Large-breadth high-resolution infrared remote sensing simulation image generation method and simulation system

Abstract

The invention provides a large-breadth high-resolution infrared remote sensing simulation image generation method and a simulation system. The method comprises the following steps: acquiring remote sensing image data and corresponding elevation data according to a target resolution; obtaining an infrared texture according to the remote sensing image data; converting the elevation data into a terrain three-dimensional grid model; mapping the infrared texture into the terrain three-dimensional grid model to obtain a mapping result; obtaining a plurality of small-range scenes with different sizes from the mapping result, performing three-dimensional rendering on each small-range scene, and determining the block size of the large-format scene according to the rendering result; dividing a to-be-imaged large-format scene into a plurality of small areas according to the block size; scanning the areas in sequence according to a preset sequence, and rendering to obtain simulation images corresponding to the small areas; the simulation images corresponding to the small areas are spliced, the large-format high-resolution infrared remote sensing simulation image with the resolution being the target resolution is generated, and the problems that the data volume is increased sharply and the internal occupation is too large are solved in the mode that the small areas are scanned and then spliced.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a method and a simulation system for generating a large-format high-resolution infrared remote sensing simulation image. Background Art

[0002] The generation of large-format high-resolution infrared images can be used for more detailed and wider information in multiple fields such as military reconnaissance, disaster warning, ecological monitoring, virtual digital earth, etc. Therefore, it has become an important issue to study how to generate larger-format and higher-resolution infrared images under the limited computer and hardware resources.

[0003] Currently, the main methods for generating infrared remote sensing images are divided into the following three types: First, directly through infrared sensing devices such as thermal imagers, remote sensing satellites, etc., by capturing infrared thermal radiation signals to generate infrared images. Second, based on deep learning intelligent algorithms. Through the learning and training of intelligent networks with a large number of data sets, the required images are generated. Third, based on physical model modulation, using the correlation between the radiation characteristics of different bands, modulating the infrared texture that meets the requirements through the thermophysical characteristics of the ground object, and finally performing texture mapping to obtain the infrared remote sensing image. Among them, the latter two schemes are infrared remote sensing image data generated by computers, so they are all affected by computer and hardware resources.

[0004] The disadvantages of the first method mentioned above are that the cost of directly performing infrared remote sensing detection is very high, and only remote sensing data under one meteorological condition can be obtained in one detection. In addition, compared with visible light remote sensing detection, the image resolution obtained by infrared remote sensing detection is lower, and the interference and noise in the infrared detection band are more difficult to process than those in the visible light band. The main disadvantage of the second method mentioned above is that a large amount of training data is required, otherwise the generated images do not meet the expectations. Even if a large amount of data sets with high costs are used for learning and training, the physical credibility of the generated images is still unstable.

[0005] In the above-mentioned third method, early foreign scholars only used physical models for modulation to obtain infrared textures and generate images. However, the generated images had a single radiation characteristic directionality and poor randomness. The textures of the images showed strong periodicity, and the landform boundaries were straight, rigid, and unnatural. In the paper "Research on the Generation Method of Infrared Texture Modulation Templates Based on Remote Sensing Images" by Ji Kaiyue, the author used visible light remote sensing images with relatively low acquisition cost and difficulty to generate infrared textures, then modulated the infrared textures with visible light images, and finally imported them into the rendering engine to generate high-resolution infrared images. However, this method has a large amount of data and is restricted by the problem of insufficient computer hardware resources, so it is impossible to load a large amount of data resources. Therefore, it can only generate infrared simulation images with high resolution but small imaging range or infrared simulation images with large imaging range but low resolution, which is difficult to meet the demand for generating wide-area high-resolution infrared images. Moreover, for the problem of generating large-format infrared remote sensing simulation data, there is no complete integrated system and platform for generating simulation data and post-processing data. Summary of the Invention

[0006] To solve the above problems existing in the prior art, the present invention provides a method and a simulation system for generating large-format high-resolution infrared remote sensing simulation images, specifically including:

[0007] In a first aspect, the present invention provides a method for generating large-format high-resolution infrared remote sensing simulation images, including:

[0008] Obtain remote sensing image data and corresponding elevation data according to the target resolution;

[0009] Preprocess the remote sensing image data, and obtain infrared textures according to the preprocessed remote sensing image data;

[0010] After performing block processing on the elevation data, convert it into a terrain three-dimensional grid model;

[0011] Use texture mapping technology to map the infrared textures into the terrain three-dimensional grid model to obtain a mapping result;

[0012] Obtain multiple small-range scenes with different sizes from the mapping result, perform three-dimensional rendering on each small-range scene respectively, and determine the block size of the large-format scene according to the rendering result;

[0013] Divide the large-format scene to be imaged into multiple small regions according to the block size;

[0014] Scan each region in a preset order and render to obtain simulation images corresponding to each small region;

[0015] Stitch the simulation images corresponding to each small region to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution.

