Method for simulating and generating ultraviolet band surface radiation data

Abstract

The present invention provides a method for simulating and generating ultraviolet band surface radiation data, which is applied to the technical fields of data processing and remote sensing. The method includes: obtaining a ground object spectral dataset from a spectral database according to the ground object type, where the ground object spectral dataset includes first ground object spectral data; calculating a first equivalent ground object reflectance and a second equivalent ground object reflectance for simulating the ultraviolet band based on a spectral equivalence mechanism; obtaining a first spectral image corresponding to the first ground object spectral data when the first equivalent ground object reflectance matches the second equivalent ground object reflectance; inputting the first ground object spectral data and the first spectral image into a prediction model to obtain second ground object spectral data and a second spectral image; calculating the sensor entrance pupil radiance for simulating the ultraviolet band based on the atmospheric effect parameters and surface information of the second spectral image; and performing image processing on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image.

Description

Technical Field

[0001] The present invention relates to the technical fields of data processing and remote sensing technology, and particularly relates to a method for simulating and generating ultraviolet band surface radiation data. Background Art

[0002] The ultraviolet band has strong absorption and strong scattering characteristics in the atmosphere, which helps to identify and quantify the changes and differences in atmospheric components, surface features, and optical properties. It is widely used in fields such as space exploration, environmental monitoring, and climate change. Among them, the radiation characteristics of the ultraviolet band on the surface are often an important basis for the above research.

[0003] In the process of realizing the concept of the present invention, the inventors found that there are at least the following problems in the related technologies: Spectral band data is usually collected by sensors on aerospace platforms. However, due to factors such as atmospheric absorption and weak reflection characteristics in the ultraviolet band, it is difficult for the sensors carried by aerospace platforms to obtain the radiation information of the ultraviolet band on the surface. Summary of the Invention

[0004] In view of this, the present invention provides a method for simulating and generating ultraviolet band surface radiation data.

[0005] One aspect of the present invention provides a method for simulating and generating ultraviolet band surface radiation data, including: obtaining a ground object spectral data set from a spectral database according to the ground object type, the ground object spectral data set including first ground object spectral data, and the first ground object spectral data characterizing the radiation characteristics of the ground object in the spectral band; calculating a first equivalent ground object reflectance in the spectral band and a second equivalent ground object reflectance in the simulated ultraviolet band based on a spectral equivalent mechanism, where the spectral response function in the simulated ultraviolet band is determined based on sensor parameters, and the spectral response function in the simulated ultraviolet band characterizes the response characteristics of the sensor in the simulated ultraviolet band; obtaining a first spectral image corresponding to the first ground object spectral data when the first equivalent ground object reflectance matches the second equivalent ground object reflectance; inputting the first ground object spectral data and the first spectral image into a prediction model to obtain second ground object spectral data and a second spectral image on the surface in the simulated ultraviolet band, and the second ground object spectral data characterizing the radiation characteristics of the ground object in the simulated ultraviolet band; calculating the sensor entrance pupil radiance in the simulated ultraviolet band based on the atmospheric effect parameters and surface information of the second spectral image, where the atmospheric effect parameters are obtained by inputting the second spectral image into an atmospheric radiation transfer analytical model; and performing image processing on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image.

[0006] Another aspect of the present invention provides an apparatus for simulating and generating ultraviolet band surface radiation data, comprising: a first acquisition module, configured to acquire a ground object spectral data set from a spectral database according to the ground object type, the ground object spectral data set including at least one first ground object spectral data, the first ground object spectral data characterizing the radiation characteristics of the ground object in the spectral band; a first calculation module, configured to calculate a first equivalent ground object reflectance in the spectral band and a second equivalent ground object reflectance in the simulated ultraviolet band based on a spectral equivalent mechanism, the spectral response function in the simulated ultraviolet band being determined based on sensor parameters, and the spectral response function in the simulated ultraviolet band characterizing the response characteristics of the sensor in the simulated ultraviolet band; a second acquisition module, configured to acquire a first spectral image corresponding to the first ground object spectral data when the first equivalent ground object reflectance matches the second equivalent ground object reflectance; an input module, configured to input the first ground object spectral data and the first spectral image into a prediction model to obtain second ground object spectral data and a second spectral image on the ground surface in the simulated ultraviolet band, the second ground object spectral data characterizing the radiation characteristics of the ground object in the simulated ultraviolet band; a second calculation module, configured to calculate the sensor entrance pupil radiance in the simulated ultraviolet band based on the atmospheric effect parameters and surface information of the second spectral image, the atmospheric effect parameters being obtained by inputting the second spectral image into an atmospheric radiation transfer analysis model; and an image processing module, configured to perform image processing on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image.

[0007] Another aspect of the present invention provides an electronic device, comprising:

[0008] One or more processors;

[0009] A memory for storing one or more programs,

[0010] wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described above.

[0011] Another aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the method as described above when executed.

[0012] Another aspect of the present invention provides a computer program product, the computer program product including computer-executable instructions, which are used to implement the method as described above when executed.

