Method and system for selecting index parameters of satellite-borne hyperspectral load

Abstract

The invention provides a satellite-borne hyperspectral load index parameter selection method and system, and the method comprises the steps: obtaining an application scene of a target satellite-borne hyperspectral load, and determining a target spectral band range corresponding to the target satellite-borne hyperspectral load based on a preset application scene-spectral band corresponding relation; based on the application scene and the target spectral band range, selecting at least two to-be-tested index parameter groups from the preset index parameter groups; simulating preset scene data based on a preset simulation method, and determining simulation data corresponding to each to-be-tested index parameter group; and determining the to-be-tested index parameter group corresponding to the simulation data meeting the preset evaluation standard as a target index parameter group. In the mode, a plurality of key parameters of the spaceborne hyperspectral load can be simulated and evaluated, so that the index parameters of the spaceborne hyperspectral load are selected more accurately and efficiently, and the actual application requirements are met.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing technology, and in particular to a method and system for selecting index parameters of a satellite-borne hyperspectral payload. Background Art

[0002] Satellite-borne hyperspectral payloads have made significant progress in recent years and have become an important part of space remote sensing technology. They are widely used in agricultural monitoring, mineral exploration, environmental protection, military reconnaissance and other fields. However, the current satellite-borne hyperspectral imaging technology faces a high cost problem, which limits its widespread application. There are two main existing processing methods: one is to design based on the optimal indicators of hardware implementation. Although it can achieve the highest performance, it has high costs and large technical barriers, which is not conducive to the low-cost and miniaturization of hyperspectral payloads; the other is to simulate according to the spectral characteristics and signal-to-noise ratio in specific application scenarios. Although it can balance performance and cost, it does not consider key factors such as spatial resolution and data accuracy, and the signal-to-noise ratio processing is relatively rough. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method and system for selecting index parameters of a satellite-borne hyperspectral payload, which can simulate and evaluate multiple key parameters of the satellite-borne hyperspectral payload, so that the index parameter selection of the satellite-borne hyperspectral payload is more accurate, efficient and meets the actual application requirements. At the same time, the performance of the satellite-borne hyperspectral payload can be guaranteed at a low cost.

[0004] In a first aspect, an embodiment of the present invention provides a method for selecting index parameters of a satellite-borne hyperspectral payload, comprising: obtaining an application scenario of a target satellite-borne hyperspectral payload, and determining a target spectral band range corresponding to the target satellite-borne hyperspectral payload based on a preset application scenario-spectral band correspondence; based on the application scenario and the target spectral band range, selecting at least two index parameter groups to be tested from preset index parameter groups; the preset index parameter groups include a spatial resolution to be tested, a spectral resolution to be tested, a signal-to-noise ratio to be tested, and an accuracy to be tested; simulating preset scenario data based on a preset simulation method, and determining simulation data corresponding to each index parameter group to be tested; determining the index parameter group to be tested corresponding to the simulation data that meets the preset evaluation criteria as the target index parameter group.

[0005] Furthermore, the scene data includes data spatial resolution, data spectral resolution, imaging altitude, surface reflectivity and solar altitude information; the preset simulation method includes a spatial resampling method; based on the preset simulation method, the preset scene data is simulated to determine the simulation data corresponding to each parameter group to be measured, including: obtaining the data spatial resolution corresponding to the scene data; based on the surface reflectivity and the spatial resampling method, converting the data spatial resolution corresponding to the scene data into the spatial resolution to be measured corresponding to each parameter to be measured, so as to obtain the first simulated surface reflectivity corresponding to each pixel in the scene data.

[0006] Furthermore, the preset simulation method also includes a spectral resampling method; based on the preset simulation method, the preset scene data is simulated to determine the steps of simulation data corresponding to each group of index parameters to be measured, including: obtaining the data spectral resolution corresponding to the scene data; based on the first simulated surface reflectivity and the spectral resampling method, the data spectral resolution corresponding to the scene data is converted into the spectral resolution to be measured corresponding to each index parameter to be measured, so as to obtain a second simulated surface reflectivity.

[0007] Furthermore, the preset simulation method also includes an atmospheric correction and conversion method; based on the preset simulation method, the preset scene data is simulated to determine the simulation data corresponding to each parameter group to be measured, including: inputting the imaging altitude, the second simulated surface reflectivity and the solar altitude angle information into the preset atmospheric radiation transmission model to obtain the atmospheric parameters corresponding to the scene data; the atmospheric parameters include the albedo of the large balloon surface, the path radiation, the downlink radiation and the uplink radiation; by using the following formula (1), the surface reflectivity and the atmospheric parameters, the first simulated entrance pupil radiation brightness corresponding to each parameter group to be measured is calculated; Among them, L * is the first simulated entrance pupil radiance; ρ is the second simulated surface reflectivity; S is the albedo of the large balloon surface; F is the process radiation; d is the downlink radiation; T is the uplink radiation.

[0008] Furthermore, the preset simulation method also includes a noise superposition method; based on the preset simulation method, the preset scene data is simulated to determine the simulation data corresponding to each parameter group to be measured, including: obtaining the spectral band-signal-to-noise ratio corresponding rule corresponding to the target spectral band range; the spectral band-signal-to-noise ratio corresponding rule is used to characterize the correspondence between a preset spectral band and a preset signal-to-noise ratio in the target spectral band range; based on the spectral band-signal-to-noise ratio corresponding rule, a normalized signal-to-noise ratio simulation curve is fitted; based on the signal-to-noise ratio simulation curve and the measured signal-to-noise ratio corresponding to each parameter group to be measured, the noise standard deviation corresponding to each parameter group to be measured is determined; based on each noise standard deviation and the preset normal distribution method, the Gaussian noise corresponding to each parameter group to be measured in each preset spectral band is calculated; each Gaussian noise is superimposed on the corresponding first simulated entrance pupil radiation brightness to obtain a second simulated entrance pupil radiation brightness.

[0009] Furthermore, the preset simulation method also includes a precision superposition method; based on the preset simulation method, the preset scene data is simulated to determine the simulation data corresponding to each parameter group of the indicator to be measured, including: based on the preset normal distribution method, determining the precision deviation value corresponding to each parameter group of the indicator to be measured in each preset spectral band; superimposing each precision deviation value on the second simulated entrance pupil radiation brightness to obtain the target simulated entrance pupil radiation brightness.

