems. The radiation characteristics of an infrared scene depend on the surface temperature distribution, emissivity, reflectivity, and interaction with the atmospheric environment, making it a complex multiphysics coupling problem.

[0003] In existing technologies, infrared radiation characteristic simulation methods are mainly divided into three categories: The first category is offline calculation methods based on the heat conduction equation. These methods obtain the surface temperature distribution of ground objects by solving one-dimensional or three-dimensional heat conduction equations, and then calculate infrared radiation by combining emissivity. The physical mechanism is rigorous, but the computational load is extremely large, and it can only be used for offline scene generation. Its ability to handle large-scale complex scenes is seriously insufficient. The second category is rapid simulation methods based on empirical models. These methods use simplified models such as isothermal assumptions and equivalent emissivity to quickly generate infrared images. They can meet real-time requirements, but the physical accuracy is low and cannot reflect the real physical process of ground object thermal radiation. In particular, the processing of mixed pixels with vegetation and soil and non-Lambertian surfaces is oversimplified, resulting in a large deviation between the simulated image and the real scene. The third category is real-time rendering methods based on GPU acceleration, which realizes real-time generation of infrared images. However, they generally use the Lambertian assumption, ignoring the directional radiation characteristics of non-Lambertian surfaces. At the same time, the component decomposition of mixed pixels is insufficient, resulting in systematic biases in radiation calculations, which cannot meet the application requirements of high-fidelity simulation.

[0004] Furthermore, the pixels of surface temperature products contain various land cover types such as vegetation, soil, and water. Existing methods usually treat mixed pixels as a single land cover and cannot decompose the true temperature of each component, resulting in insufficient processing capability of mixed pixels and low accuracy of radiation calculation. The infrared images generated by existing methods lack texture details and have a significant difference from real infrared images, failing to fully utilize the land cover texture information contained in high-resolution visible light images to enhance the realism of the simulation. Summary of the Invention

[0005] To address the aforementioned technical challenges, this invention proposes a real-time simulation method for infrared radiation characteristics in complex scenes. By combining hybrid pixel component temperature calculation, non-Lambertian directional radiation calculation, visible light texture detail enhancement, and GPU parallel acceleration architecture, it achieves infrared radiation simulation of complex scenes that balances high accuracy and high real-time performance.

[0006] In a first aspect, the present invention provides a real-time simulation method for infrared radiation characteristics in complex scenarios, including: Acquire the geographic data, remote sensing data, and meteorological parameters required for the simulation; geographic data includes topographic data and land cover classification maps; remote sensing data includes visible light remote sensing imagery; meteorological parameters include temperature, humidity, wind speed, and solar radiation. Based on the land cover classification map, the area proportion of each land cover component within each target pixel is calculated. The component temperature corresponding to each land cover component is solved according to the heat conduction equation and the surface energy balance boundary conditions. Based on the area proportion and component temperature of each land cover component, the equivalent temperature of the mixed pixel is obtained. The directional emissivity of each landform component in the observation direction is calculated based on topographic data and directional emissivity model. The directional reflection components of the land surface to solar radiation and environmental radiation are calculated based on BRDF model. The infrared radiation brightness received by the sensor is calculated based on equivalent temperature, directional emissivity and directional reflection components. Extract the texture features of visible light remote sensing images, modulate the texture details of infrared radiation brightness, and obtain the modulated infrared radiation brightness; The modulated infrared radiance is calculated using a GPU parallel computing architecture, and then mapped to a grayscale image for real-time rendering and output.

[0007] Secondly, the present invention provides a computer-readable storage medium storing a computer program for executing the aforementioned real-time simulation method for infrared radiation characteristics in complex scenarios.

[0008] Thirdly, this invention provides an electronic device, which includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to execute the real-time simulation method for infrared radiation characteristics in complex scenarios.