[0013] According to an embodiment of the present invention, by obtaining a ground object spectral dataset from a spectral database according to the ground object type, the ground object spectral dataset includes first ground object spectral data, and the first ground object spectral data characterizes the radiation characteristics of the ground object in the spectral band; then based on the spectral equivalence mechanism, calculate the first equivalent ground object reflectance in the spectral band and the second equivalent ground object reflectance in the simulated ultraviolet band respectively; the matching of the first equivalent ground object reflectance and the second equivalent ground object reflectance indicates that the spectral band and the ultraviolet band are adjacent bands or the same band. Therefore, obtain the first spectral image corresponding to the first ground object spectral data; input the first ground object spectral data and the first spectral image into the prediction model, and the second ground object spectral data and the second spectral image in the simulated ultraviolet band can be obtained. Based on the atmospheric effect parameters and surface information of the second spectral image, calculate the sensor entrance pupil radiance in the simulated ultraviolet band, and the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transfer analysis model; use the sensor entrance pupil radiance to perform image processing on the second spectral image to obtain the target spectral image. Therefore, the ultraviolet surface spectral data and the ultraviolet surface spectral image are extracted from the spectral database. Description of the Drawings

[0014] Through the following description of the embodiments of the present invention with reference to the drawings, the above and other objects, features, and advantages of the present invention will become clearer. In the drawings:

[0015] Figure 1A An application scenario diagram of a method for simulating the generation of ultraviolet band surface radiation data according to an embodiment of the present invention is shown;

[0016] Figure 1B A flowchart of a method for simulating the generation of ultraviolet band surface radiation data according to an embodiment of the present invention is shown;

[0017] Figure 2 A curve graph of the spectral response function value in the simulated ultraviolet band according to an embodiment of the present invention is shown;

[0018] Figure 3 A flowchart of a training method for a prediction model according to an embodiment of the present invention is shown;

[0019] Figure 4 A block diagram of an apparatus for simulating the generation of ultraviolet band surface radiation data according to an embodiment of the present invention is shown;

[0020] Figure 5 A block diagram of an electronic device suitable for implementing a method for simulating the generation of ultraviolet band surface radiation data according to an embodiment of the present invention is shown. Detailed Embodiments

[0021] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present invention. In the following detailed description, for the sake of explanation, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present invention. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts of the present invention.

[0022] The terms used herein are merely for describing specific embodiments and are not intended to limit the present invention. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] In cases where expressions similar to "at least one of A, B, and C, etc." are used, generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but not be limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C, etc.).

[0025] In the embodiments of the present invention, in terms of the collection, update, analysis, processing, use, transmission, provision, invention, storage, etc. of the involved data (for example, including but not limited to user personal information), they all comply with the provisions of relevant laws and regulations, are used for legal purposes, and do not violate public order and good customs. In particular, necessary measures are taken for user personal information to prevent illegal access to user personal information data and to maintain the security of user personal information and network security.

[0026] In the embodiments of the present invention, before obtaining or collecting user personal information, the authorization or consent of the user is obtained.

[0027] The ultraviolet band has strong absorption and scattering characteristics in the atmosphere, which helps to identify and quantify the changes and differences in atmospheric components, surface features, and optical properties. It is widely used in fields such as space exploration, environmental monitoring, and climate change. Among them, the surface ultraviolet band reflectance is often an important basis for the above research. Spectral band data is usually collected by sensors on aerospace platforms. However, due to factors such as atmospheric absorption and weak reflection characteristics in the ultraviolet band, it is difficult for sensors carried on aerospace platforms to obtain the radiation information of the ultraviolet band on the surface.

[0028] To solve the above problems, satellite data in adjacent bands and ground object spectral library data are used to generate surface reflectance data in the ultraviolet band. Embodiments of the present invention provide a method for simulating and generating surface radiation data in the ultraviolet band, including: obtaining a ground object spectral data set from a spectral database according to the ground object type. The ground object spectral data set includes first ground object spectral data, and the first ground object spectral data characterizes the radiation characteristics of the ground object in the spectral band; based on the spectral equivalent mechanism, calculating the first equivalent ground object reflectance in the spectral band and the second equivalent ground object reflectance in the simulated ultraviolet band. The spectral response function in the simulated ultraviolet band is determined based on sensor parameters, and the spectral response function in the simulated ultraviolet band characterizes the response characteristics of the sensor in the simulated ultraviolet band; when the first equivalent ground object reflectance matches the second equivalent ground object reflectance, obtaining a first spectral image corresponding to the first ground object spectral data; inputting the first ground object spectral data and the first spectral image into a prediction model to obtain second ground object spectral data and a second spectral image on the surface in the simulated ultraviolet band. The second ground object spectral data characterizes the radiation characteristics of the ground object in the simulated ultraviolet band on the ground object; based on the atmospheric effect parameters and surface information of the second spectral image, calculating the sensor entrance pupil radiance in the simulated ultraviolet band. The atmospheric effect parameters are obtained by inputting the second spectral image into an atmospheric radiation transfer analysis model; using the sensor entrance pupil radiance to perform image processing on the second spectral image to obtain a target spectral image.

[0029] Figure 1A The application scenario diagram of the method for simulating and generating surface radiation data in the ultraviolet band according to an embodiment of the present invention is shown.

[0030] As Figure 1A shown, the application scenario according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0031] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only for example).

[0032] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop computers, desktop computers, and so on.

[0033] The server 105 can be a server that provides various services. For example, it can store spectral data and spectral images for users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (only for example). And send a request to the server to extract spectral information of the ultraviolet band on the earth's surface from the spectral data and spectral images. The background management server can use a prediction model to analyze and process the received spectral images and spectral data to obtain the spectral information of the ultraviolet band on the earth's surface, and feedback the spectral information of the ultraviolet band on the earth's surface to the terminal device.