[0010] Furthermore, the preset simulation method also includes a step of simulating the preset scene data based on the preset simulation method by the atmospheric correction method, and determining the simulation data corresponding to each parameter group of the index to be measured, including: determining the target simulation ground object reflectivity based on the following formula (2) and the target simulation entrance pupil radiation brightness; determining the target simulation entrance pupil radiation brightness and the target simulation ground object reflectivity as simulation data; formula (2) is: Among them, ρ' is the reflectivity of the target simulated object, L * 'Simulate the entrance pupil radiance for the target.

[0011] Furthermore, after simulating the pre-set scene data based on the preset simulation method and determining the simulation data corresponding to each parameter group of the index to be measured, the method also includes: inputting each simulation data into the preset quantitative inversion model respectively to obtain the probability of occurrence of the scene object corresponding to each simulation data; comparing the probability of occurrence of each scene object with the scene object corresponding to the scene data to obtain the comparison result corresponding to each simulation data; the comparison result is used to characterize the accuracy of the probability of occurrence of the scene object.

[0012] Furthermore, the step of determining the group of indicator parameters to be tested corresponding to the simulation data that meets the preset evaluation criteria as the target indicator parameter group includes: determining the performance evaluation result corresponding to each indicator parameter group to be tested based on the preset performance evaluation criteria and the comparison result corresponding to each simulation data; obtaining the preset cost evaluation criteria and the preset cost data corresponding to each indicator parameter group to be tested, and determining the cost evaluation result corresponding to each indicator parameter group to be tested; determining the group of indicator parameters to be tested whose performance evaluation result is performance compliance and whose cost evaluation result is cost compliance as the target indicator parameter group.

[0013] In the second aspect, an embodiment of the present invention provides an indicator parameter selection system for a satellite-borne hyperspectral payload, including: a target spectral band range determination module, used to obtain the application scenario of the target satellite-borne hyperspectral payload, and determine the target spectral band range corresponding to the target satellite-borne hyperspectral payload based on a preset application scenario-spectral band correspondence; a module for setting an indicator parameter group to be tested, used to select at least two indicator parameter groups to be tested from the preset indicator parameter group based on the application scenario and the target spectral band range; the preset indicator parameter group includes a spatial resolution to be tested, a spectral resolution to be tested, a signal-to-noise ratio to be tested, and an accuracy to be tested; a simulation module, used to simulate preset scenario data based on a preset simulation method, and determine the simulation data corresponding to each indicator parameter group to be tested; a target indicator parameter group determination module, used to determine the indicator parameter group to be tested corresponding to the simulation data that meets the preset evaluation criteria as the target indicator parameter group.

[0014] The embodiment of the present invention provides a method and system for selecting index parameters of a satellite-borne hyperspectral payload, including: obtaining the application scenario of the target satellite-borne hyperspectral payload, and determining the target spectral band range corresponding to the target satellite-borne hyperspectral payload based on the preset application scenario-spectral band correspondence relationship; based on the application scenario and the target spectral band range, selecting at least two index parameter groups to be tested from the preset index parameter group; the preset index parameter group includes the spatial resolution to be tested, the spectral resolution to be tested, the signal-to-noise ratio to be tested, and the accuracy to be tested; simulating the preset scene data based on the preset simulation method, and determining the simulation data corresponding to each index parameter group to be tested; determining the index parameter group to be tested corresponding to the simulation data that meets the preset evaluation criteria as the target index parameter group. In this way, multiple key parameters of the satellite-borne hyperspectral payload can be simulated and evaluated, so that the index parameter selection of the satellite-borne hyperspectral payload is more accurate, efficient and meets the actual application requirements. At the same time, the performance of the satellite-borne hyperspectral payload can be guaranteed while the cost is low.

[0015] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0016] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flow chart of a method for selecting index parameters of a satellite-borne hyperspectral payload provided in Embodiment 1 of the present invention;

[0019] Figure 2 A flow chart of a method for determining simulation data when the preset simulation method provided in the first embodiment of the present invention is a spatial resampling method;

[0020] Figure 3 A flow chart of a method for determining simulation data when the preset simulation method provided in the first embodiment of the present invention is a spectrum resampling method;

[0021] Figure 4 A flow chart of a method for determining simulation data when the preset simulation method provided in the first embodiment of the present invention is a noise superposition method;

[0022] Figure 5 A flow chart of a method for determining a target indicator parameter group provided in Embodiment 1 of the present invention;

[0023] Figure 6 The first simulated surface reflectivity at different spatial resolutions to be measured provided by the first embodiment of the present invention;

[0024] Figure 7 The second simulated surface reflectance at different spectral resolutions to be measured provided in the first embodiment of the present invention;

[0025] Figure 8 The second simulated entrance pupil radiance under different signal-to-noise ratios to be measured provided in the first embodiment of the present invention;

[0026] Fig. 9 The simulated entrance pupil radiance of the target at different accuracies to be measured provided by the first embodiment of the present invention;

[0027] Fig.10 The change curve when the signal-to-noise ratio is 350 provided in the first embodiment of the present invention;

[0028] Fig.11Schematic diagram of the indicator parameter selection system for the satellite-borne hyperspectral payload provided in the second embodiment of the present invention.

[0029] Icons: 1-target spectral band range determination module; 2-test index parameter group setting module; 3-simulation module; 4-target index parameter group determination module. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The development of satellite-borne hyperspectral payloads has made significant progress in recent decades and has become an important part of space remote sensing technology. The imaging principle of satellite-borne hyperspectral payloads is to achieve fine spectral analysis by acquiring spectral information of target objects in multiple continuous bands. Advanced satellite-borne hyperspectrometers can cover multiple bands from visible light to short-wave infrared and even long-wave infrared, have high spectral resolution and spatial resolution, and can provide rich information about objects. These payloads are widely used in agricultural monitoring, mineral resource exploration, environmental protection, military reconnaissance and other fields, greatly promoting the development and application of related disciplines. In the future, satellite-borne hyperspectral payloads will play a more important role in the field of space remote sensing, providing more accurate and efficient means for earth science research and resource management.

