On-orbit remote sensing image simulation method, system, equipment and medium

Abstract

The invention relates to an in-orbit remote sensing image simulation method, system and device and a medium. The method comprises the following steps: constructing an in-orbit remote sensing image database for image simulation; the atmosphere is modeled, and the influence of the atmosphere on signal transmission is simulated; modeling the optical system, including the characteristics of the optical element and the influence of the optical element on imaging; modeling satellite platform vibration, and analyzing the influence of the satellite platform vibration on imaging stability; modeling is carried out on the detector, and response characteristics of the detector and signal conversion of the detector are considered; modeling the electronic part, including the influence of the signal processing circuit on the signal; and integrating the models of the links, simulating remote sensing imaging effects under different in-orbit working conditions, and performing remote sensing image evaluation. According to the invention, from two aspects of a modulation transfer function link and a radiation transmission link, various key links such as atmosphere, optics, a detector, electronics and platform vibration are modeled, and the two links are integrated to simulate remote sensing imaging effects under various working conditions.

Description

Technical Field

[0001] This disclosure belongs to the field of image simulation technology, and in particular relates to an on-orbit remote sensing image simulation method, system, device and medium. Background Technology

[0002] Full-link image simulation technology is one of the key technologies in the field of remote sensing. It predicts and evaluates the performance of remote sensing systems by simulating the entire imaging process from the Earth's surface to the atmosphere, sensors, and satellite orbit. This technology has a wide range of applications. During the satellite design phase, it can evaluate the performance of different design schemes, optimize satellite payload configuration, and help reduce project risks and increase success rates by identifying potential problems in the design phase. During the project initiation phase, simulation is used to demonstrate the feasibility of technical indicators and ensure the achievement of project goals. Before satellite launch, simulation data is used to conduct functional, performance, robustness, and stress tests on the ground system. During the on-orbit operation phase, satellite parameters are adjusted based on actual operating conditions to optimize imaging effects.

[0003] Simulation technology allows for comprehensive testing and evaluation of satellite systems without actual satellite launches, significantly reducing trial-and-error costs. Furthermore, during the design and development phases of new satellites, simulation technology can verify the effectiveness of new technologies and concepts, ensuring their feasibility.

[0004] Existing technical solution 1: Simulation method for the integrated response of spaceborne TDICCD camera MTF and SNR: To mathematically model the degradation mechanisms of MTF and SNR in various stages of dynamic imaging by a spaceborne TDI CCD camera, this paper explores signal description and MTF and SNR degradation simulation methods for each stage, using the mathematical models of MTF and SNR for each stage as nodes and the signal transmission and conversion process as the main thread. A mathematical simulation process for the comprehensive MTF and SNR response based on Fast Fourier Transform and a specific distribution random number generation algorithm is also presented. (This method relies heavily on the mathematical models of each stage and lacks connection to actual satellite parameters, thus lacking realism.) Existing technical solution 2: A remote sensing image simulation method based on integrated geometric radiometric sampling: This scheme simulates the impact of different state parameters on geometry during satellite remote sensing. Based on the observation geometry of the satellite / payload, the projected range of each pixel in the simulated remote sensing image on the ground is obtained. Using higher spatial resolution remote sensing data as a base map, after identifying ground objects, the bidirectional reflectance distribution function and reflectivity of various ground objects within the simulation range in typical bands are obtained. By solving the atmospheric radiative transfer equation and combining it with the observation angle, energy integration is performed within the pixel to obtain the radiative information of each pixel at the entrance pupil. Pixel-level geometric-radiative integrated sampling is carried out for each pixel in the simulated remote sensing image. The coverage area of ​​the output pixel on the base map, the optical point spread function, and the spectral response function of each band are integrated to simulate and obtain the simulated image. The simulated image of the remote sensor imaging is obtained through geometric-radiative integrated sampling technology. (This method focuses on end-to-end radiative degradation and geometric sampling.) Existing technical solution 3: An optical remote sensing image simulation method based on integrated radiation geometry design: This scheme establishes an imaging geometric model for each element of the detector based on simulated attitude data, orbital data, focal length and interior orientation elements of the optical system, detector size, integration time, and number of stages. For each element, the imaging geometric model calculates its three-dimensional coordinate matrix in the object-space coordinate system based on the elevation data of the simulated scene. The reflectance of each three-dimensional point in the object-space coordinate system is obtained from the reflectance data of the simulated scene. The entrance pupil radiance data of the simulated scene is calculated by calculating solar irradiance and atmospheric transmittance. The entrance pupil radiance data is then processed by the optical simulation system to obtain the energy reaching the detector. The detector energy is then processed by the detector simulation system to obtain the final simulated image. This invention simultaneously considers the relationship between geometric imaging and radiation imaging, achieving a high degree of simulation for space remote sensing optical imaging. (This method mainly considers the changes in radiation and geometric relationships of the satellite in orbit.)