[0034] It should be noted that the method for simulating and generating ultraviolet band surface radiation data provided by the embodiments of the present invention can generally be executed by the server 105. Correspondingly, the device for simulating and generating ultraviolet band surface radiation data provided by the embodiments of the present invention can generally be set in the server 105. The method for simulating and generating ultraviolet band surface radiation data provided by the embodiments of the present invention can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105. Correspondingly, the device for simulating and generating ultraviolet band surface radiation data provided by the embodiments of the present invention can also be set in a server or a server cluster different from the server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or the server 105.

[0035] It should be understood that Figure 1A the numbers of terminal devices, networks, and servers in

[0036] Figure 1B shows a flowchart of the method for simulating and generating ultraviolet band surface radiation data according to an embodiment of the present invention.

[0037] AsFigure 1B As shown, the method includes operations S110 to S160.

[0038] In operation S110, according to the ground object type, obtain a ground object spectral data set from the spectral database, where the ground object spectral data set includes first ground object spectral data and second ground object spectral data.

[0039] According to an embodiment of the present invention, the first ground object spectral data characterizes the radiation characteristics of the spectral band on the ground object. For example, the first ground object spectral data can be the ground object reflectance, emissivity, etc.

[0040] According to an embodiment of the present invention, the spectral database can be an existing foreign invention database that contains spectral data of a large number of earth surface substances (such as the USGS spectral library, USGS Spectral Library). Usually, the spectral database is widely used in fields such as remote sensing and environmental monitoring, and can better analyze and identify various substances on the earth's surface. The data in the spectral database are usually actual measurement values, such as the relationship between the reflectance (or emissivity) of different materials or ground objects and the wavelength.

[0041] According to an embodiment of the present invention, the spectral database can include ground object types such as artificial substances, soil and mixtures, coatings, liquids, minerals, organic compounds, and vegetation. For example, the first ground object spectral data can be the spectral data of the mineral type.

[0042] According to an embodiment of the present invention, the spectral database provides spectral data of multiple spectral bands in the wavelength range from near ultraviolet to far infrared. The spectral database can contain information such as spectral data tables, spectral graphs, and spectral curves.

[0043] In operation S120, based on the spectral equivalence mechanism, calculate the first equivalent ground object reflectance of the spectral band and the second equivalent ground object reflectance of the simulated ultraviolet band.

[0044] According to an embodiment of the present invention, the spectral response function of the simulated ultraviolet band is determined based on the sensor parameters, and the spectral response function of the simulated ultraviolet band characterizes the response characteristics of the sensor in the simulated ultraviolet band. For example, the sensor can be a device that meets the performance configuration for collecting the ultraviolet band. For example, the simulated ultraviolet band can be the near ultraviolet band.

[0045] According to an embodiment of the present invention, the spectral equivalence mechanism can be constructed based on the wavelength of the spectral band.

[0046] According to an embodiment of the present invention, the first equivalent ground object reflectance characterizes the reflectance of the spectral band on the ground surface after removing the atmospheric influence.

[0047] According to an embodiment of the present invention, the second equivalent ground object reflectance characterizes the reflectance of the simulated ultraviolet band on the ground surface after removing the atmospheric influence.

[0048] In operation S130, when the first equivalent ground reflectance matches the second equivalent ground reflectance, a first spectral image corresponding to the first ground spectral data is acquired.

[0049] According to an embodiment of the present invention, based on the utilization of the correlation between adjacent bands, the first equivalent ground reflectance and the second equivalent ground reflectance can be matched. The matching of the first equivalent ground reflectance and the second equivalent ground reflectance indicates that the spectral band and the ultraviolet band are adjacent bands or the same band. Based on the simulation of the ultraviolet band range setting, the band characteristics of the existing spectral data are comprehensively analyzed according to the target requirements, and appropriate first ground spectral data is selected as the data source.

[0050] For example, the simulated ultraviolet band is divided according to the wavelength range to subdivide the near-ultraviolet band. The first equivalent ground reflectance and the second equivalent ground reflectance are matched to obtain first ground spectral data similar to or the same as multiple subdivided near-ultraviolet bands.

[0051] In operation S140, the first ground spectral data and the first spectral image are input into a prediction model to obtain second ground spectral data and a second spectral image of the ultraviolet band on the ground surface.

[0052] According to an embodiment of the present invention, the second ground spectral data characterizes the radiation characteristics of the ground object in the ultraviolet band. The ultraviolet band usually includes two bands, namely near-ultraviolet and middle-ultraviolet.

[0053] According to an embodiment of the present invention, the prediction model can be a machine learning algorithm, such as neural network, support vector machine, deep learning, etc. In the prediction model, the first spectral data and the first spectral image are input to obtain the second spectral image. The second ground spectral data can be obtained from the second spectral image.

[0054] According to an embodiment of the present invention, data preprocessing, classification, and sorting are performed on the first ground spectral data to ensure that the data meets the input requirements of the prediction model. For example, normalization is performed on the first ground spectral data.

[0055] In operation S150, based on the atmospheric effect parameters and surface information of the second spectral image, the sensor entrance pupil radiance of the simulated ultraviolet band is calculated.

[0056] The atmospheric effect parameters are obtained by inputting the second spectral image into an atmospheric radiative transfer analysis model. Due to the scattering and absorption effects in the atmosphere, the radiance value received by the sensor will deviate from the true reflectance. The atmospheric radiative transfer analysis model is used to eliminate the influence of the atmospheric effect to obtain the sensor entrance pupil radiance.

[0057] For example, the atmospheric radiation transfer analysis model can be the MODTRAN (MODerate resolution atmospheric TRANsmission, used for atmospheric radiation transfer calculation) model.

[0058] The atmospheric effect parameters can be transmittance, reflectivity, radiance, etc.