[0016] In a second aspect, the present invention further provides a large-format high-resolution infrared remote sensing simulation system, including:

[0017] An acquisition module, configured to acquire remote sensing image data and corresponding elevation data according to the target resolution;

[0018] An infrared texture generation module based on remote sensing images, configured to preprocess the remote sensing image data, and obtain infrared textures according to the preprocessed remote sensing image data; perform block processing on the elevation data and convert it into a terrain three-dimensional grid model; use texture mapping technology to map the infrared textures into the terrain three-dimensional grid model to obtain a mapping result;

[0019] A remote sensing scene simulation module, configured to obtain multiple small-range scenes of different sizes from the mapping result, perform three-dimensional rendering on each small-range scene respectively, and determine the block size of the large-format scene according to the rendering result; divide the large-format scene to be imaged into multiple small regions according to the block size; sequentially scan each region in a preset order and render to obtain simulation images corresponding to each small region;

[0020] An image stitching module, configured to stitch the simulation images corresponding to each small region to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution.

[0021] In a third aspect, the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus;

[0022] The memory is used to store a computer program;

[0023] The processor, when executing the program stored on the memory, implements any of the methods provided in the first aspect.

[0024] In a fourth aspect, the present invention provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, any of the methods provided in the first aspect is implemented.

[0025] In a fifth aspect, the present invention provides a program product, the program product includes computer program instructions, and when the computer program instructions are executed, any of the methods provided in the first aspect can be implemented.

[0026] Advantages of the present invention:

[0027] The method and system for generating large-format high-resolution infrared remote sensing simulation images provided by the present invention obtain remote sensing image data and corresponding elevation data according to the target resolution; preprocess the remote sensing image data, and obtain infrared textures based on the preprocessed remote sensing image data; perform block processing on the elevation data and convert it into a terrain three-dimensional grid model; use texture mapping technology to map the infrared textures into the terrain three-dimensional grid model to obtain a mapping result; obtain multiple small-range scenes of different sizes from the mapping result, perform three-dimensional rendering on each small-range scene respectively, and determine the block size of the large-format scene according to the rendering result; divide the large-format scene to be imaged into multiple small regions according to the block size; scan each region in a preset order and render to obtain the simulation images corresponding to each small region; splice the simulation images corresponding to each small region to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution. When loading resources, the limited device resources are fully utilized, and by means of time-division multiplexing, a large-format high-resolution infrared remote sensing image is generated by scanning small regions and then splicing. Compared with the prior art, the amount of resources loaded in the memory at the same time is reduced, and in the case of limited hardware device resources, the problems such as the sudden increase in the amount of calculated data and excessive memory occupation are avoided, and the problem of the calculation capacity limit faced in the process of loading a large-range high-resolution three-dimensional infrared radiation field during imaging simulation generation is solved at low cost.

[0028] The following will further elaborate on the present invention in conjunction with the accompanying drawings and embodiments. Description of the Drawings

[0029] Figure 1 It is a schematic flow chart of a method for generating large-format high-resolution infrared remote sensing simulation images provided by the present invention;

[0030] Figure 2 It is a schematic diagram of a rendering process provided by the present invention;

[0031] Figure 3 It is a schematic structural diagram of a large-format high-resolution infrared remote sensing simulation system provided by the present invention. Specific Embodiments

[0032] The following will further elaborate on the present invention in conjunction with specific embodiments, but the implementation manners of the present invention are not limited thereto.

[0033] The purpose of the present invention is to provide a method for generating high-resolution infrared simulation image data in a larger range under the general conditions of computers and hardware devices, meeting the requirements of wide-area monitoring and a large number of wide-area image data sets. At the same time, a complete large-format infrared remote sensing scene simulation system is established, with low coupling between working steps and strong system scalability, laying a foundation for the research of topics such as wide-area monitoring and large-format high-resolution infrared remote sensing scene simulation.