[0005] Existing end-to-end image simulation technology has the following drawbacks: Single link: Most current full-link image simulations are conducted from a single perspective, such as MTF degradation or radiative transfer attenuation. Lack of realism: Most current end-to-end image simulations simulate image degradation based on theoretical formulas.

[0006] Therefore, it is necessary to provide a new method, system, device, and medium for simulating on-orbit remote sensing images to solve the above-mentioned technical problems. Summary of the Invention

[0007] The purpose of this disclosure is to provide an on-orbit remote sensing image simulation method, system, device, and medium to solve the above-mentioned problems.

[0008] This disclosure achieves the above objectives through the following technical solutions: An on-orbit remote sensing image simulation method includes the following steps: Construct an on-orbit remote sensing image database for image simulation; Model the atmosphere to simulate its impact on signal transmission; Modeling of the optical system includes analysis of optical component characteristics and simulation of the impact of the optical system on imaging; Model the vibration of the satellite platform and analyze its impact on imaging stability. Modeling the detector involves considering its response characteristics and signal conversion process, including the photoelectric conversion and sampling processes. Model the electronics components and simulate the effects of signal processing circuits on signals, including noise superposition and digital signal conversion; Multiple models were used to simulate the remote sensing imaging effects under different on-orbit conditions, and the simulated remote sensing images were evaluated.

[0009] As a further optimization of this disclosure, an on-orbit remote sensing image database for image simulation is constructed, including: The aerial images are normalized, and the asphalt road plots in each aerial image are extracted. The mean normalized DN value is calculated. The image reflectance is corrected according to the ratio of typical ground feature reflectance data to the mean value. The average reflectance images of the R, G, and B spectral bands of the aerial images are obtained. A lookup table for entrance pupil radiance data at different solar altitude angles, wavelengths, and reflectivities was exported from MOTRAN. Linear interpolation was performed on the reflectivity image to calculate the entrance pupil radiance data for each wavelength. Finally, the entrance pupil radiance was integrated according to the spectral range to obtain the entrance pupil radiance image for that spectral range. .

[0010] As a further optimization of this disclosure, atmospheric modeling is performed to simulate the influence of the atmosphere on signal transmission, including: Calculate the transfer function of atmospheric turbulence; Calculate the transfer function of atmospheric scattering; The entrance pupil radiance image is calculated based on the transfer function of atmospheric turbulence and the transfer function of atmospheric scattering. .

[0011] As a further optimization of this disclosure, the optical system is modeled, including optical component characteristic analysis and simulation of the impact of the optical system on imaging, including: For multispectral images, the center wavelength within the spectral range is selected, the optical system PSF is calculated using optical design software, and the OTF is obtained by performing a Fourier transform on the PSF. Entrance pupil radiance image Perform a Fourier transform, multiply by the OTF, multiply by the transmittance of the optical system, and then perform an inverse Fourier transform to obtain the simulated image of the optical system. For a panchromatic image, the center wavelengths of multiple narrow spectral bands are selected, and the PSF and OTF corresponding to each center wavelength are calculated. Fourier transform and inverse transform are performed on each group of entrance pupil radiance images, and the calculation results of each narrow spectral band are added together to obtain the simulation results of the panchromatic image.