[0059] In operation S160, image processing is performed on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image.

[0060] According to an embodiment of the present invention, the sensor entrance pupil radiance is the radiance received by the sensor from the scene.

[0061] For example, based on the sensor entrance pupil radiance simulating the ultraviolet band, a data matrix in the ultraviolet band can be extracted from the second spectral image to obtain a target spectral image.

[0062] For example, important information is extracted from the sensor entrance pupil radiance based on the principal component analysis algorithm to highlight the information in the ultraviolet band in the second spectral image, thereby obtaining a target spectral image.

[0063] According to an embodiment of the present invention, by obtaining a ground object spectral data set from a spectral database according to the ground object type, the ground object spectral data set includes first ground object spectral data, and the first ground object spectral data characterizes the radiation characteristics of the ground object in the spectral band; then based on the spectral equivalence mechanism, the first equivalent ground object reflectivity in the spectral band and the second equivalent ground object reflectivity in the simulated ultraviolet band are calculated respectively; the matching of the first equivalent ground object reflectivity and the second equivalent ground object reflectivity indicates that the spectral band and the ultraviolet band are adjacent bands or the same band, so the first spectral image corresponding to the first ground object spectral data is obtained; the first ground object spectral data and the first spectral image are input into a prediction model, and the second ground object spectral data and the second spectral image in the ultraviolet band can be obtained; based on the atmospheric effect parameters and surface information of the second spectral image, the sensor entrance pupil radiance in the simulated ultraviolet band is calculated, and the atmospheric effect parameters are obtained by inputting the second spectral image into an atmospheric radiation transfer analysis model; image processing is performed on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image. Thus, the ultraviolet surface spectral data and the ultraviolet surface spectral image are extracted from the spectral database.

[0064] The atmospheric effect parameters include the atmospheric spherical albedo reflected upward from the ground and the coefficient simulating the amount of solar radiation reaching the top of the atmosphere after scattering in the atmosphere in the ultraviolet band; based on the atmospheric effect parameters and surface information of the second spectral image, the sensor pupil radiance in the simulated ultraviolet band is calculated, including: calculating the reference pupil radiance based on the atmospheric spherical albedo reflected upward from the ground and the surface information; calculating the sensor pupil radiance in the simulated ultraviolet band based on the reference pupil radiance and the coefficient of the amount of solar radiation reaching the top of the atmosphere after scattering in the atmosphere in the ultraviolet band.

[0065] For the second spectral image generated by simulation, based on the analytical model of atmospheric radiation transfer, various atmospheric radiation effects are simulated, and the atmospheric effect parameters are calculated using atmospheric radiation transfer simulation software. Finally, combined with the surface information and atmospheric effects, the radiance at the sensor pupil is obtained. For example, the analytical model of atmospheric radiation transfer can be the MODTRAN (MODerate resolution atmospheric TRANsmission, used for atmospheric radiation transfer calculation) model.

[0066] Sensor pupil radiance The calculation formula is as follows:

[0067] (1);

[0068] Among them, the first term on the right side of the equation represents the radiance of the direct solar light reflected by the target and entering the sensor; the second term represents the radiance of the space scattered light reaching the sensor after reaching the background target; the third term represents the path radiation. The surface information includes and . is the surface reflectivity of the target pixel in the second spectral image; represents the surface reflectivity of the background in the second spectral image, defined as the average reflectivity value of the surrounding pixels; , , and are the atmospheric effect parameters, where represents the atmospheric spherical albedo reflected upward from the ground; represents the coefficient of the total amount of solar radiation reaching the top of the atmosphere after being reflected by the target pixel; represents the coefficient of the total amount of solar radiation reaching the top of the atmosphere reflected by the background; represents the coefficient of the amount of solar radiation reaching the top of the atmosphere only after scattering in the atmosphere. is the reference pupil radiance.

[0069] To simulate and calculate the atmospheric effect parameters of the target area using the atmospheric radiation transfer software, the imaging conditions of the second spectral image need to be obtained, including geometric parameters such as the solar zenith angle, solar azimuth angle, observation zenith angle, and observation azimuth angle. Since the second spectral image is converted from the first spectral image through a prediction model, the imaging parameters of the second spectral image are the same as those of the first spectral image. For wide - format images, the atmospheric conditions vary greatly between pixels. To ensure the simulation accuracy, the atmospheric effect parameters for each pixel should be obtained, which can be achieved by interpolating each pixel through the establishment of an atmospheric effect parameter lookup table.

[0070] According to an embodiment of the present invention, based on the spectral equivalence mechanism, calculating the first equivalent ground reflectance of the spectral band and the second equivalent ground reflectance of the simulated ultraviolet band respectively includes: inputting the sensor parameters of the simulated ultraviolet band into the fitting response model to obtain the spectral response function values of the simulated ultraviolet band; based on the spectral equivalence mechanism, calculating the second equivalent ground reflectance according to the spectral response function values of the simulated ultraviolet band.

[0071] According to an embodiment of the present invention, the fitting response model can use function fitting to simulate the spectral response function values at each wavelength in the simulated ultraviolet band or use function fitting to simulate the spectral response function values at each wavelength in the spectral band. The spectral response function of the spectral band can also be obtained from the official website of the aerospace platform. By substituting the wavelengths of the first ground spectral band of the spectral band into the spectral response function, the spectral response function values at each wavelength in the spectral band can be obtained.

[0072] The spectral response function is a function that describes the response degree of the sensor to light of different wavelengths. It represents the detection efficiency or sensitivity of the sensor at a specific wavelength.