[0009] In some optional embodiments, the component temperatures corresponding to each land cover component are solved based on the heat conduction equation and the surface energy balance boundary conditions, including: set up The coordinates are in the depth direction. For time, the corresponding component temperatures for each local component are: , For ground feature density, For the specific heat capacity of the earth's surface, For the first The thermal conductivity of the plant components is given by the heat conduction equation: ; The surface energy balance boundary conditions are the surface energy balance equations, assuming... It is the surface albedo. Solar radiation, For downward longwave radiation, For surface temperature, Surface radiance, It is the Stefan-Boltzmann constant. For sensible heat flux, If the latent heat flux is used, then the surface energy balance equation can be expressed as: .

[0010] In some optional embodiments, the equivalent temperature of the mixed pixel is obtained based on the area ratio of each landform component and the component temperature of each landform component, including: synthesizing the component temperatures into the equivalent temperature of the mixed pixel based on a linear mixing model; assuming The equivalent temperature of the mixed pixels. For the first The area ratio of crop types within a pixel. For the first The component temperature of the crop type is then expressed as the equivalent temperature of the mixed pixel as: .

[0011] In some optional embodiments, the directional emissivity of each land feature component in the observation direction is calculated based on terrain data and a directional emissivity model, including: For vegetation, the canopy directional emissivity model is used to calculate the canopy directional emissivity in the observation direction, assuming the observation zenith angle is . , To observe the zenith angle Canopy directional emissivity in the direction of the canopy. The optical thickness of the canopy in the observation direction. The intrinsic emissivity of the blade. Let be the intrinsic emissivity of the soil, then the emissivity in the canopy direction is expressed as: ; For soil, a rough surface model is used to calculate the soil directional emissivity in the observation direction, assuming the soil directional emissivity is . , The normal emissivity of a smooth soil surface. Let be the soil surface roughness parameter, then the soil directional emissivity is expressed as: .

[0012] In some optional embodiments, the directional reflection components of solar radiation and ambient radiation from the Earth's surface are calculated based on the BRDF model, including: assuming the directional reflection components of solar radiation and ambient radiation from the Earth's surface are... , The diffuse reflectance coefficient is... The specular reflection coefficient, For the angle of incidence at the zenith, The incident azimuth angle is... To observe the zenith angle, To observe the azimuth angle, The angle between the half-angle vector and the normal. For Fresnel terms, The normal Fresnel reflection coefficient, If the direction function is given, then the Fresnel term is expressed as: ; The direction function is expressed as: ; The directional reflection components of solar radiation and ambient radiation from the Earth's surface are then expressed as: .

[0013] In some optional embodiments, the infrared radiation brightness received by the sensor is calculated based on the equivalent temperature, directional emissivity, and directional reflection components, including: set up This is Planck blackbody radiation. For surface temperature, Let be Planck's constant. At the speed of light, Boltzmann's constant, If λ is the wavelength, then Planck's blackbody radiation is: ; Let the infrared radiation brightness received by the sensor be... , For the angle of incidence at the zenith, The incident azimuth angle is... To observe the zenith angle, To observe the azimuth angle, the directional reflection components of solar radiation and environmental radiation from the Earth's surface are: , For ambient radiant brightness, For a solid angle in a hemispherical space, the integral term is... Let the integral of environmental radiation over a hemispherical space be: .

[0014] In some optional embodiments, texture features of visible light remote sensing images are extracted, and texture detail modulation is applied to the infrared radiance to obtain modulated infrared radiance, including: Let the modulated infrared radiation brightness be , Texture features extracted from visible light images, is the x-coordinate of the pixel. Let be the ordinate of the pixel, then: ; set up The x-coordinate within the local window. The vertical coordinate within the local window. Coordinates of the visible light image within a local window grayscale value at that location The average gray level of the window. This represents the average grayscale value of the visible light image within a local window. For local windows, For local windows The total number of pixels contained within. Let be the modulation intensity coefficient. Then, the texture features extracted from the visible light image are represented as: .