[0032] There are currently two main methods for demonstrating the parameters of satellite-borne hyperspectral indicators: the first method is to design based on the optimal indicators achieved by hardware, such as improving the hardware requirements such as spatial resolution, spectral resolution, and signal-to-noise ratio at the same time to optimize the performance of the payload; the second method is to simulate the spectral characteristics of the material and the angle of signal-to-noise ratio in a specific application scenario, and propose some indicators for the camera payload. The signal-to-noise ratio in the existing technology is simulated using a normalized value. The detection efficiency of satellite-borne hyperspectral payloads is different in different bands, so the signal-to-noise ratio is also different in each band. Simulating using a normalized value will affect the quality of the indicator parameters and the inversion results when demonstrating the indicator parameters.

[0033] The embodiment of the present application provides a method for selecting index parameters of a satellite-borne hyperspectral payload, which can simulate and evaluate multiple key parameters of the satellite-borne hyperspectral payload, making the index parameter selection of the satellite-borne hyperspectral payload more accurate, efficient and in line with actual application requirements. At the same time, the performance of the satellite-borne hyperspectral payload can be guaranteed at low cost.

[0034] To facilitate understanding of this embodiment, the embodiment of the present invention is described in detail below.

[0035] Embodiment 1:

[0036] Figure 1 This is a flow chart of a method for selecting index parameters of a satellite-borne hyperspectral payload provided in Example 1 of the present invention.

[0037] Reference Figure 1 , the index parameter selection method of the satellite-borne hyperspectral payload includes:

[0038] Step S101 , obtaining an application scenario of a target satellite-borne hyperspectral payload, and determining a target spectral band range corresponding to the target satellite-borne hyperspectral payload based on a preset application scenario-spectral band correspondence relationship.

[0039] Here, the application scenarios can be harmful gas monitoring, vegetation assessment, agricultural product quality assessment and geological exploration, etc.

[0040] The application scenario-spectral band correspondence is pre-set according to the actual situation. For example, when the application scenario is geological exploration, the purpose of using the satellite-borne hyperspectral payload is to find different altered minerals (such as alunite, kaolinite, muscovite, chlorite and aluminum mica, etc.). The spectral characteristics of the above mineral categories are mainly in the band range of 0.4-2.5μm, that is, the geological exploration field is determined to be associated with the spectral band range of 0.4-2.5μm and saved in the application scenario-spectral band correspondence.

[0041] Here, the satellite-borne hyperspectral payload includes satellite-borne hyperspectral instruments and sensors.

[0042] Step S102, based on the application scenario and the target spectral band range, at least two indicator parameter groups to be measured are selected from the preset indicator parameter groups; the preset indicator parameter groups include the spatial resolution to be measured, the spectral resolution to be measured, the signal-to-noise ratio to be measured and the accuracy to be measured.

[0043] Here, the preset index parameter group is pre-set according to actual conditions, and in this embodiment is set with reference to the public data of AVIRIS (Airborne Visible InfraRed Imaging Spectrometer) of the United States.

[0044] The specific values ​​set in each parameter group to be tested are different. The purpose of selecting multiple parameter groups to be tested is to simulate the performance of the load under different parameter groups to be tested. By comparing the imaging and spectral effects under these different parameter groups to be tested, it is possible to evaluate which group of parameters best meets the actual application requirements and project budget.

[0045] Step S103, simulating the preset scenario data based on a preset simulation method to determine the simulation data corresponding to each index parameter group to be measured.

[0046] Here, a scene database is pre-set, and a plurality of historical scene data are stored in the scene database. At least two historical scene data are selected from the scene database as scene data based on the application scenario.

[0047] The scene data includes data spatial resolution, data spectral resolution, imaging altitude, surface reflectivity and solar altitude information. Among them, the surface reflectivity is the surface reflectivity of the current pixel.

[0048] The scene database pre-stores historical scene data captured by multiple sensors, where all the scene data are small-area high-quality data.

[0049] The imaging altitude and local solar altitude information in the historical scene data are used for the conversion of signal-to-noise ratio and entrance pupil radiation brightness. The historical scene data should have better spatial resolution, spectral resolution and signal-to-noise ratio. Poor data quality is not convenient for subsequent spatial and spectral resampling data processing.

[0050] Specifically, if the satellite-borne hyperspectral payload data is to be applied to geological exploration, then airborne or ground-based data should be collected in areas rich in mineral categories to facilitate subsequent verification.

[0051] According to the preset simulation method and the index parameter group to be tested, each scene data is simulated respectively to obtain the simulation data corresponding to each index parameter group to be tested. In each index parameter group to be tested, one scene data corresponds to one simulation data.

[0052] The preset simulation methods include spatial resampling method, spectral resampling method, atmospheric correction and conversion method, noise superposition method, precision superposition method and atmospheric correction method.

[0053] Reference Figure 2 When the preset simulation method is the spatial resampling method, the steps of step S103 include:

[0054] Step S201, obtaining the data space resolution corresponding to the scene data.

[0055] Step S202, based on the surface reflectance and the spatial resampling method, the data spatial resolution corresponding to the scene data is converted into the measured spatial resolution corresponding to each measured index parameter to obtain the first simulated surface reflectance corresponding to each pixel in the scene data.

[0056] Here, the spatial resampling method can be nearest neighbor interpolation, bilinear interpolation, bicubic interpolation, etc. Based on the spatial resampling method, each pixel value of the data spatial resolution is recalculated to match the spatial resolution to be measured. The resampling process will stretch or compress the data spatially to achieve the conversion from the data spatial resolution to the spatial resolution to be measured.

[0057] Specifically, the application scenario is geological exploration, the scene data is mining area data with a spatial resolution of 1 meter, and the spatial resolution to be measured is 10 meters. That is, the 1-meter resolution data needs to be resampled to 10-meter resolution to simulate the imaging effect of the load at a 10-meter resolution.

[0058] Every 10m x 10m area will calculate a new pixel value from the 100 pixels of the scene data through bilinear interpolation. This process is equivalent to weighted calculation of the average value of all 1m pixels within a 10m range to generate a pixel representing a 10m resolution.

[0059] In one embodiment, when the preset simulation method is the spectrum resampling method, refer to Figure 3 The steps of step S103 include:

[0060] Step S301, obtaining the data spectral resolution corresponding to the scene data.