[0012] As a further optimization of this disclosure, satellite platform vibration is modeled, and the impact of satellite platform vibration on imaging stability is analyzed, including: Calculate the transfer function of the detector pushbroom; Calculate the transfer function of the image shift matching residual; Calculate the transfer function of high-frequency vibration; The transfer function of low-frequency vibrations is calculated. When the amplitude of the low-frequency vibrations is less than a preset threshold, the MTF is used to describe image degradation. When the amplitude of the low-frequency vibrations is greater than the preset threshold, the blur kernel is obtained line by line based on the vibration path, and image degradation is simulated through convolution to obtain the entrance pupil radiance image. .

[0013] As a further optimization of this disclosure, the detector is modeled, taking into account the detector's response characteristics and signal conversion process, involving the detector's photoelectric conversion and sampling process, including: Photoelectric conversion is performed, and the number of signal electrons is calculated based on photoelectric conversion efficiency, detector center wavelength, optical system F number, Planck constant, speed of light, and entrance pupil radiance. The equivalent photosensitive size of the detector is calculated based on the measured MTF@Nyquist data of the detector, a convolution template is constructed, the convolution stride is determined based on the ground pixel resolution, and the detector sampling process is simulated.

[0014] As a further optimization of this disclosure, the electronic components are modeled to simulate the influence of signal processing circuits on signals, including noise superposition and digital signal conversion, including: Add shot noise, whose probability distribution follows a Poisson distribution; Add dark current noise, calculated based on the number of dark current electrons and integration time; Add readout noise, the probability distribution of which follows a Gaussian distribution; Based on gain and analog-to-digital conversion parameters, the signal electron count is converted into a DN value.

[0015] As a further optimization of this disclosure, multiple models are simulated to demonstrate the remote sensing imaging effects under different on-orbit operating conditions, and the simulated remote sensing images are evaluated, including: Dynamic transfer function analysis is used to calculate the overall MTF by multiplying the MTF of each stage, and to simulate and calculate the image-space and object-space modulation of the striped target image, and to plot the MTF curve. Signal-to-noise ratio (SNR) analysis: Calculate the SNR pixel by pixel, generate and display the SNR heatmap.

[0016] An on-orbit remote sensing image simulation system includes: The database construction module is used to build an on-orbit remote sensing image database for image simulation; The atmospheric modeling module is used to model the atmosphere and simulate its impact on signal transmission. The optical system modeling module is used to model optical systems, including optical component characteristic analysis and simulation of the impact of the optical system on imaging; The satellite platform vibration modeling module is used to model the vibration of the satellite platform and analyze the impact of the vibration on imaging stability. The detector modeling module is used to model the detector, taking into account the detector's response characteristics and signal conversion process, including the detector's photoelectric conversion and sampling process; The electronics modeling module models the electronics components and simulates the effects of signal processing circuits on signals, including noise superposition and digital signal conversion. The image evaluation module is used to simulate the remote sensing imaging effects of multiple models under different on-orbit operating conditions and to evaluate the simulated remote sensing images.

[0017] An electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; The processor is used to execute the program stored in the memory to implement the on-orbit remote sensing image simulation method.

[0018] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the on-orbit remote sensing image simulation method.

[0019] The beneficial effects of this disclosure are as follows: High simulation accuracy: By comprehensively considering the impact of factors such as optical system, atmospheric conditions, and platform motion on image quality, it can more realistically simulate the imaging effect of on-orbit remote sensing images.

[0020] Dynamic adjustment capability: It can flexibly adjust the technical indicators of ground features, attitude, atmosphere, and detectors, making it easy to optimize according to different operating conditions and requirements during the satellite design phase.

[0021] Verifying satellite imaging capabilities: After the optical system design is completed, the camera resolution and noise level can be considered in conjunction with the overall satellite performance, providing a strong basis for the overall satellite design scheme.