[0015] In some optional embodiments, mapping the modulated infrared radiance to a grayscale image includes: set up is the x-coordinate of the pixel. y is the ordinate of the pixel. The grayscale value of the grayscale image. This refers to the infrared radiation brightness after texture detail modulation. This represents the maximum value of the scene's radiant brightness. If the minimum scene radiance is given, then the grayscale value of the grayscale image is represented as: .

[0016] The beneficial effects of this invention are: (1) This invention decomposes the scale pixels of high-resolution land cover data and solves the temperature of each component by combining the one-dimensional heat conduction equation. This solves the defect of the existing technology that treats mixed pixels as a single land feature, which can greatly improve the accuracy of mixed pixel radiation calculation and eliminate the systematic error caused by vegetation-soil temperature difference. (2) This invention introduces the directional emissivity model and the BRDF model to realistically simulate the directional radiation characteristics of the ground surface, abandons the traditional Lambertian assumption, significantly reduces the radiation calculation error under tilted observation conditions, and greatly improves the physical accuracy of the simulation. (3) This invention uses a GPU parallel computing architecture to parallelize the entire process of computational tasks such as heat conduction solution, radiation calculation and texture modulation. Under the premise of ensuring high-precision modeling of multi-physics coupling, it realizes real-time simulation rendering, which solves the contradiction between the inability of high-precision models to be calculated in real time and the insufficient accuracy of fast models in the existing technology, and achieves a balance between accuracy and real-time performance. (4) This invention utilizes the texture features of high-resolution visible light images to modulate infrared radiation brightness, fully explores the application value of visible light information, and enables simulated infrared images to have texture details of ground objects consistent with real scenes, solving the problems of texture loss and lack of realism in existing simulated images, and significantly improving the realism of simulated images. (5) This invention constructs a complete technical chain from temperature calculation, radiation calculation to texture enhancement and real-time rendering, which can be adapted to complex scenes composed of various land features such as vegetation, soil, water and buildings, and can be widely used in multiple scenarios such as infrared system performance evaluation, operator simulation training and infrared guidance system hardware simulation. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the real-time simulation method for infrared radiation characteristics in complex scenarios provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the electronic device provided in Embodiment 3 of the present invention.

[0018] In the diagram: 30 - Electronic device; 310 - Processor; 320 - Bus; 330 - Memory; 340 - Transceiver. Detailed Implementation

[0019] 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, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0020] Example 1 As an example, to address the problems existing in the prior art, this embodiment provides a real-time simulation method for infrared radiation characteristics in complex scenarios.

[0021] The implementation details of the method in this embodiment are described below. The following content is only for the convenience of understanding and is not necessary for implementing this solution.

[0022] The real-time simulation method for infrared radiation characteristics in complex scenarios described in this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. (See attached...) Figure 1 As shown, the method provided in this embodiment includes steps 110-150.

[0023] Step 110: Obtain the geographic data, remote sensing data and meteorological parameters required for the simulation; geographic data includes topographic data and land cover classification maps; remote sensing data includes visible light remote sensing images; meteorological parameters include temperature, humidity, wind speed and solar radiation.

[0024] Step 120: Calculate the area proportion of each land cover component within each target pixel based on the land cover classification map, solve for the component temperature corresponding to each land cover component based on the heat conduction equation and the surface energy balance boundary conditions, and obtain the equivalent temperature of the mixed pixel based on the area proportion and component temperature of each land cover component.

[0025] Specifically, based on land cover data, the area proportion of each type of vegetation in each pixel is statistically analyzed, and the first... The area ratio of object types in each pixel is ,satisfy By quantizing the composition of mixed pixels through pixel decomposition, weights are provided for the calculation of component temperature.

[0026] In some optional embodiments, the component temperatures corresponding to each land cover component are solved based on the heat conduction equation and the surface energy balance boundary conditions, including: set up The coordinates are in the depth direction. For time, the corresponding component temperatures for each local component are: , For ground feature density, For the specific heat capacity of the earth's surface, For the first The thermal conductivity of the plant components is given by the heat conduction equation: ; The surface energy balance boundary conditions are the surface energy balance equations, assuming... It is the surface albedo. Solar radiation, For downward longwave radiation, For surface temperature, Surface radiance, It is the Stefan-Boltzmann constant. For sensible heat flux, If the latent heat flux is used, then the surface energy balance equation can be expressed as: .