[0061] Step S302: based on the first simulated surface reflectance and the spectral resampling method, the data spectral resolution corresponding to the scene data is converted into the spectral resolution to be measured corresponding to each index parameter to be measured, so as to obtain the second simulated surface reflectance.

[0062] Here, the spectrum resampling method can be linear interpolation, cubic interpolation, and convolution-based spectrum matching methods. Based on the spectrum resampling method, the spectrum bands of the data spectral resolution are recalculated and adjusted to the spectral resolution to be measured. For example, if the spectral resolution to be measured is low, multiple bands will be merged or smoothed; if the spectral resolution to be measured is high, the spectral intervals will be filled by interpolation methods to generate more subdivided bands.

[0063] Specifically, the scene data has a spectral resolution of 1 nanometer, and the spectral resolution to be measured is 10 nanometers, that is, the data with a resolution of 1 nanometer needs to be resampled to a resolution of 10 nanometers to simulate the performance of the load at a resolution of 10 nanometers.

[0064] The target band value for each 10 nm is generated by calculating the average or weighted average of the spectral resolution of the 1 nm data. This is equivalent to aggregating the 1 nm high-resolution spectral data into a wider 10 nm band to generate a second simulated surface reflectance after spectral resampling.

[0065] In one embodiment, when the preset simulation method is the atmospheric correction and conversion method, the steps of step S103 include:

[0066] The imaging altitude, the second simulated surface reflectivity and the solar altitude angle information are input into a preset atmospheric radiation transmission model to obtain the atmospheric parameters corresponding to the scene data; the atmospheric parameters include the large balloon surface albedo, path radiation, downlink radiation and uplink radiation.

[0067] Here, the scene data also includes information on meteorological and atmospheric conditions in the target area, such as atmospheric aerosol type, solar altitude angle, observation geometry, surface type, etc.

[0068] The preset atmospheric radiation transfer model may be a 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model.

[0069] Set the input parameters of the 6S model, including solar spectrum, observation geometry, atmospheric aerosol model, gas concentration, etc. The 6S model will output the corresponding atmospheric parameters, including surface reflectivity, large balloon surface albedo, path radiation, downlink radiation, and uplink radiation.

[0070] Among them, the path radiation is the radiance value of the atmospheric path radiation entering the sensor, the downlink radiation is the downlink radiation from the sun to the ground, the uplink radiation is the uplink radiation from the ground to the sensor, and the pupil radiation brightness is the pupil radiation brightness of a single pixel received at the sensor.

[0071] The first simulated entrance pupil radiation brightness corresponding to each parameter group to be measured is calculated by the following formula (1), the surface reflectivity and the atmospheric parameters.

[0072]

[0073] Among them, L * is the first simulated entrance pupil radiance; ρ is the second simulated surface reflectivity; S is the albedo of the large balloon surface; F is the process radiation; d is the downlink radiation; T is the uplink radiation.

[0074] Specifically, the first simulated entrance pupil radiation brightness corresponding to a scene data is 0.3, which is at mid-latitude. The atmospheric parameters obtained by the 6S model are as follows: The path radiation is 10 W·m -2 ·sr -1 μm -1 , downlink radiation is 100W·m -2 μm -1 , the upward radiation is 0.8, and the albedo of the large balloon surface is 0.1.

[0075] Based on formula (1), the following formula (3) is obtained:

[0076]

[0077] In one embodiment, the radiance obtained by the short-wave infrared band instrument mainly comes from the characteristic of ground reflected radiation, and the noise generated during the payload imaging process is superimposed on the signal value to simulate the actual signal value.

[0078] When the preset simulation method is the noise superposition method, refer to Figure 4 The steps of step S103 include:

[0079] Step S401, obtaining a spectral band-signal-noise ratio correspondence rule corresponding to a target spectral band range; the spectral band-signal-noise ratio correspondence rule is used to characterize a correspondence between a preset spectral band and a preset signal-noise ratio in the target spectral band range.

[0080] Here, the satellite-borne hyperspectral payload has different signal-to-noise ratios in different spectral bands. By fitting this characteristic, a normalized signal-to-noise ratio simulation curve can be obtained.

[0081] Step S402: Based on the spectral band-signal-to-noise ratio correspondence rule, a normalized signal-to-noise ratio simulation curve is obtained by fitting.

[0082] Here, the signal-to-noise ratio simulation curve is a normalized curve (a curve with a signal-to-noise ratio of 1) obtained by fitting the actual signal-to-noise ratio data in different bands of reference satellites (such as EnMAP (German hyperspectral satellite) and PRISMA (satellite managed and operated by the Italian Space Agency). The signal-to-noise ratio simulation curve represents the relative level of the signal-to-noise ratio of each band and is a reference standard for calculating noise.

[0083] Step S403 , determining the noise standard deviation corresponding to each index parameter group to be measured based on the signal-to-noise ratio simulation curve and the measured signal-to-noise ratio corresponding to each index parameter group to be measured.

[0084] Here, during the data simulation process, the signal-to-noise ratio simulation curve is used to adjust the noise adding method. Specifically, according to the signal-to-noise ratio to be measured in each parameter group to be measured, combined with the signal-to-noise ratio curve, the amount of noise to be added to each spectral band is determined.

[0085] For example, if the signal-to-noise ratio curve shows that the signal-to-noise ratio of a certain band is low, then in the simulated data, the noise of this band will be relatively increased to make the data more consistent with the noise level in actual observations.

[0086] Taking the signal-to-noise ratio of a certain band as a benchmark, the specific signal-to-noise ratio value of the band is set, and then the noise standard deviation of each band is obtained.

[0087] The calculation formula is shown in the following formula (4):

[0088]

[0089] Where S(λ) is the first simulated entrance pupil radiance under specific conditions (such as the ground reflection signal under specific hemispherical albedo and solar zenith angle conditions), λ is the central wavelength of the band, σ(λ) is the noise standard deviation of the band, and SNR(λ) is the signal-to-noise ratio to be measured.

[0090] Specifically, the signal-to-noise ratio of a short-wave infrared band (λ = 1600 nm) of the satellite-borne hyperspectral payload is 100. After fitting the signal-to-noise ratio curve, the first simulated entrance pupil radiance under specific observation conditions is measured to be S(λ) = 500 W·m -2 ·sr -1 μm -1 .