[0022] Determine the indicators of each satellite subsystem: Through simulation results, the technical indicators of each satellite subsystem can be determined more accurately, ensuring that the quality of remote sensing images meets user requirements. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method in an embodiment of this disclosure; Figure 2 This is a flowchart illustrating a specific method in an embodiment of this disclosure; Figure 3 This is a system structure block diagram of an embodiment of this disclosure; Figure 4 This is a block diagram of the device structure in an embodiment of this disclosure. Detailed Implementation

[0024] The present application will now be described in further detail with reference to the accompanying drawings. It should be noted that the following specific embodiments are only used to further illustrate the present application and should not be construed as limiting the scope of protection of the present application. Those skilled in the art can make some non-essential improvements and adjustments to the present application based on the above application content.

[0025] like Figure 1 As shown, an on-orbit remote sensing image simulation method includes the following steps: S1. Construct an on-orbit remote sensing image database for image simulation, such as... Figure 2 As shown, it includes: Step 1: Under defined operating conditions, the DN value of an image is directly proportional to its reflectance. Therefore, the image is normalized, and asphalt road patches are extracted from each image. The mean normalized DN value is calculated, and the image reflectance is corrected based on the ratio of typical ground feature reflectance data to the mean. This yields the average reflectance image of the R, G, and B spectral bands of the aerial image. This solves the problem that publicly available aerial images lack geometric and radiometric parameters, making it impossible to determine reflectance through observation of geometric and radiometric correction parameters.

[0026] Step 2: Export the entrance pupil radiance data lookup table from MOTRAN for different solar altitude angles, wavelengths (wavelength range 400-1000nm), and reflectivities (reflectivities of 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.7). Perform linear interpolation on the reflectivity image to calculate the entrance pupil radiance data for each wavelength. Integrate the entrance pupil radiance according to the spectral range to obtain the entrance pupil radiance image for that spectral range. .

[0027] S2. Model the atmosphere to simulate its impact on signal transmission, including: Step 1: Calculate the transfer function of atmospheric turbulence. For severe turbulence, k is set to 0.0025; for moderate turbulence, k is set to 0.001; and for low turbulence, k is set to 0.00025. The formula is as follows: ; Where u and v are frequencies, the MTF can be understood as a matrix, and u and v represent the matrix positions, which are not parameters.

[0028] Step 2: Calculate the atmospheric scattering transfer function, where K is the sky-to-background brightness ratio. Atmospheric attenuation coefficient ( ), Atmospheric transmittance, This represents the atmospheric path length.

[0029] ; 3) Calculate the entrance pupil radiance.

[0030] .

[0031] S3. Model the optical system, including optical component characteristic analysis and simulation of the optical system's impact on imaging, including: Step 1: For multispectral images, take the wavelength within its spectral range as the center wavelength and input the normalized field of view. and the center wavelength of the band The PSF of the optical system is calculated using optical design software; if system defocus is considered, the defocus amount is further input. The PSF after defocusing is obtained, and the OTF is obtained by performing a Fourier transform on the PSF: ; Step 2: Process the input entrance pupil radiance image Perform a Fourier transform, multiply by the OTF, perform an inverse Fourier transform, and then multiply by the transmittance of the optical system. This allows for the simulation of the overall response of the optical system. ; Step 3: For a panchromatic image, take the wavelength within each 50nm spectral range as the center wavelength. (Taking 450-700 as an example, take the wavelengths (475, 525, 575, 625, 575) set in the optical design within this range as the center wavelength of the 50nm spectral band, and calculate its corresponding PSF.) Repeat the process in step 1: ; Step 4: Process the input set of entrance pupil radiance images Perform a Fourier transform, multiply by the OTF, and then multiply by the transmittance of the optical system. After calculating the blocking ratio, an inverse Fourier transform is performed, and the calculation results of each narrow spectrum are summed to simulate the overall response of the optical system. .

[0032] S4. Model the vibration of the satellite platform and analyze its impact on imaging stability, including: Step 1: Calculate the push-broom transfer function of the detector. CCD pixels experience image shift during signal acquisition, and their push-broom MTF is as follows. If the charge transfer method is burst-type, then... If it is a continuous expression, then take denoted as the clock phase number, and 'a' as the detector pixel size.