[0073] According to an embodiment of the present invention, the sensor parameters include at least one of the following: the sensor center wavelength and the spectral resolution.

[0074] According to an embodiment of the present invention, the sensor center wavelength is the wavelength at which the band response (or the transmission characteristic of the band filter) is the strongest when the sensor receives the simulated ultraviolet band.

[0075] According to an embodiment of the present invention, inputting the sensor parameters of the simulated ultraviolet band into the fitting response model to obtain the spectral response function values of the simulated ultraviolet band includes: inputting the sensor center wavelength and the spectral resolution of each simulated ultraviolet sub - band in the simulated ultraviolet band into the fitting response model to obtain the spectral response function values of each simulated ultraviolet sub - band.

[0076] The fitting function of the fitting response model is a Gaussian function constructed according to the sensor center wavelength and the wavelengths of each band in the simulated ultraviolet.

[0077] According to an embodiment of the present invention, the fitting function of the fitting response model is as follows:

[0078] (2);

[0079] G(λ i ) is the Gaussian function value, A is the maximum response value of the center wavelength of the sensor, λ i is the wavelength of each band in the simulated ultraviolet band, λ0 is the center wavelength of the sensor, the center wavelength of the sensor is located in the simulated ultraviolet band, usually the representative wavelength of the simulated ultraviolet band, and σ is the standard deviation. That is, the fitting function is a Gaussian function.

[0080] The standard deviation is the spectral resolution. For example, if the spectral resolution is 10 nm (nanometers), then the FWHM (Full Width At Half Maximum) value is equal to 10.

[0081] For example, when , the spectral resolution can be determined by the full width at half maximum of the spectral response function, and its calculation formula is . Set the spectral resolution to 10 nm, that is, the spectral resolution FWHM is equal to 10.

[0082] Figure 2 Shows a curve graph of the spectral response function values of the simulated ultraviolet band according to an embodiment of the present invention.

[0083] When the spectral resolution is 10 nm, the simulated ultraviolet band is in the range of 300 - 400 nm, the abscissa is the wavelength, the ordinate is the spectral response value, the simulated ultraviolet band is divided into 10 ultraviolet sub-bands (S1 - S10), and the center wavelengths λ0 of the sensors in each simulated ultraviolet sub-band in the simulated ultraviolet band are respectively set to 305 nm, 315 nm, 325 nm, 335 nm, 345 nm, 355 nm, 365 nm, 375 nm, 385 nm, 395 nm. Each simulated ultraviolet sub-band (S1 - S10) can also be used as each simulated channel. The curve graphs of the spectral response function values of each ultraviolet sub-band (S1 - S10) are as Figure 2 shown.

[0084] At the same time, each spectral sub-band in the spectral band is used as a spectral channel.

[0085] For the spectral data in the spectral database, equivalent calculations are respectively performed using the spectral response function values of the spectral band and the spectral response function values of the simulated ultraviolet band, so as to obtain the equivalent surface reflectances of the spectral channel and the simulated channel respectively.

[0086] According to an embodiment of the present invention, the spectral equivalent mechanism is to divide the integral value of the product of multiple spectral response function values in the spectral band and the first ground object reflectance by the integral value of multiple spectral response function values to obtain the first equivalent ground object reflectance of the spectral band.

[0087] For example, the second equivalent ground object reflectance in the simulated ultraviolet band can be calculated through a spectral equivalent mechanism. The ground object reflectance in the simulated ultraviolet band can be obtained from a spectral database.

[0088] For example, the first ground object spectral data can be the first ground object reflectance, and the predicted second ground object spectral data can be the second ground object reflectance in the ultraviolet band.

[0089] According to an embodiment of the present invention, when the first equivalent ground object reflectance matches the second equivalent ground object reflectance, obtaining a first spectral image corresponding to the first ground object spectral data from a spectral database further includes: calculating the correlation between the first equivalent ground object reflectance and the second equivalent ground object reflectance to obtain a correlation value; and determining that the first equivalent ground object reflectance matches the second equivalent ground object reflectance when the correlation value meets a preset correlation threshold.

[0090] Perform Pearson's correlation analysis on the first equivalent ground object reflectance of the spectral channel and the second equivalent ground object reflectance of the simulation channel. Based on the result of the correlation analysis, determine the band of the ground object spectral dataset that can participate in the production of the training dataset and the training of the prediction model. The band with a higher correlation with the ultraviolet band is generally the visible light band, and the closer it is to the spectral channel of the ultraviolet band, the stronger its information correlation.

[0091] The value range of the correlation value r is (-1, 1). The larger the correlation value, the stronger the correlation between the spectral band and the simulated ultraviolet band. If r > 0, it indicates a positive correlation between the spectral band and the simulated ultraviolet band; if r < 0, it indicates a negative correlation between the spectral band and the simulated ultraviolet band.

[0092] Considering the characteristic that adjacent band satellite data of remote sensing data has spectral correlation, especially the visible light band adjacent to the near-ultraviolet band (simulated ultraviolet band), which has a strong spectral dimension correlation with the near-ultraviolet band. Based on this characteristic, use the spectral data of the adjacent band of the near-ultraviolet band as the data source. Input the first ground object spectral data and the first spectral image of the adjacent band of the near-ultraviolet band into the prediction model, and the surface spectral data in the ultraviolet band (i.e., the second spectral data) and the second spectral image can be generated. This method solves the problem of the lack of surface remote sensing images in the ultraviolet band, is not restricted by the surface underlying surface, has a wide application range, and has a relatively high prediction accuracy.