[0027] This invention solves for component temperatures by applying the heat conduction equation and surface energy balance boundary conditions, thereby obtaining the surface temperature distribution of each land cover type. By decomposing scale pixels from high-resolution land cover data and combining them with the one-dimensional heat conduction equation to solve for the temperature of each component, this invention overcomes the shortcomings of existing technologies that treat mixed pixels as single land cover types. This significantly improves the accuracy of radiation calculation for mixed pixels and eliminates systematic errors caused by vegetation-soil temperature differences.

[0028] A linear mixing model is employed to synthesize the temperatures of each component into the equivalent temperature of the mixed pixel. It is assumed that the total radiant energy of the pixel is the area-weighted sum of the radiant energies of each component; this approximation is valid in the thermal infrared band (8–14 μm). By aggregating high-resolution (30 m resolution) component information into low-resolution (1 km) pixels, pixel-level temperature input is provided for subsequent non-Lambertian radiation calculations.

[0029] In some optional embodiments, the equivalent temperature of the mixed pixel is obtained based on the area ratio of each landform component and the component temperature of each landform component, including: synthesizing the component temperatures into the equivalent temperature of the mixed pixel based on a linear mixing model; assuming The equivalent temperature of the mixed pixels. For the first The area ratio of crop types within a pixel. For the first The component temperature of the crop type is then expressed as the equivalent temperature of the mixed pixel as: .

[0030] Step 130: Calculate the directional emissivity of each landform component in the observation direction based on the terrain data and directional emissivity model. Calculate the directional reflection components of the land surface to solar radiation and environmental radiation based on the BRDF model. Calculate the infrared radiation brightness received by the sensor based on the equivalent temperature, directional emissivity, and directional reflection components.

[0031] Specifically, the directional emissivity of each landform component in the observation direction is calculated based on topographic data and the directional emissivity model, including: For vegetation, the canopy directional emissivity model is used to calculate the canopy directional emissivity in the observation direction, assuming the observation zenith angle is . , To observe the zenith angle Canopy directional emissivity in the direction of the canopy. The optical thickness of the canopy in the observation direction. The intrinsic emissivity of the blade. Let be the intrinsic emissivity of the soil, then the emissivity in the canopy direction is expressed as: ; For soil, a rough surface model is used to calculate the soil directional emissivity in the observation direction, assuming the soil directional emissivity is . , The normal emissivity of a smooth soil surface. Let be the soil surface roughness parameter, then the soil directional emissivity is expressed as: .

[0032] The surface roughness parameter can be obtained according to the actual application scenario through the following methods: (1) User-defined input: In simulation applications, surface roughness parameters can be used as user-configurable input parameters and manually set according to the soil surface condition of the actual scenario. Based on empirical assignment of land surface type, typical empirical values ​​are obtained by looking up the table according to the soil type in the land cover classification map, as shown in Table 1.

[0033] Table 1 Soil Types and Corresponding Typical Empirical Values

[0034] (2) Calculation based on digital elevation model (DEM): If high-resolution DEM data is available, slope can be calculated through local window statistics.

[0035] set up For the first in the local window The slope angle of each pixel, If the total number of pixels in the window is , then: .

[0036] (3) Inversion based on remote sensing data: using multi-angle remote sensing observation data (such as MODIS BRDF products), roughness parameters are inverted by fitting the BRDF model.

[0037] This invention improves the accuracy of infrared radiation calculations by simulating the directional radiation characteristics of ground surfaces, avoiding the biases of the Lambertian body assumption. By introducing a directional emissivity model and a BRDF model, it realistically simulates the directional radiation characteristics of ground surfaces, including the hotspot effect of vegetation canopy and specular reflection from rough soil. This abandons the traditional Lambertian body assumption, significantly reduces radiation calculation errors under tilted observation conditions, and greatly improves the physical accuracy of the simulation.