[0033] ; 2) Step 2: Calculate the transfer function of the image shift matching residual, considering both the direction along the track and perpendicular to the track. M is the integral series. For charge transfer rate, This is the amount of mismatch. This represents the vertical track image shift mismatch.

[0034] ; Step 3: Calculate the transfer function of the high-frequency vibration, using the root mean square error of the vibration amplitude. The calculation is performed using the following formula: ; Step 4: Calculate the transfer function of low-frequency vibration: When the amplitude of low-frequency vibration is small: the image degradation caused by low-frequency vibration is described by MTF, where f is the focal length of the optical system, H is the orbital height, and A is the amplitude of the low-frequency vibration. For integration time, Let M be the oscillation period and M be the integral series.

[0035] The MTF of low-frequency vibration along the push-broom direction and lateral low-frequency vibration are as follows: ; ; ; ; When the low-frequency vibration amplitude is large, image degradation cannot be described solely by MTF. Because the vibration paths may differ across integration periods, the blur kernels generated for each row of the CCD based on the vibration path are also different. Therefore, the blur kernels of TDI vibration images exhibit spatial variability. For cases where the blur kernels for each row of the image are different, vibration blur kernel information must be acquired row by row. The sinusoidal vibrations of the camera perpendicular to the push-broom direction and along the push-broom direction can be represented as follows: ; ; The direction is perpendicular to the sweeping direction. The direction is along the sweeping direction. For the oscillation period, The integral time is used. The flutter path is obtained using the motion formula, and the image shift is obtained line by line using the flutter path, thus yielding the blur kernel. The information is then used to convolve the original clear image to obtain the final image. This process is represented by the following formula: .

[0036] S5. Model the detector, considering its response characteristics and signal conversion process, including the photoelectric conversion and sampling processes of the detector, including: Step 1: Perform photoelectric conversion. Let λ be the photoelectric conversion efficiency, λ be the center wavelength of the detector, F be the F-number of the optical system, and h be Planck's constant, h = 6.62 × 10⁻⁶. -34 c is the speed of light, c = 3 × 10 8 m / s, calculate the number of signal electrons received by the detector using the following formula. : ; Step 2: Calculate the equivalent photosensitive size of the detector based on the given MTF@Nyquist data. The equivalent photosensitive size is obtained by solving the following equation: ; The following convolution template is constructed: its size is the multiple of its equivalent photosensitive size relative to the equivalent pixel size of the input image. To effectively perform the summation of signal electrons, all elements within the convolution template are set to 1, and the outermost elements are set to the multiple residuals. The convolution stride is determined based on the ground pixel resolution. For example, if the ground pixel resolution of the input image is 0.05m, then if the ground pixel resolution is 0.5m, the convolution stride should be 10.

[0037] S6. Model the electronics section, simulating the impact of signal processing circuits on signals, including noise superposition and digital signal conversion, including: Step 1: Superimpose Poisson distributed shot noise. To satisfy the variance is Poisson distributed shot noise.

[0038] Step 2: Add dark current noise. The probability density of the dark current noise follows a variance of... The Poisson distribution has an integration time of . At that time, the number of electrons in the dark current is: ; Step 3: Add readout noise. The readout noise can be approximated by a Gaussian distribution, and its variance... It can be determined based on the number of readout noise electrons and the integral stage. calculate: ; ; Step 4: DN value conversion. The charge is finally output as a digital signal through processes such as gain conversion and analog-to-digital conversion. The detector conversion gain factor, or DN value, at this gain multiple is: .

[0039] S7. Simulate the remote sensing imaging effects under different on-orbit operating conditions using multiple models, and evaluate the simulated remote sensing images, including: Dynamic transfer analysis: Step 1: Under the condition that the spatial response is uncorrelated, the overall MTF response can be determined by the product of the MTFs of each component.

[0040] Therefore, based on the MTF of each component calculated above, the overall MTF response value at the Nyquist frequency can be given, providing the dynamic transfer function analysis results for the optical design stage.

[0041] Step 2: Perform image simulation on striped targets in different directions, calculate the image-side modulation and object-side modulation at different frequencies using the following formula, and then calculate the MTF of the simulated image. , The maximum and minimum brightness of the image stripes are represented, and MTF curves are plotted. These curves are then compared with the dynamic transfer function data results, and a data table is output. .