[0093] According to an embodiment of the present invention, the prediction model is trained based on the following method: Input the ground object spectral training data and the ground object spectral training image into the prediction model to obtain a predicted value, and the training equivalent ground object reflectance of the ground object spectral training data matches the second equivalent ground object reflectance; Calculate the error between the true value and the predicted value corresponding to the ground object spectral training data; When the error meets the preset error value, obtain the prediction model to be tested; Based on the test results of the prediction model to be tested, determine the prediction model to be tested as the trained prediction model.

[0094] For example, the ground object spectral training data in the spectral database can be divided into a training set and a test set according to a ratio of 7:3. The error can be the ten-fold cross-validation error, and the preset error value can be the minimum value reached by the error. Input the training set into the prediction model, perform parameter tuning according to the principle of the minimum ten-fold cross-validation error, and determine the prediction model to be tested with the optimal parameter configuration. Based on the optimal parameter configuration, use the test set to perform a regression test on the prediction model to be tested and evaluate the model accuracy, and obtain the trained prediction model.

[0095] According to an embodiment of the present invention, the test results include regression test results and accuracy evaluation results. Based on the test results of the prediction model to be tested, determining the prediction model to be tested as the trained prediction model includes: performing a regression test on the prediction model to be tested to obtain regression test results; When the regression test results indicate that the regression test of the prediction model to be tested passes, perform a model accuracy evaluation on the prediction model to be tested to obtain accuracy evaluation results; When the accuracy evaluation results indicate that the model accuracy evaluation of the prediction model to be tested passes, determine the prediction model to be tested as the trained prediction model.

[0096] The prediction model includes multiple sub-models, and the above method further includes: using the multiple sub-models to predict the ground object spectral training data to obtain reference predicted values of the multiple sub-models; According to the multiple reference predicted values, determine the predicted value of the prediction model.

[0097] According to an embodiment of the present invention, the calculation formula for the predicted value of the prediction model is as follows:

[0098] (3);

[0099] Where t = 1, 2,..., T, represents the predicted value of the t-th sub-model for the ground object spectral training data x, and T is the number of sub-models in the prediction model.

[0100] For example, the prediction model can be a random forest regression model, and the sub - models can be decision trees. Each decision tree corresponds to a different spectral channel. The training set and the test set can be refined according to different spectral channels to obtain the training subsets and test subsets for each spectral channel. The decision trees are trained using the training subsets and test subsets.

[0101] For example, the prediction results of each decision tree are used for the final prediction. In a regression task, the random forest regression model usually takes the average of the predicted values of all decision trees as the final prediction result. The steps of random forest regression are as follows: Multiple training subsets are generated from the training set by sampling with replacement. For each generated training subset, a decision tree is constructed. When constructing each decision tree, at each node, a part of the features are randomly selected from all the features, and then the optimal splitting point is found based on these features.

[0102] In the prediction process of the prediction model, the first ground object spectral data in the visible light band with high correlation adjacent to the simulated ultraviolet band can be extracted from the spectral database. And pre - process the first spectral image, such as format conversion, reprojection, cloud removal, etc. Perform band operations on the image data with a reflectance range of 0 - 10000 after pre - processing, so that the reflectance range is between 0 - 1, and perform band synthesis on the visible light band data to ensure that the data format is suitable for the input format of the prediction model.

[0103] Use the sub - models of different spectral channels to generate the second ground object spectral data and the second spectral image. For example, input the pre - processed first spectral image data and the first ground object spectral data into the trained random forest regression model. The random forest regression model will output the ultraviolet surface background radiation value of each pixel, that is, the reflectance value (the second ground object spectral data), and generate an ultraviolet surface reflectance image (the second spectral image) according to the second ground object spectral data.

[0104] Figure 3 The flowchart of the training method of the prediction model according to an embodiment of the present invention is shown.

[0105] As Figure 3 shown, the training method of the prediction model includes operations S310 - S380.

[0106] In operation S310, obtain the ground object spectral data set from the spectral database. The ground object spectral data set includes at least one ground object spectral data.

[0107] In operation S320, obtain the spectral response function values of the simulated ultraviolet band and the spectral response function values corresponding to each of the at least one ground object spectral data.

[0108] In operation S330, for each ground object spectral data, based on the spectral equivalence mechanism, calculate the training equivalent ground object reflectance of the ground object spectral training data and the second equivalent ground object reflectance for simulating the ultraviolet band.

[0109] In operation S340, when the Pearson correlation between the training equivalent ground object reflectance and the second equivalent ground object reflectance meets a preset correlation threshold, determine the ground object spectral data as the ground object spectral training data; and obtain the ground object spectral training image corresponding to the ground object spectral training data from the spectral database.

[0110] In operation S350, construct a model data set based on the ground object spectral training data and the ground object spectral training image, and divide the model data set into a training set and a test set.

[0111] In operation S360, use the training set to train a prediction model to obtain a prediction model to be tested.

[0112] In operation S370, use the test set to test the prediction model to be tested to obtain a test result.

[0113] In operation S380, when the test result indicates that the prediction model to be tested passes the test, obtain the trained prediction model.

[0114] The method for simulating the surface radiation data in the ultraviolet band combines real satellite images (the first spectral image) and the first ground object spectral data. By inputting the first ground object spectral data and the first spectral image similar to the simulated ultraviolet band into the prediction model, the surface background radiation data (the second ground object spectral data) and the second spectral image in the ultraviolet band can be obtained. This technical point enables the method to obtain the ultraviolet surface background radiation data and spectral images even in the case of scarce ultraviolet satellite images, which has important practical application value.