[0038] In some optional embodiments, the directional reflection components of solar radiation and ambient radiation from the Earth's surface are calculated based on the BRDF model, including: assuming the directional reflection components of solar radiation and ambient radiation from the Earth's surface are... , The diffuse reflectance coefficient is... The specular reflection coefficient, For the angle of incidence at the zenith, The incident azimuth angle is... To observe the zenith angle, To observe the azimuth angle, The angle between the half-angle vector and the normal. For Fresnel terms, The normal Fresnel reflection coefficient, If the direction function is given, then the Fresnel term is expressed as: ; The direction function is expressed as: ; The directional reflection components of solar radiation and ambient radiation from the Earth's surface are then expressed as: .

[0039] Step 140: Extract the texture features of the visible light remote sensing image, perform texture detail modulation on the infrared radiation brightness, and obtain the modulated infrared radiation brightness.

[0040] In some optional embodiments, the infrared radiation brightness received by the sensor is calculated based on the equivalent temperature, directional emissivity, and directional reflection components, including: set up This is Planck blackbody radiation. For surface temperature, Let be Planck's constant. At the speed of light, Boltzmann's constant, If λ is the wavelength, then Planck's blackbody radiation is: ; Let the infrared radiation brightness received by the sensor be... , For the angle of incidence at the zenith, The incident azimuth angle is... To observe the zenith angle, To observe the azimuth angle, the directional reflection components of solar radiation and environmental radiation from the Earth's surface are: , For ambient radiant brightness, For a solid angle in a hemispherical space, the integral term is... Let the integral of environmental radiation over a hemispherical space be: .

[0041] In some optional embodiments, texture features of visible light remote sensing images are extracted, and texture detail modulation is applied to the infrared radiance to obtain modulated infrared radiance, including: Let the modulated infrared radiation brightness be , Texture features extracted from visible light images, is the x-coordinate of the pixel. Let be the ordinate of the pixel, then: ; set up The x-coordinate within the local window. The vertical coordinate within the local window. Coordinates of the visible light image within a local window grayscale value at that location The average gray level of the window. This represents the average grayscale value of the visible light image within a local window. For local windows, For local windows The total number of pixels contained within. Let be the modulation intensity coefficient. Then, the texture features extracted from the visible light image are represented as: .

[0042] High-resolution visible light imaging can enhance the realism and detail of infrared images.

[0043] Step 150: Calculate the modulated infrared radiance using a GPU parallel computing architecture, map the modulated infrared radiance into a grayscale image, and complete real-time rendering and output.

[0044] In some optional embodiments, mapping the modulated infrared radiance to a grayscale image includes: set up is the x-coordinate of the pixel. y is the ordinate of the pixel. The grayscale value of the grayscale image. This refers to the infrared radiation brightness after texture detail modulation. This represents the maximum value of the scene's radiant brightness. If the minimum scene radiance is given, then the grayscale value of the grayscale image is represented as: .

[0045] The computational tasks in steps 120-140 are distributed to multiple cores of the GPU for parallel execution to achieve real-time rendering (frame rate greater than or equal to 30 fps) and output the final infrared simulation image while meeting real-time requirements.

[0046] This invention parallelizes the entire computational process, including heat conduction calculation, radiation calculation, and texture modulation, through a GPU parallel computing architecture. While ensuring high-precision modeling of multi-physics coupling, it enables real-time simulation rendering, resolving the contradiction between the inability of high-precision models to be calculated in real time and the insufficient accuracy of fast models in existing technologies, thus achieving a balance between accuracy and real-time performance.