[0034] The method for generating large-format high-resolution infrared remote sensing simulation data provided by the present invention uses a time-division multiplexing method to generate large-format high-resolution infrared remote sensing image data. The main idea is divided into two parts. The first part first organizes multiple small-range high-resolution visible light images and elevations through slicing to generate corresponding infrared textures, imports them into a 3D rendering engine to first load the resources of the small-range scene and render, calculates and sets the detector's small viewport to scan this part of the scene, and saves the image. The second part is to divide the large-range scene to be generated into blocks, and then repeat the above steps until all small-range image data is generated. Finally, the generated small-range high-resolution scenes are stitched and edge-processed to finally obtain a large-format high-resolution infrared remote sensing image.

[0035] Figure 1 It is a schematic flowchart of a method for generating a large-format high-resolution infrared remote sensing simulation image provided by the present invention, as Figure 1 shown, the method includes:

[0036] S101. Obtain remote sensing image data and corresponding elevation data according to the target resolution.

[0037] The target resolution is the resolution of the large-format high-resolution infrared remote sensing simulation image to be generated.

[0038] Specifically, remote sensing image data and corresponding elevation data with the resolution level closest to the target resolution can be downloaded in satellite remote sensing image acquisition software such as Bigmap.

[0039] S102. Preprocess the remote sensing image data, and obtain infrared textures according to the preprocessed remote sensing image data.

[0040] Optionally, preprocessing the remote sensing image data includes: performing color homogenization on the textures in the obtained remote sensing image data with a color deviation greater than a preset threshold, and downsampling the visible light data in the obtained remote sensing image data according to actual application requirements to obtain the preprocessed remote sensing image data.

[0041] The resolution of the remote sensing image data can be improved through the above preprocessing.

[0042] Further optionally, obtaining infrared textures according to the preprocessed remote sensing image data includes the following steps a1-a4:

[0043] a1. Determine the distribution of typical landform features in the area corresponding to the preprocessed remote sensing image data according to the preprocessed remote sensing image data.

[0044] a2. Divide the area corresponding to the preprocessed remote sensing image data according to the distribution of typical geomorphic features in the area, and obtain the division result.

[0045] a3. Predict the radiation characteristics of each division result based on the ground object thermal characteristics prediction model, the measured spectral characteristics database of various ground object materials, and the division result.

[0046] a4. Obtain the infrared texture corresponding to the preprocessed remote sensing image data based on the preprocessed remote sensing image data, elevation data, and the radiation characteristics of each division result.

[0047] Specifically, based on the visible light remote sensing image, determine the typical geomorphic features existing in the area. In remote sensing image processing platforms such as Envi, divide different geomorphic types according to the geomorphic distribution in the visible light remote sensing image, clarify the accurate distribution positions of different ground object materials, and integrate the geomorphic distribution data of all scenes. Then, based on the ground object thermal characteristics prediction model and the measured spectral characteristics database of various ground object materials, predict the radiation characteristics of different ground objects under specific environmental conditions. Utilize the texture details of the visible light remote sensing image and elevation data information, and perform detailed modulation of the infrared texture according to altitude, adjacent material areas, etc., to generate a single-channel DDS texture with the same pixel size as the visible light remote sensing image, where the RGB and Alpha channels are the average temperature value, emissivity, radiance, and transparency respectively.

[0048] S103. After performing block processing on the elevation data, convert it into a terrain three-dimensional grid model.

[0049] Specifically, comprehensively consider the actual size of the scene, scale, and ground resolution and other actual requirements, use 3D modeling software such as 3DSMAX, adjust the parameter settings, reasonably block the obtained elevation data and convert it into a terrain three-dimensional grid model. According to the actual requirements and the resource loading capacity of the scene generation system, perform block processing on the three-dimensional grid model, and calibrate the coordinates of the local block grid according to the actual geospatial geographic coordinates corresponding to each block of terrain, and establish a coordinate system for the complete scene.

[0050] S104. Use texture mapping technology to map the infrared texture into the terrain three-dimensional grid model to obtain the mapping result.

[0051] Specifically, use texture mapping technology to complete the mapping of the infrared texture to the three-dimensional grid model in 3D modeling software such as 3DSMAX using the UVW mapping modifier.

[0052] S105. Obtain multiple small-range scenes with different sizes from the mapping result, perform three-dimensional rendering on each small-range scene respectively, and determine the block size of the large-format scene according to the rendering result.

[0053] Optionally, obtain small - scale scenes of multiple different sizes from the mapping result, perform three - dimensional rendering on each small - scale scene respectively, and determine the chunk size of the large - format scene according to the rendering result, including the following steps b1 and b2:

[0054] b1. Obtain small - scale scenes of multiple different sizes from the mapping result, and perform three - dimensional rendering on each small - scale scene respectively.