[0091] According to formula (4), the noise standard deviation can be calculated as shown in the following formula (5):

[0092]

[0093] Step S404: Based on each noise standard deviation and a preset normal distribution method, the Gaussian noise corresponding to each index parameter group to be measured in each preset spectral band is calculated.

[0094] Here, in each band, the 2 ) with Gaussian noise added to the normal distribution.

[0095] Specifically, in the wavelength band λ = 1600nm, the generated 2 ) is the Gaussian noise value.

[0096] Step S405 , superimposing each Gaussian noise onto the corresponding first simulated entrance pupil radiance to obtain a second simulated entrance pupil radiance.

[0097] Specifically, each Gaussian noise is superimposed on the corresponding first simulated entrance pupil radiation brightness. For example, the generated noise value is -3.2, which changes randomly during the simulation. The noise value is superimposed on the first simulated entrance pupil radiation brightness to obtain the second simulated entrance pupil radiation brightness, S'(λ) = 500 + (-3.2) = 496.8 W·m -2 ·sr -1 μm -1 , where S'(λ) is the second simulated entrance pupil radiance.

[0098] In one embodiment, when the preset simulation method is the precision superposition method, the steps of step S103 include:

[0099] Based on the preset normal distribution method, the accuracy deviation value corresponding to each parameter group of the index to be measured in each preset spectral band is determined.

[0100] Each precision deviation value is added to the second simulated entrance pupil radiance to obtain the target simulated entrance pupil radiance.

[0101] Here, in order to simulate the accuracy deviation of the satellite-borne hyperspectral payload caused by factors such as radiation calibration error and atmospheric correction error in actual observation, the target simulated entrance pupil radiation brightness is determined according to the precision superposition method.

[0102] According to the set precision P to be measured and the preset normal distribution method, a normal distribution (0, P 2 ), this process simulates the randomness and distribution characteristics of the precision error in the data. The precision deviation value simulates the random error of the load caused by various error sources in actual observation.

[0103] After randomization, in the band λ1,λ2...λ n The errors are P1, P2...P n ,The generated accuracy deviation value is superimposed on the second simulated entrance pupil radiance of each band to obtain the target simulated entrance pupil radiance containing the accuracy error.

[0104] Specifically, the accuracy to be measured is P=1%. Through random normal division, the accuracy P(1200)=1.5% is randomly obtained in the band λ=1200 nm.

[0105] For this band, assume that the second simulated entrance pupil radiance of the current band is S'(1200) = 300 W·m -2 ·sr -1 μm -1 .

[0106] The target simulated entrance pupil radiance can be obtained by adding the precision deviation value to the second simulated entrance pupil radiance, S”(λ)=300*(1+0.015)=304.5W·m -2 ·sr -1 μm -1 , where S”(λ) is the target simulated entrance pupil radiance.

[0107] In one embodiment, when the preset simulation method is the atmospheric correction method, the steps of step S103 include:

[0108] The target simulated ground object reflectivity is determined based on the following formula (2) and the target simulated entrance pupil radiance.

[0109] The target simulated entrance pupil radiation brightness and the target simulated ground object reflectivity are determined as simulation data.

[0110] Formula (2) is as follows:

[0111]

[0112] Among them, ρ' is the reflectivity of the target simulated object, L * 'Simulate the entrance pupil radiance for the target.

[0113] Here, the atmospheric correction method is used to remove the influence of the atmosphere in the acquired data, and the target simulated entrance pupil radiation brightness is converted into the target simulated ground object reflectivity. Among them, the target simulated ground object reflectivity is usually between 0 and 1. If it exceeds 1 in the actual calculation, the input data quality needs to be checked.

[0114] In one embodiment, after step S103, the method further includes:

[0115] Each simulation data is input into the preset quantitative inversion model to obtain the occurrence probability of the scene object corresponding to each simulation data.

[0116] Here, the scene object is the object captured by the scene data in the application scenario. For example, in the agricultural detection scenario, the scene objects are vegetation, soil, and water bodies; in the mineral exploration scenario, the scene objects are alunite, kaolinite, muscovite, chlorite, and aluminum mica. The quantitative inversion model is used to invert the probability of the scene object appearing at a pixel point.

[0117] Among them, the quantitative inversion model is trained by the following method:

[0118] The occurrence probability of the scene object corresponding to each historical scene data is obtained; the historical scene data is used as input and the occurrence probability of the scene object is used as output to train the neural network model until the preset training conditions are met to obtain a trained quantitative inversion model.

[0119] The occurrence probability of each scene object is compared with the scene object corresponding to the scene data to obtain a comparison result corresponding to each simulation data; the comparison result is used to characterize the accuracy of the occurrence probability of the scene object.

[0120] Step S104, determining the to-be-tested indicator parameter group corresponding to the simulation data that meets the preset evaluation standard as the target indicator parameter group.

[0121] In one embodiment, referring to Figure 5 , the steps of step S104 include:

[0122] Step S501, based on the preset performance evaluation standard and the comparison result corresponding to each simulation data, determine the performance evaluation result corresponding to each index parameter group to be measured.

[0123] Here, the preset performance evaluation standard is pre-set according to actual conditions, and may be a set accuracy threshold. The performance evaluation result is performance compliance or performance non-compliance.

[0124] The comparison results corresponding to each scene data in each indicator parameter to be measured are compared with the accuracy threshold, and the performance evaluation results of the indicator parameter to be measured whose comparison results are within the accuracy threshold are determined to be performance compliance.

[0125] Step S502 , obtaining a preset cost evaluation standard and preset cost data corresponding to each indicator parameter group to be measured, and determining a cost evaluation result corresponding to each indicator parameter group to be measured.

[0126] Here, the preset cost evaluation standard is preset according to actual conditions, and can be a set cost threshold. The cost evaluation result is cost compliance or cost non-compliance.

[0127] In the preset indicator parameter groups, each indicator parameter group to be measured is pre-set with corresponding preset cost data.

[0128] The preset cost data corresponding to each indicator parameter to be measured is compared with the cost threshold, and the cost evaluation result of the indicator parameter to be measured within the cost threshold is determined to be cost compliance.

[0129] Step S503, determining the indicator parameter group to be tested whose performance evaluation result is performance compliance and whose cost evaluation result is cost compliance as the target indicator parameter group.