[0042] Signal-to-noise ratio analysis: The signal-to-noise ratio of each pixel is calculated pixel by pixel based on the simulated image. This represents the full-well charge count of the detector. G is the quantization bit depth of the detector, and G is the conversion gain.

[0043] A heat map is generated and displayed based on the calculation results.

[0044] like Figure 3 As shown, embodiments of this disclosure provide an on-orbit remote sensing image simulation system, including: The database construction module is used to build an on-orbit remote sensing image database for image simulation; The atmospheric modeling module is used to model the atmosphere and simulate its impact on signal transmission. The optical system modeling module is used to model optical systems, including optical component characteristic analysis and simulation of the impact of the optical system on imaging; The satellite platform vibration modeling module is used to model the vibration of the satellite platform and analyze the impact of the vibration on imaging stability. The detector modeling module is used to model the detector, taking into account the detector's response characteristics and signal conversion process, including the detector's photoelectric conversion and sampling process; The electronics modeling module models the electronics components and simulates the effects of signal processing circuits on signals, including noise superposition and digital signal conversion. The image evaluation module is used to simulate the remote sensing imaging effects of multiple models under different on-orbit operating conditions and to evaluate the simulated remote sensing images.

[0045] The implementation process of the functions and roles of each module in the above system is detailed in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0046] For the system embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0047] See Figure 4The electronic device provided in the embodiments of this disclosure includes a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120 and the memory 1130 communicate with each other through the communication bus 1140. Memory 1130 is used to store computer programs; The processor 1110 is used to execute the program stored in the memory 1130 to implement the above-described on-orbit remote sensing image simulation method. The aforementioned communication bus 1140 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0048] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0049] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory 1130 may also be at least one storage device located remotely from the aforementioned processor 1110.

[0050] Embodiments of this disclosure also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the on-orbit remote sensing image simulation method described above.

[0051] The embodiments described above are merely examples of several implementations of this disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent disclosure. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this disclosure, and these modifications and improvements all fall within the protection scope of this disclosure.

Claims

1. An on-orbit remote sensing image simulation method, characterized in that, Includes the following steps: Construct an on-orbit remote sensing image database for image simulation; Model the atmosphere to simulate its impact on signal transmission; Modeling of the optical system includes analysis of optical component characteristics and simulation of the impact of the optical system on imaging; Model the vibration of the satellite platform and analyze its impact on imaging stability. Modeling the detector involves considering its response characteristics and signal conversion process, including the photoelectric conversion and sampling processes. Model the electronics components and simulate the effects of signal processing circuits on signals, including noise superposition and digital signal conversion; Multiple models were used to simulate the remote sensing imaging effects under different on-orbit conditions, and the simulated remote sensing images were evaluated.

2. The on-orbit remote sensing image simulation method according to claim 1, characterized in that, Construct an on-orbit remote sensing image database for image simulation, including: The aerial images are normalized, and the asphalt road plots in each aerial image are extracted. The mean normalized DN value is calculated. The image reflectance is corrected according to the ratio of typical ground feature reflectance data to the mean value. The average reflectance images of the R, G, and B spectral bands of the aerial images are obtained. A lookup table for entrance pupil radiance data at different solar altitude angles, wavelengths, and reflectivities was exported from MOTRAN. Linear interpolation was performed on the reflectivity image to calculate the entrance pupil radiance data for each wavelength. Finally, the entrance pupil radiance was integrated according to the spectral range to obtain the entrance pupil radiance image for that spectral range. .

3. The on-orbit remote sensing image simulation method according to claim 1, characterized in that, Modeling the atmosphere and simulating its impact on signal transmission includes: Calculate the transfer function of atmospheric turbulence; Calculate the transfer function of atmospheric scattering; The entrance pupil radiance image is calculated based on the transfer function of atmospheric turbulence and the transfer function of atmospheric scattering. .