[0055] b2. Determine the chunk size of the large - format scene according to the memory occupancy and rendering frame rate of the system when rendering each small - scale scene.

[0056] Specifically, perform small - scale scene rendering test runs on the device in different sizes, and view the memory occupancy and rendering frame rate corresponding to each rough size. Under the condition of meeting the rendering frame rate, determine the chunk size of the large - format scene according to the test results based on the principle of maximizing resource utilization.

[0057] S106. Divide the large - format scene to be imaged into multiple small regions according to the chunk size.

[0058] Exemplarily, assume that it is required to scan a large - format scene with an area of 110km * 110km and a resolution of 1m. Since each row of the generated image will have 110,000 pixel values, the data volume is very large. It can be divided into 6 * 6 small regions with a minimum unit of 20km * 20km.

[0059] S107. Scan each region in sequence according to the preset order, and render the simulation image corresponding to each small region.

[0060] As Figure 2 shown, optionally, scan each region in sequence according to the preset order, and render the simulation image corresponding to each small region, including the following steps c1 and c2:

[0061] c1. Calibrate the coordinates of the large - format scene.

[0062] c2. Determine the coordinates of the detection positions corresponding to each small region according to the orbital parameters of the remote - sensing imaging detector and the scene coordinate calibration result. The optical axis of the detector corresponding to each small region points to the center position of the corresponding small region.

[0063] c3. Set the field - of - view angle corresponding to each small region according to the length and width of each small region.

[0064] c4. Move the detector between the detection positions corresponding to each small region in the order from left to right or from right to left, scan each region row by row, and render the simulation image corresponding to each small region.

[0065] Specifically, the obtained high-resolution visible light remote sensing images generally have specific coordinate calibrations and projection methods. Taking the remote sensing images obtained from Google Earth as an example, the WGS84 coordinate system is adopted, and the projection method is the Universal Transverse Mercator projection (UTM projection).

[0066] Relevant parameters of the Earth: At the equator, the radius Re = 6378137m, and at the poles, the radius Rp = 6356752m. Approximating the Earth as an ellipsoid, the flattening of the Earth is approximately:

[0067]

[0068] The first eccentricity of the Earth is approximately:

[0069] e 2 = 2f - f 2 ,

[0070] Calculate the radius of curvature N, which is the distance from a point on the ellipsoid to the center of the Earth. The calculation formula is:

[0071]

[0072] where is the latitude of the coordinate point.

[0073] In a 3D rendering engine, generally a longitude-latitude-elevation point is used as a reference point, and its coordinates in the 3D scene are (0, 0, H). Its coordinates (X, Y, Z) in the coordinate system with the center of the Earth as the center point can be calculated by the following formula, expressed as:

[0074]

[0075] For any other longitude-latitude point in the remote sensing image data, similarly, its coordinates (X′, Y′, Z′) in the coordinate system with the center of the Earth as the center point can be obtained. Furthermore, its corresponding coordinates (x, y, z) in the 3D scene can be obtained, expressed as:

[0076] x = X′ - X,

[0077] y = Y′ - Y,

[0078] z = Z′ - Z + H.

[0079] It can be seen that after setting the reference point, the relative coordinates of any point in the remote sensing image in the 3D simulation scene can be calculated by the above formula. Correspondingly, the coordinates of the detection positions corresponding to each small area can be determined by the above method.

[0080] S108. Stitch the simulation images corresponding to each small area to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution.

[0081] Optionally, stitching the simulation images corresponding to each small area to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution includes the following steps d1 - d3:

[0082] d1. Crop the part of the simulation image corresponding to each small area that does not contain the large-scale scene to obtain the cropped image.

[0083] d2. Perform image stitching row by row on the cropped image, and perform a weighted processing at the edge after each stitching to obtain the row stitching result with smooth image edges.

[0084] d3. Stitch the row stitching results to obtain a large-format high-resolution infrared remote sensing simulation image with the target resolution.

[0085] Specifically, since the detector parameters set for each scan are only different in position, the field of view angle and the number of pixels are the same, and the pixel values of the images generated by each scan are the same.

[0086] Furthermore, since the images at the scene edge may have parts that do not contain the scene to be simulated, the parts that do not contain the scene to be simulated need to be cropped first. Therefore, in the three-dimensional scene, the scene background can be set to a single color. Each pixel of the infrared texture image has a quantized gray value, and the gray values of the parts without the background are determined. The redundant parts can be cropped using traditional edge detection algorithms.