[0130] Here, the parameter group of the indicator to be tested that meets the requirements in both performance and cost is determined as the target parameter group of the indicator to be tested. If there is more than one parameter group of the indicator to be tested that meets the requirements in both performance and cost, the performances of different parameter groups of the indicator to be tested are compared, and the parameter group of the indicator to be tested with better performance is selected as the target parameter group of the indicator to be tested.

[0131] In one embodiment, a target satellite-borne hyperspectral payload is applied to a geological exploration scenario to find different altered minerals. The corresponding target spectral band range is determined to be 0.4-2.5 μm. Referring to Table 1, four groups of index parameters to be measured are set.

[0132] Table 1 Setting table of parameter groups of indicators to be tested

[0133] Parameter group of indicators to be tested Group 1 Group 2 Group 3 Group 4 Spatial resolution to be measured 5m 10m 15m 15m Spectral resolution to be measured 10nm 10nm 10nm 15nm Signal-to-noise ratio to be measured 350 350 350 350 Accuracy to be measured 6％ 6％ 3％ 1％

[0134] Based on the preset simulation method, the preset scenario data is simulated to determine the simulation data corresponding to each parameter group of the index to be tested. Figure 6-Figure 9 , respectively showing the simulation data obtained based on a preset simulation method for a scene data.

[0135] Figure 6The following figure shows the imaging effects of converting scene data into spatial resolutions of 5m, 10m and 15m respectively according to the spatial resampling method from left to right. The imaging effect is generated based on the first simulated surface reflectivity.

[0136] Figure 7 The second simulated surface reflectance curve corresponding to each spectral resolution is shown when the spectral resolution is 6.03nm, 10nm and 15nm, among which blue is the second simulated surface reflectance curve corresponding to the spectral resolution of 6.03nm, orange is the second simulated surface reflectance curve corresponding to the spectral resolution of 10nm, and green is the second simulated surface reflectance curve corresponding to the spectral resolution of 15nm. The second simulated surface reflectance curve is used to compare the impact of spectral data at different resolutions on the details of the observed target. Spectral resolution usually refers to the minimum wavelength difference between two adjacent spectral lines that a spectrometer can distinguish, and the unit is nanometers (nm). It reflects the detail resolution capability of the satellite-borne high-spectral payload: the smaller the spectral resolution, the finer the details that can be resolved, and the better the separation of close spectral features. Reference Figure 7 The curve with a spectral resolution of 6.03nm represents the highest resolution and can capture more spectral details; the resolutions of 10nm and 15nm are lower, the curve will be smoother, and more details will be lost.

[0137] Figure 8 The second simulated entrance pupil radiation brightness curve under different signal-to-noise ratios (SNRs) are shown, where blue is the second simulated entrance pupil radiation brightness curve without adding noise, orange is the second simulated entrance pupil radiation brightness curve when the signal-to-noise ratio is 550, green is the second simulated entrance pupil radiation brightness curve when the signal-to-noise ratio is 350, and red is the second simulated entrance pupil radiation brightness curve when the signal-to-noise ratio is 150. The higher the signal-to-noise ratio, the clearer the signal and the less noise; the lower the signal-to-noise ratio, the greater the noise and the worse the signal quality. Figure 8 The signal-to-noise ratio simulation curve shows the changes in spectral signals under different signal-to-noise ratio conditions. The lower the SNR, the more obvious the fluctuation and noise of the curve.

[0138] Fig. 9 The target simulated pupil radiation brightness curve after superimposing the accuracy is shown, among which the blue is the target simulated pupil radiation brightness curve without adding accuracy, the orange is the target simulated pupil radiation brightness curve with 1% accuracy, the green is the target simulated pupil radiation brightness curve with 3% accuracy, and the red is the target simulated pupil radiation brightness curve with 6% accuracy. The higher the accuracy percentage, the more obvious the deviation of the curve from the original data, which is manifested as increased volatility of the data points or the emergence of systematic errors. Fig. 9 The changes in measurement data at different accuracies and their impact on the quality of spectral data are demonstrated.

[0139] The shape of the signal-to-noise ratio simulation curve is fitted according to the signal-to-noise ratio shape of Italian PRISMA. The signal-to-noise ratio in Table 1 represents the signal-to-noise ratio at the central wavelength of 2.4 μm under the conditions of ground reflectivity of 0.5 and solar altitude angle of 60°.

[0140] Fig.10 It shows the curve of the signal-to-noise ratio of 350, that is, the signal-to-noise ratio of 350 at the central wavelength of 2.4μm under the conditions of ground reflectivity of 0.5 and solar altitude angle of 60°, showing the change of signal-to-noise ratio in different bands. Among them, the ordinate is the value of the signal-to-noise ratio, and the abscissa is the range of the band. In some bands, the signal-to-noise ratio is high (such as bands around 1.0-1.3 and 1.6-2.0), indicating that the signal quality is good and the noise is low in these bands. In some bands, the signal-to-noise ratio drops sharply (such as bands around 1.4 and 1.9), which means that the signals in these bands are greatly interfered by noise and the signal quality is poor. The overall signal-to-noise ratio level is 350, but the specific value fluctuates in each band, and the signal-to-noise ratio levels of different bands are quite different.

[0141] Each simulation data is input into the preset quantitative inversion model respectively, and the occurrence probability of the scene object corresponding to each simulation data is obtained, and the occurrence probability of each scene object is compared with the scene object corresponding to the scene data to obtain the comparison result corresponding to each simulation data. The embodiment of the present invention provides an index parameter selection method for a satellite-borne hyperspectral payload, including: obtaining the application scenario of the target satellite-borne hyperspectral payload, and determining the target spectral band range corresponding to the target satellite-borne hyperspectral payload based on the preset application scenario-spectral band correspondence relationship; based on the application scenario and the target spectral band range, selecting at least two index parameter groups to be tested from the preset index parameter group; the preset index parameter group includes the spatial resolution to be tested, the spectral resolution to be tested, the signal-to-noise ratio to be tested, and the accuracy to be tested; simulating the preset scene data based on the preset simulation method, and determining the simulation data corresponding to each index parameter group to be tested; the index parameter group to be tested corresponding to the simulation data that meets the preset evaluation criteria is determined as the target index parameter group. In this way, multiple key parameters of the satellite-borne hyperspectral payload can be simulated and evaluated, so that the index parameter selection of the satellite-borne hyperspectral payload is more accurate, efficient and meets the actual application requirements. At the same time, the performance of satellite-borne hyperspectral payloads can be guaranteed at low cost.