[0115] First, the ground object background radiation data in the ultraviolet band can be obtained. Currently, the background radiation data retrieved from satellite observation data mainly focuses on the visible light, near-infrared, short-wave infrared, and thermal infrared channels. In contrast, the surface background radiation images covering the near-ultraviolet band are scarce, and it is difficult to generate the surface background radiation model in the ultraviolet band through methods such as the ground object radiation model, spectral mixture model, and spectral channel correlation modeling. The embodiment of the present invention is based on the visible light band data and the ground object spectral data of satellite data, and by inputting the first ground object spectral data and the first spectral image adjacent to the simulated ultraviolet band into the random forest regression model, the surface background radiation data and spectral images in the ultraviolet band are generated.

[0116] Secondly, high-resolution and high-quality spectral images in the ultraviolet band can be provided. Appropriate satellite data can be selected as the data source according to the spatial resolution and temporal resolution required by the target. In addition, by constructing the spectral response function of the simulated channel and setting the spectral resolution of the simulated channel, hyperspectral images in the near-ultraviolet band can be generated. In addition, by inputting high-resolution and high-quality satellite data with clear sky into the random forest regression model, spectral images in the near-ultraviolet band with the same spatial resolution and temporal resolution can be obtained.

[0117] The random forest regression model can predict the second spectral image of each channel in the ultraviolet band. In order to eliminate the influence of the atmospheric effect on the second spectral image, the second spectral image is input into the atmospheric radiation transfer analysis model to obtain the atmospheric effect parameters. Based on the atmospheric effect parameters of the second spectral image and the surface information, the sensor pupil radiance in the simulated ultraviolet band is calculated; the second spectral image is processed using the sensor pupil radiance to obtain the target spectral image. This makes the spectral image in the ultraviolet band more comprehensive and accurate.

[0118] Finally, it has a wide range of applications and does not rely on ultraviolet remote sensing images as the data source. Compared with the ground object radiation model method, it is not limited to a certain ground object and underlying surface. The ground object spectral data of typical ground objects are used to calculate the training equivalent ground object reflectance through spectral equivalent calculation. The correlation analysis is carried out between the training equivalent ground object reflectance and the second equivalent reflectance to obtain the ground object spectral training data and the ground object spectral training image in the band adjacent to the simulated ultraviolet band; the prediction model is trained using the ground object spectral training data and the ground object spectral training image, so that the trained prediction model is applicable to common ground objects, complex surfaces, etc., and has a wide range of applications.

[0119] Figure 4 The block diagram of the ultraviolet band surface radiation data simulation generation device according to an embodiment of the present invention is shown.

[0120] As Figure 4 shown, the ultraviolet band surface radiation data simulation generation device includes a first acquisition module 410, a first calculation module 420, a second acquisition module 430, an input module 440, a second calculation module 450, and an image processing module 460.

[0121] The first acquisition module 410 is used to acquire the ground object spectral data set from the spectral database according to the ground object type. The ground object spectral data set includes the first ground object spectral data, and the first ground object spectral data characterizes the radiation characteristics of the ground object in the spectral band. In one embodiment, the first acquisition module 410 can be used to perform the operation S110 described above, which will not be elaborated here.

[0122] The first calculation module 420 is configured to calculate a first equivalent ground reflectance of a spectral band and a second equivalent ground reflectance of a simulated ultraviolet band based on a spectral equivalence mechanism. The spectral response function of the simulated ultraviolet band is determined based on sensor parameters, and the spectral response function of the simulated ultraviolet band characterizes the response characteristics of the sensor in the simulated ultraviolet band. In one embodiment, the calculation module 420 may be configured to perform the operation S120 described above, which will not be elaborated herein.

[0123] The second acquisition module 430 is configured to acquire a first spectral image corresponding to the first ground spectral data when the first equivalent ground reflectance matches the second equivalent ground reflectance. In one embodiment, the second acquisition module 430 may be configured to perform the operation S130 described above, which will not be elaborated herein.

[0124] The input module 440 is configured to input the first ground spectral data and the first spectral image into a prediction model to obtain second ground spectral data and a second spectral image on the ground in the simulated ultraviolet band. The second ground spectral data characterizes the radiation characteristics of the ground object in the simulated ultraviolet band. In one embodiment, the second input module 440 may be configured to perform the operation S140 described above, which will not be elaborated herein.

[0125] The second calculation module 450 is configured to calculate the sensor entrance pupil radiance in the simulated ultraviolet band based on the atmospheric effect parameters and the surface information of the second spectral image. The atmospheric effect parameters are obtained by inputting the second spectral image into an atmospheric radiation transfer analysis model. In one embodiment, the calculation module 450 may be configured to perform the operation S150 described above, which will not be elaborated herein.

[0126] The image processing module 460 is configured to perform image processing on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image. In one embodiment, the image processing module 460 may be configured to perform the operation S160 described above, which will not be elaborated herein.

[0127] According to an embodiment of the present invention, the first calculation module 420 includes a first input sub-module and a first calculation sub-module. The first input sub-module is configured to input the sensor parameters of the simulated ultraviolet band into a fitting response model to obtain the spectral response function values of the simulated ultraviolet band. The first calculation sub-module is configured to calculate the second equivalent ground reflectance based on the spectral equivalence mechanism according to the spectral response function values of the simulated ultraviolet band.

[0128] According to an embodiment of the present invention, the sensor parameters include at least one of the following: the sensor central wavelength and the spectral resolution.