[0087] Furthermore, since each simulation image calculates and sets the detector azimuth and field of view angle through coordinate calibration, there is not much overlapping area between the images. The main error comes from the calculation method of coordinate calibration, and the smaller the generated scene area, the smaller this error. The images can be directly stitched row by row, and a simple weighted processing at the edge after each stitching can make the image edges transition smoothly. Finally, the images obtained by each row stitching are stitched together to obtain a large-format high-resolution infrared simulation image.

[0088] The method for generating a large-format high-resolution infrared remote sensing simulation image provided by the present invention includes: obtaining remote sensing image data and corresponding elevation data according to the target resolution; preprocessing the remote sensing image data, and obtaining an infrared texture based on the preprocessed remote sensing image data; performing block processing on the elevation data and converting it into a terrain three-dimensional grid model; using texture mapping technology to map the infrared texture into the terrain three-dimensional grid model to obtain a mapping result; obtaining multiple small-range scenes of different sizes from the mapping result, respectively performing three-dimensional rendering on each small-range scene, and determining the block size of the large-format scene according to the rendering result; dividing the large-format scene to be imaged into multiple small regions according to the block size; sequentially scanning each region in a preset order and rendering to obtain the simulation image corresponding to each small region; stitching the simulation images corresponding to each small region to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution. When loading resources, the method makes full use of limited device resources, and uses the time-division multiplexing method to generate a large-format high-resolution infrared remote sensing image by scanning small regions and then stitching. Compared with the prior art, the amount of resources loaded in the memory at the same time is reduced, and in the case of limited hardware device resources, the problems such as the sudden increase in the amount of calculated data and excessive memory occupation are avoided, and the problem of the calculation capacity limit faced in the process of loading a large-range high-resolution three-dimensional infrared radiation field during imaging simulation generation is solved at low cost.

[0089] Figure 3 FIG. is a schematic structural diagram of a large-format high-resolution infrared remote sensing simulation system provided by the present invention, as Figure 3 shown, the device includes:

[0090] An acquisition module 31, configured to obtain remote sensing image data and elevation data according to the target resolution.

[0091] A texture generation module 32 based on remote sensing images, configured to preprocess the remote sensing image data, and obtain an infrared texture based on the preprocessed remote sensing image data; perform block processing on the elevation data and convert it into a terrain three-dimensional grid model; use texture mapping technology to map the infrared texture into the terrain three-dimensional grid model to obtain a mapping result.

[0092] A remote sensing scene simulation module 33, configured to obtain multiple small-range scenes of different sizes from the mapping result, respectively perform three-dimensional rendering on each small-range scene, and determine the block size of the large-format scene according to the rendering result; divide the large-format scene to be imaged into multiple small regions according to the block size; sequentially scan each region in a preset order and render to obtain the simulation image corresponding to each small region.

[0093] An image stitching module 34, configured to stitch the simulation images corresponding to each small region to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution.

[0094] The large-format high-resolution infrared remote sensing simulation system provided by the present invention has low coupling and strong independence among modules, can be developed module by module, and can improve the secondary development rate by setting rich data interfaces.

[0095] The present invention also provides a structure of an electronic device, including a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus.

[0096] The memory is used to store computer programs.

[0097] The processor is used to implement the steps provided in the above method embodiments when executing the programs stored on the memory.

[0098] The communication interface is used for communication between the above electronic device and other devices.

[0099] The method provided by the embodiments of the present invention can be applied to electronic devices. Specifically, the electronic device can be: a desktop computer, a portable computer, a smart mobile terminal, a server, etc. There is no limitation here. Any electronic device that can implement the present invention belongs to the protection scope of the present invention.

[0100] The present invention also provides a computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps provided in the above method embodiments are implemented.

[0101] The present invention also provides a program product, which includes program instructions. When the program instructions are executed by a processor, the steps provided in the above method embodiments are implemented.

[0102] For the embodiments of the device / electronic device / storage medium / program product, since they are basically similar to the method embodiments, the description is relatively simple. For the specific content, beneficial effects, and other related aspects, please refer to the partial description of the method embodiments.