[0142] Embodiment 2:

[0143] Fig.11 Schematic diagram of the indicator parameter selection system for the satellite-borne hyperspectral payload provided in the second embodiment of the present invention.

[0144] Reference Fig.11 , the index parameter selection system of the satellite-borne hyperspectral payload includes:

[0145] The target spectral band range determination module 1 is used to obtain the application scenario of the target satellite-borne hyperspectral payload, and determine the target spectral band range corresponding to the target satellite-borne hyperspectral payload based on a preset application scenario-spectral band correspondence relationship.

[0146] The module 2 for setting the index parameter group to be measured is used to select at least two index parameter groups to be measured from the preset index parameter groups based on the application scenario and the target spectral band range; the preset index parameter groups include the spatial resolution to be measured, the spectral resolution to be measured, the signal-to-noise ratio to be measured and the accuracy to be measured.

[0147] The simulation module 3 is used to simulate the preset scenario data based on a preset simulation method to determine the simulation data corresponding to each index parameter group to be measured.

[0148] The target indicator parameter group determination module 4 is used to determine the indicator parameter group to be measured corresponding to the simulation data that meets the preset evaluation standard as the target indicator parameter group.

[0149] In one embodiment, the preset simulation method includes a spatial resampling method, and the simulation module 3 is further used for:

[0150] Get the data space resolution corresponding to the scene data.

[0151] Based on the surface reflectance and the spatial resampling method, the data spatial resolution corresponding to the scene data is converted into the measured spatial resolution corresponding to each index parameter to be measured, so as to obtain the first simulated surface reflectance corresponding to each pixel in the scene data.

[0152] In one embodiment, the preset simulation method includes a spectrum resampling method, and the simulation module 3 is further used for:

[0153] Get the data spectral resolution corresponding to the scene data.

[0154] Based on the first simulated surface reflectance and the spectral resampling method, the data spectral resolution corresponding to the scene data is converted into the spectral resolution to be measured corresponding to each index parameter to be measured, so as to obtain the second simulated surface reflectance.

[0155] In one embodiment, the preset simulation method includes an atmospheric correction and conversion method, and the simulation module 3 is further used for:

[0156] The imaging altitude, the second simulated surface reflectivity and the solar altitude angle information are input into a preset atmospheric radiation transmission model to obtain the atmospheric parameters corresponding to the scene data; the atmospheric parameters include the large balloon surface albedo, path radiation, downlink radiation and uplink radiation.

[0157] The first simulated entrance pupil radiation brightness corresponding to each parameter group to be measured is calculated by the following formula (1), the surface reflectivity and the atmospheric parameters.

[0158]

[0159] Among them, L * is the first simulated entrance pupil radiance; ρ is the second simulated surface reflectivity; S is the albedo of the large balloon surface; F is the process radiation; d is the downlink radiation; T is the uplink radiation.

[0160] In one embodiment, the preset simulation method includes a noise superposition method, and the simulation module 3 is further used for:

[0161] A spectral band-signal-to-noise ratio corresponding rule corresponding to the target spectral band range is obtained; the spectral band-signal-to-noise ratio corresponding rule is used to characterize the corresponding relationship between a preset spectral band and a preset signal-to-noise ratio in the target spectral band range.

[0162] Based on the spectral band-SNR correspondence rule, the normalized SNR simulation curve was fitted.

[0163] Based on the signal-to-noise ratio simulation curve and the measured signal-to-noise ratio corresponding to each measured index parameter group, the noise standard deviation corresponding to each measured index parameter group is determined.

[0164] Based on each noise standard deviation and a preset normal distribution method, the Gaussian noise corresponding to each index parameter group to be measured in each preset spectral band is calculated.

[0165] Each Gaussian noise is superimposed on the corresponding first simulated entrance pupil radiance to obtain a second simulated entrance pupil radiance.

[0166] In one embodiment, the preset simulation method includes a precision superposition method, and the simulation module 3 is further used for:

[0167] Based on the preset normal distribution method, the accuracy deviation value corresponding to each parameter group of the index to be measured in each preset spectral band is determined.

[0168] Each precision deviation value is added to the second simulated entrance pupil radiance to obtain the target simulated entrance pupil radiance.

[0169] In one embodiment, the preset simulation method includes an atmospheric correction method, and the simulation module 3 is further used for:

[0170] The target simulated ground object reflectivity is determined based on the following formula (2) and the target simulated entrance pupil radiance.

[0171] The target simulated entrance pupil radiation brightness and the target simulated ground object reflectivity are determined as simulation data.

[0172] Formula (2) is:

[0173]

[0174] Among them, ρ' is the reflectivity of the target simulated object, L * 'Simulate the entrance pupil radiance for the target.

[0175] In one embodiment, the target indicator parameter group determination module 4 is further used to:

[0176] Each simulation data is input into the preset quantitative inversion model to obtain the occurrence probability of the scene object corresponding to each simulation data.

[0177] The occurrence probability of each scene object is compared with the scene object corresponding to the scene data to obtain a comparison result corresponding to each simulation data; the comparison result is used to characterize the accuracy of the occurrence probability of the scene object.

[0178] In one embodiment, the target indicator parameter group determination module 4 is further used to:

[0179] Based on the preset performance evaluation standard and the comparison result corresponding to each simulation data, the performance evaluation result corresponding to each index parameter group to be tested is determined.

[0180] Obtain the preset cost evaluation standard and the preset cost data corresponding to each indicator parameter group to be measured, and determine the cost evaluation result corresponding to each indicator parameter group to be measured.

[0181] The indicator parameter group to be tested whose performance evaluation result is performance compliance and whose cost evaluation result is cost compliance is determined as the target indicator parameter group.

[0182] The embodiment of the present invention provides an index parameter selection system for a satellite-borne hyperspectral payload, in which multiple key parameters of the satellite-borne hyperspectral payload can be simulated and evaluated, so that the index parameter selection of the satellite-borne hyperspectral payload is more accurate, efficient and meets the actual application requirements. At the same time, the performance of the satellite-borne hyperspectral payload can be guaranteed at a low cost.