4. The on-orbit remote sensing image simulation method according to claim 2, characterized in that, Modeling the optical system, including optical component characteristic analysis and simulation of the optical system's impact on imaging, includes: For multispectral images, the center wavelength within the spectral range is selected, the optical system PSF is calculated using optical design software, and the OTF is obtained by performing a Fourier transform on the PSF. Entrance pupil radiance image Perform a Fourier transform, multiply by the OTF, multiply by the transmittance of the optical system, and then perform an inverse Fourier transform to obtain the simulated image of the optical system. For a panchromatic image, the center wavelengths of multiple narrow spectral bands are selected, and the PSF and OTF corresponding to each center wavelength are calculated. Fourier transform and inverse transform are performed on each group of entrance pupil radiance images, and the calculation results of each narrow spectral band are added together to obtain the simulation results of the panchromatic image.

5. The on-orbit remote sensing image simulation method according to claim 1, characterized in that, The vibration of the satellite platform is modeled and its impact on imaging stability is analyzed, including: Calculate the transfer function of the detector pushbroom; Calculate the transfer function of the image shift matching residual; Calculate the transfer function of high-frequency vibration; The transfer function of low-frequency vibrations is calculated. When the amplitude of the low-frequency vibrations is less than a preset threshold, the MTF is used to describe image degradation. When the amplitude of the low-frequency vibrations is greater than the preset threshold, the blur kernel is obtained line by line based on the vibration path, and image degradation is simulated through convolution to obtain the entrance pupil radiance image. .

6. The on-orbit remote sensing image simulation method according to claim 1, characterized in that, Modeling the detector, considering its response characteristics and signal conversion process, involves the detector's photoelectric conversion and sampling processes, including: Photoelectric conversion is performed, and the number of signal electrons is calculated based on photoelectric conversion efficiency, detector center wavelength, optical system F number, Planck constant, speed of light, and entrance pupil radiance. The equivalent photosensitive size of the detector is calculated based on the measured MTF@Nyquist data of the detector, a convolution template is constructed, the convolution stride is determined based on the ground pixel resolution, and the detector sampling process is simulated.

7. The on-orbit remote sensing image simulation method according to claim 1, characterized in that, Modeling the electronics components and simulating the impact of signal processing circuits on signals, including noise superposition and digital signal conversion, includes: Add shot noise, whose probability distribution follows a Poisson distribution; Add dark current noise, calculated based on the number of dark current electrons and integration time; Add readout noise, the probability distribution of which follows a Gaussian distribution; Based on gain and analog-to-digital conversion parameters, the signal electron count is converted into a DN value.

8. The on-orbit remote sensing image simulation method according to claim 1, characterized in that, Multiple models were used to simulate the remote sensing imaging effects under different on-orbit operating conditions, and the simulated remote sensing images were evaluated, including: Dynamic transfer function analysis is used to calculate the overall MTF by multiplying the MTF of each stage, and to simulate and calculate the image-space and object-space modulation of the striped target image, and to plot the MTF curve. Signal-to-noise ratio (SNR) analysis: Calculate the SNR pixel by pixel, generate and display the SNR heatmap.

9. An on-orbit remote sensing image simulation system, characterized in that, include: The database construction module is used to build an on-orbit remote sensing image database for image simulation; The atmospheric modeling module is used to model the atmosphere and simulate its impact on signal transmission. The optical system modeling module is used to model optical systems, including optical component characteristic analysis and simulation of the impact of the optical system on imaging; The satellite platform vibration modeling module is used to model the vibration of the satellite platform and analyze the impact of the vibration on imaging stability. The detector modeling module is used to model the detector, taking into account the detector's response characteristics and signal conversion process, including the detector's photoelectric conversion and sampling process; The electronics modeling module models the electronics components and simulates the effects of signal processing circuits on signals, including noise superposition and digital signal conversion. The image evaluation module is used to simulate the remote sensing imaging effects of multiple models under different on-orbit operating conditions and to evaluate the simulated remote sensing images.

10. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor is used to execute a program stored in a memory to implement the on-orbit remote sensing image simulation method according to any one of claims 1-8.

11. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the on-orbit remote sensing image simulation method according to any one of claims 1-8.

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