[0103] The terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0104] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention pertains, without departing from the concept of the present invention, several simple deductions or substitutions can still be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for generating a large-format high-resolution infrared remote sensing simulation image, characterized in that, Including: Obtain remote sensing image data and corresponding elevation data according to the target resolution; Preprocess the remote sensing image data, and obtain infrared texture according to the preprocessed remote sensing image data; Perform block processing on the elevation data and convert it into a terrain three-dimensional grid model; Use texture mapping technology to map the infrared texture into the terrain three-dimensional grid model to obtain a mapping result; Obtain multiple small-range scenes of different sizes from the mapping result, perform three-dimensional rendering on each small-range scene respectively, and determine the block size of the large-format scene according to the rendering result; Divide the large-format scene to be imaged into multiple small regions according to the block size; Scan each of the regions in a preset order and render to obtain a simulation image corresponding to each small region; Stitch the simulation images corresponding to each small region to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution.

2. The method according to claim 1, characterized in that, The preprocessing of the remote sensing image data includes: Perform color homogenization on the texture in the obtained remote sensing image data with a color deviation greater than a preset threshold, and downsample the visible light data in the obtained remote sensing image data according to actual application requirements to obtain the preprocessed remote sensing image data.

3. The method according to claim 2, wherein The obtaining of the infrared texture according to the preprocessed remote sensing image data includes: Determine the distribution of typical geomorphic features in the area corresponding to the preprocessed remote sensing image data according to the preprocessed remote sensing image data; Divide the area corresponding to the preprocessed remote sensing image data according to the distribution of typical geomorphic features in the area corresponding to the preprocessed remote sensing image data to obtain a division result; Predict the radiation characteristics of each division result according to the ground object thermal characteristics prediction model, the spectral characteristic database measured for various ground object materials, and the division result; Obtain the infrared texture corresponding to the preprocessed remote sensing image data according to the preprocessed remote sensing image data, the elevation data, and the radiation characteristics of each division result.

4. The method according to claim 3, wherein The obtaining of multiple small-range scenes of different sizes from the mapping result, performing three-dimensional rendering on each small-range scene respectively, and determining the block size of the large-format scene according to the rendering result includes: Obtain multiple small-range scenes of different sizes from the mapping result, and perform three-dimensional rendering on each small-range scene respectively; Determine the block size of the large-format scene according to the memory occupancy and rendering frame rate of the system when rendering each small-range scene.

5. The method according to claim 4, wherein The scanning of each of the regions in a preset order and rendering to obtain a simulation image corresponding to each small region includes: Perform coordinate calibration on the large-format scene; Determine the coordinates of the detection positions corresponding to each small region according to the remote sensing imaging detector orbit parameters and the scene coordinate calibration result, and the optical axis of the detector corresponding to each small region points to the center position of the corresponding small region; Set the field of view angle corresponding to each small region according to the length and width of each small region. Move the detector between the detection positions corresponding to each of the small regions in the order from left to right or from right to left, scan each of the regions line by line, and render the simulation images corresponding to each of the small regions.

6. The method according to claim 5, wherein The step of stitching the simulation images corresponding to each of the small regions to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution includes: Crop the parts of the simulation images corresponding to each of the small regions that do not contain a large-scale scene to obtain the cropped images; Perform image stitching on the cropped images line by line, and perform a weighted processing at the edge after each stitching to obtain a line stitching result with smooth image edges; Stitch each of the line stitching results to obtain the large-format high-resolution infrared remote sensing simulation image with the target resolution.

7. A large-format high-resolution infrared remote sensing simulation system, characterized in that, It includes: An acquisition module, configured to acquire remote sensing image data and corresponding elevation data according to the target resolution; An infrared texture generation module based on remote sensing images, configured to preprocess the remote sensing image data, and obtain an infrared texture according to the preprocessed remote sensing image data; perform block processing on the elevation data and convert it into a terrain three-dimensional grid model; use texture mapping technology to map the infrared texture into the terrain three-dimensional grid model to obtain a mapping result; A remote sensing scene simulation module, configured to obtain multiple small-scale scenes with different sizes from the mapping result, perform three-dimensional rendering on each of the small-scale scenes respectively, and determine the block size of the large-scale scene according to the rendering result; Divide the large-scale scene to be imaged into multiple small regions according to the block size; scan each of the regions in a preset order and render the simulation images corresponding to each of the small regions; An image stitching module, configured to stitch the simulation images corresponding to each of the small regions to generate a large-format high-resolution infrared remote sensing simulation image with the target resolution.

8. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus; The memory is used to store computer programs; The processor is configured to implement the method according to any one of claims 1-6 when executing the program stored on the memory.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the method according to any one of claims 1-6.

10. A program product, characterized in that, The program product includes computer program instructions, and when the computer program instructions are executed, they can implement the method according to any one of claims 1-6.

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