[0047] This invention modulates infrared radiation brightness by utilizing the texture features of high-resolution visible light images, fully exploring the application value of visible light information, enabling simulated infrared images to have ground feature texture details consistent with real scenes, solving the problems of texture loss and insufficient realism in existing simulated images, and significantly improving the realism of simulated images. This invention constructs a complete technical chain from temperature calculation and radiation calculation to texture enhancement and real-time rendering. It can be adapted to complex scenes composed of various land cover types such as vegetation, soil, water bodies and buildings, and can be widely used in multiple scenarios such as infrared system performance evaluation, operator simulation training and semi-physical simulation of infrared guidance systems.

[0048] Example 2 Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the method embodiments described above.

[0049] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0050] In some embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the methods described in the above embodiments.

[0051] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

[0052] Example 3 Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide electronic devices, such as those shown in the appendix. Figure 2 As shown, the electronic device may include, but is not limited to: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method.

[0053] In one alternative embodiment, an electronic device is provided, with... Figure 2 The illustrated electronic device 30 includes a processor 310 and a memory 330. The processor 310 and the memory 330 are connected, for example, via a bus 320.

[0054] Optionally, the electronic device 30 may further include a transceiver 340, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 340 is not limited to one type, and the structure of the electronic device 30 does not constitute a limitation on the embodiments of the present invention.

[0055] Processor 310 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application-Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or other programmable logic devices, hardware components, or any combination thereof. Processor 310 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, or a combination of a DSP and a microprocessor.

[0056] Bus 320 may include a pathway for transmitting information between the aforementioned components. Bus 320 may be a PCI peripheral component interconnect standard bus or an EISA extended industry standard architecture bus, etc. Bus 320 can be divided into control bus, data bus, address bus, etc. For ease of illustration, see attached... Figure 2 The character is represented by a single thick line, but this does not mean that there is only one bus or a type of bus.

[0057] The memory 330 may be a ROM read-only memory or other type of static storage device capable of storing static information and instructions, RAM random access memory or other type of dynamic storage device capable of storing information and instructions, or an EEPROM electrically erasable programmable read-only memory, a CD-ROM read-only optical disc or other optical disc storage, optical disc storage (including optical discs, laser discs, compressed optical discs, digital universal optical discs, etc.), a disk storage medium, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0058] The memory 330 is used to store application code (computer program) for executing the present invention, and its execution is controlled by the processor 310. The processor 310 is used to execute the application code stored in the memory 330 to implement the content shown in the foregoing method embodiments.

[0059] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A real-time simulation method for infrared radiation characteristics in complex scenarios, characterized in that, include: Acquire the geographic data, remote sensing data, and meteorological parameters required for the simulation; geographic data includes topographic data and land cover classification maps. Remote sensing data includes visible light remote sensing images; meteorological parameters include temperature, humidity, wind speed, and solar radiation. Based on the land cover classification map, the area proportion of each land cover component within each target pixel is calculated. The component temperature corresponding to each land cover component is solved according to the heat conduction equation and the surface energy balance boundary conditions. Based on the area proportion and component temperature of each land cover component, the equivalent temperature of the mixed pixel is obtained. The directional emissivity of each landform component in the observation direction is calculated based on topographic data and directional emissivity model. The directional reflection components of the land surface to solar radiation and environmental radiation are calculated based on BRDF model. The infrared radiation brightness received by the sensor is calculated based on equivalent temperature, directional emissivity and directional reflection components. Extract the texture features of visible light remote sensing images, modulate the texture details of infrared radiation brightness, and obtain the modulated infrared radiation brightness; The modulated infrared radiance is calculated using a GPU parallel computing architecture, and then mapped to a grayscale image for real-time rendering and output.

2. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, The component temperatures corresponding to the components of each land cover are determined based on the heat conduction equation and the surface energy balance boundary conditions, including: set up The coordinates are in the depth direction. For time, the corresponding component temperatures for each local component are: , For ground feature density, For the specific heat capacity of the earth's surface, For the first The thermal conductivity of the plant components is given by the heat conduction equation: ； The boundary conditions for surface energy balance are the surface energy balance equations, assuming... It is the surface albedo. Solar radiation, For downward longwave radiation, For surface temperature, Surface radiance, It is the Stefan-Boltzmann constant. For sensible heat flux, If the latent heat flux is used, then the surface energy balance equation is expressed as: 。 3. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, Based on the area proportions and component temperatures of each landform component, the equivalent temperature of the mixed pixel is obtained, including: synthesizing the component temperatures into the equivalent temperature of the mixed pixel based on a linear mixing model; assuming... The equivalent temperature of the mixed pixels. For the first The area ratio of crop types within a pixel. For the first The component temperature of the crop type is then expressed as the equivalent temperature of the mixed pixel as: 。 4. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, The directional emissivity of each landform component in the observation direction is calculated based on topographic data and a directional emissivity model, including: For vegetation, the canopy directional emissivity model is used to calculate the canopy directional emissivity in the observation direction, assuming the observation zenith angle is . , To observe the zenith angle Canopy directional emissivity in the direction of the canopy. The optical thickness of the canopy in the observation direction. The intrinsic emissivity of the blade. Let be the intrinsic emissivity of the soil, then the emissivity in the canopy direction is expressed as: ； For soil, a rough surface model is used to calculate the soil directional emissivity in the observation direction, assuming the soil directional emissivity is . , The normal emissivity of a smooth soil surface. Let be the soil surface roughness parameter, then the soil directional emissivity is expressed as: 。 5. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, The directional reflection components of solar and environmental radiation by the Earth's surface are calculated based on the BRDF model, including: assuming the directional reflection components of solar and environmental radiation by the Earth's surface are... , The diffuse reflectance coefficient is... The specular reflection coefficient, For the angle of incidence at the zenith, The incident azimuth angle is... To observe the zenith angle, To observe the azimuth angle, The angle between the half-angle vector and the normal. For Fresnel terms, The normal Fresnel reflection coefficient, If the direction function is given, then the Fresnel term is expressed as: ； The direction function is expressed as: ； The directional reflection components of solar radiation and ambient radiation from the Earth's surface are then expressed as: 。 6. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, The infrared radiation brightness received by the sensor is calculated based on the equivalent temperature, directional emissivity, and directional reflection components, including: set up This is Planck blackbody radiation. For surface temperature, is Planck's constant. At the speed of light, Boltzmann's constant, If λ is the wavelength, then Planck's blackbody radiation is: ； Let the infrared radiation brightness received by the sensor be... , For the angle of incidence at the zenith, The incident azimuth angle is... To observe the zenith angle, To observe the azimuth angle, the directional reflection components of solar radiation and environmental radiation from the Earth's surface are: , For ambient radiant brightness, For a solid angle in a hemispherical space, the integral term is... Let the integral of environmental radiation over a hemispherical space be: 。 7. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, Texture features are extracted from visible light remote sensing images, and texture detail modulation is performed on the infrared radiance to obtain the modulated infrared radiance, including: Let the modulated infrared radiation brightness be , Texture features extracted from visible light images, is the x-coordinate of the pixel. Let be the ordinate of the pixel, then: ； set up The x-coordinate within the local window. The vertical coordinate within the local window. Coordinates of the visible light image within a local window grayscale value at that location The average gray level of the window. This represents the average grayscale value of the visible light image within a local window. For local windows, For local windows The total number of pixels contained within. Let be the modulation intensity coefficient. Then, the texture features extracted from the visible light image are represented as: 。 8. The real-time simulation method for infrared radiation characteristics in complex scenarios according to claim 1, characterized in that, Mapping the modulated infrared radiation brightness to a grayscale image includes: set up is the x-coordinate of the pixel. y is the ordinate of the pixel. The grayscale value of the grayscale image. This refers to the infrared radiation brightness after texture detail modulation. This represents the maximum value of the scene's radiant brightness. If the minimum scene radiance is given, then the grayscale value of the grayscale image is represented as: 。 9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which is used to execute the real-time simulation method for infrared radiation characteristics in complex scenarios as described in any one of claims 1 to 8.

10. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the real-time simulation method for infrared radiation characteristics in complex scenarios as described in any one of claims 1 to 8.

Images