[0183] The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the method described in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0184] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0185] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0186] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0187] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0188] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for selecting index parameters of satellite-borne hyperspectral payload, characterized in that: include: Acquire an application scenario of a target satellite-borne hyperspectral payload, and determine a target spectral band range corresponding to the target satellite-borne hyperspectral payload based on a preset application scenario-spectral band correspondence relationship; Based on the application scenario and the target spectral band range, selecting at least two index parameter groups to be measured from the preset index parameter groups; The preset index parameter group includes the spatial resolution to be measured, the spectral resolution to be measured, the signal-to-noise ratio to be measured and the accuracy to be measured; Simulate the preset scenario data based on a preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be tested; The index parameter group to be measured corresponding to the simulation data that meets the preset evaluation standard is determined as the target index parameter group.

2. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 1, characterized in that: The scene data includes data spatial resolution, data spectral resolution, imaging altitude, surface reflectivity and solar altitude information; the preset simulation method includes a spatial resampling method; The step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured includes: Acquire the data spatial resolution corresponding to the scene data; Based on the surface reflectivity and the spatial resampling method, the data spatial resolution corresponding to the scene data is converted into the measured spatial resolution corresponding to each of the measured index parameters to obtain a first simulated surface reflectivity corresponding to each pixel in the scene data.

3. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 2, characterized in that: The preset simulation method also includes a spectrum resampling method; The step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured includes: Acquire the data spectral resolution corresponding to the scene data; Based on the first simulated surface reflectance and the spectral resampling method, the data spectral resolution corresponding to the scene data is converted into the spectral resolution to be measured corresponding to each of the index parameters to be measured, so as to obtain a second simulated surface reflectance.

4. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 3, characterized in that: The preset simulation method also includes an atmospheric correction and conversion method; The step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured includes: Inputting the imaging altitude, the second simulated surface reflectivity and the solar altitude angle information into a preset atmospheric radiation transmission model to obtain atmospheric parameters corresponding to the scene data; the atmospheric parameters include the large balloon surface albedo, path radiation, downlink radiation and uplink radiation; The first simulated entrance pupil radiance corresponding to each of the index parameter groups to be measured is calculated by the following formula (1), the surface reflectivity and the atmospheric parameters; Among them, L * is the first simulated entrance pupil radiance; ρ is the second simulated surface reflectivity; S is the large balloon surface albedo; is the radiation of the process; F d is the downlink radiation; T is the uplink radiation.

5. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 4, characterized in that: The preset simulation method also includes a noise superposition method; The step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured includes: Acquire a spectral band-signal-to-noise ratio correspondence rule corresponding to the target spectral band range; the spectral band-signal-to-noise ratio correspondence rule is used to characterize a correspondence between a preset spectral band and a preset signal-to-noise ratio in the target spectral band range; Based on the spectral band-signal-to-noise ratio correspondence rule, a normalized signal-to-noise ratio simulation curve is obtained by fitting; Determine the noise standard deviation corresponding to each of the index parameter groups to be measured based on the signal-to-noise ratio simulation curve and the signal-to-noise ratio to be measured corresponding to each of the index parameter groups to be measured; Based on each of the noise standard deviations and a preset normal distribution method, the Gaussian noise corresponding to each of the index parameter groups to be measured in each of the preset spectral bands is calculated; Each of the Gaussian noises is superimposed on the corresponding first simulated entrance pupil radiance to obtain a second simulated entrance pupil radiance.

6. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 5, characterized in that: The preset simulation method also includes a precision superposition method; The step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured includes: Based on a preset normal distribution method, determining the accuracy deviation value corresponding to each of the index parameter groups to be measured in each of the preset spectral bands; Each of the precision deviation values ​​is respectively added to the second simulated entrance pupil radiance to obtain the target simulated entrance pupil radiance.

7. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 6, characterized in that: The preset simulation method also includes an atmospheric correction method; The step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured includes: Determine the target simulated ground object reflectivity based on the following formula (2) and the target simulated entrance pupil radiance; Determine the target simulated entrance pupil radiance and the target simulated ground object reflectivity as the simulation data; Formula (2) is: Wherein, ρ' is the reflectivity of the target simulated ground object, L * 'Simulate entrance pupil radiance for the target.

8. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 1, characterized in that: After the step of simulating the preset scenario data based on the preset simulation method to determine the simulation data corresponding to each of the index parameter groups to be measured, the method further includes: Input each of the simulation data into a preset quantitative inversion model to obtain the occurrence probability of the scene object corresponding to each of the simulation data; The occurrence probability of each of the scene objects is compared with the scene object corresponding to the scene data to obtain a comparison result corresponding to each of the simulation data; the comparison result is used to characterize the accuracy of the occurrence probability of the scene object.

9. The method for selecting index parameters of satellite-borne hyperspectral payload according to claim 8, characterized in that: The step of determining the target indicator parameter group corresponding to the simulation data meeting the preset evaluation standard as the target indicator parameter group includes: Determine the performance evaluation result corresponding to each of the index parameter groups to be tested based on the preset performance evaluation standard and the comparison result corresponding to each of the simulation data; Acquire a preset cost evaluation standard and preset cost data corresponding to each of the index parameter groups to be measured, and determine a cost evaluation result corresponding to each of the index parameter groups to be measured; The indicator parameter group to be measured, for which the performance evaluation result is performance compliance and the cost evaluation result is cost compliance, is determined as the target indicator parameter group.

10. A system for selecting index parameters of satellite-borne hyperspectral payloads, characterized in that: include: A target spectral band range determination module is used to obtain an application scenario of a target satellite-borne hyperspectral payload, and determine a target spectral band range corresponding to the target satellite-borne hyperspectral payload based on a preset application scenario-spectral band correspondence relationship; A test index parameter group setting module, used for selecting at least two test index parameter groups from preset index parameter groups based on the application scenario and the target spectral band range; The preset index parameter group includes the spatial resolution to be measured, the spectral resolution to be measured, the signal-to-noise ratio to be measured and the accuracy to be measured; A simulation module, used to simulate the preset scenario data based on a preset simulation method, and determine the simulation data corresponding to each of the index parameter groups to be tested; The target indicator parameter group determination module is used to determine the indicator parameter group to be measured corresponding to the simulation data that meets the preset evaluation standard as the target indicator parameter group.

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