[0129] According to an embodiment of the present invention, the first input sub-module includes a first input unit. The first input unit is configured to input the sensor center wavelength and spectral resolution of each analog ultraviolet sub-band in the analog ultraviolet band into a fitting response model to obtain the spectral response function value of each ultraviolet sub-band.

[0130] According to an embodiment of the present invention, the second acquisition module 430 further includes a second calculation sub-module and a determination sub-module. The second calculation sub-module is configured to calculate the correlation between the first equivalent ground reflectance and the second equivalent ground reflectance to obtain a correlation value; the determination sub-module is configured to determine that the first equivalent ground reflectance matches the second equivalent ground reflectance when the correlation value meets a preset correlation threshold.

[0131] According to an embodiment of the present invention, the prediction model is trained based on the following method: inputting the ground spectral training data into the prediction model to obtain a predicted value, and the training equivalent ground reflectance of the ground spectral training data matches the second equivalent ground reflectance; constructing a model data set based on the first equivalent ground reflectance and the second equivalent ground reflectance of the ground spectral data, and dividing the model data set into a training set and a test set; calculating the error between the true value and the predicted value corresponding to the ground spectral training data; obtaining a to-be-tested prediction model when the error meets a preset error value; and determining the to-be-tested prediction model as the trained prediction model based on the test result of the to-be-tested prediction model.

[0132] According to an embodiment of the present invention, the test result includes a regression test result and an accuracy evaluation result. Determining the to-be-tested prediction model as the trained prediction model based on the test result of the to-be-tested prediction model includes: performing a regression test on the to-be-tested prediction model to obtain a regression test result; performing a model accuracy evaluation on the to-be-tested prediction model when the regression test result indicates that the regression test of the to-be-tested prediction model passes to obtain an accuracy evaluation result. When the accuracy evaluation result indicates that the model accuracy evaluation of the to-be-tested prediction model passes, the to-be-tested prediction model is determined as the trained prediction model.

[0133] Any number of modules, sub-modules, units, and sub-units according to embodiments of the present invention, or at least part of the functions of any number thereof, may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present invention may be split into multiple modules for implementation. Any one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present invention may be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or may be implemented by any other reasonable way of integrating or packaging circuits, or in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, one or more of the modules, sub-modules, units, and sub-units according to embodiments of the present invention may be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions may be executed.

[0134] For example, any number of the first acquisition module 410, the first calculation module 420, the second acquisition module 430, the input module 440, the second calculation module 450, and the image processing module 460 may be combined and implemented in one module / unit / sub-unit, or any one of the modules / units / sub-units may be split into multiple modules / units / sub-units. Alternatively, at least part of the functions of one or more of these modules / units / sub-units may be combined with at least part of the functions of other modules / units / sub-units and implemented in one module / unit / sub-unit. According to embodiments of the present invention, at least one of the first acquisition module 410, the first calculation module 420, the second acquisition module 430, the input module 440, the second calculation module 450, and the image processing module 460 may be at least partially implemented as a hardware circuit, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-chip, a system-on-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or may be implemented by any other reasonable way of integrating or packaging circuits, or in any one of the three implementation manners of software, hardware, and firmware, or in an appropriate combination of any several of them. Alternatively, at least one of the first acquisition module 410, the first calculation module 420, the second acquisition module 430, the input module 440, the second calculation module 450, and the image processing module 460 may be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions may be executed.

[0135] It should be noted that the part of the ultraviolet band surface radiation data simulation generation device in the embodiments of the present invention corresponds to the part of the ultraviolet band surface radiation data simulation generation method in the embodiments of the present invention. For the description of the part of the ultraviolet band surface radiation data simulation generation device, please specifically refer to the part of the ultraviolet band surface radiation data simulation generation method, which will not be elaborated here.

[0136] Figure 5 The block diagram of an electronic device suitable for implementing the ultraviolet band surface radiation data simulation generation method according to an embodiment of the present invention is shown.

[0137] Figure 5 The shown electronic device is merely an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0138] As Figure 5 shown, the electronic device according to an embodiment of the present invention includes a processor 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage section 508 into the random access memory (RAM) 503. The processor 501 may include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), and so on. The processor 501 may also include on-board memory for caching purposes. The processor 501 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiments of the present invention.

[0139] In the RAM 503, various programs and data required for the operation of the electronic device are stored. The processor 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. The processor 501 performs various operations of the method flow according to the embodiments of the present invention by executing the programs in the ROM 502 and / or the RAM 503. It should be noted that the program may also be stored in one or more memories other than the ROM 502 and the RAM 503. The processor 501 may also perform various operations of the method flow according to the embodiments of the present invention by executing the programs stored in the one or more memories.

[0140] According to an embodiment of the present invention, the electronic device may further include an input / output (I / O) interface 505, and the input / output (I / O) interface 505 is also connected to the bus 504. The electronic device may further include one or more of the following components connected to the input / output (I / O) interface 505: an input portion 506 including a keyboard, a mouse, etc.; an output portion 507 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage portion 508 including a hard disk, etc.; and a communication portion 509 including a network interface card such as a LAN card, a modem, etc. The communication portion 509 performs communication processing via a network such as the Internet. A driver 510 is also connected to the input / output (I / O) interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the driver 510 as needed, so that a computer program read from it is installed into the storage portion 508 as needed.

[0141] According to an embodiment of the present invention, the method flow according to the embodiment of the present invention may be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains program codes for performing the method shown in the flowchart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above functions defined in the system of the embodiment of the present invention are executed. According to an embodiment of the present invention, the above-described system, device, apparatus, module, unit, etc. may be implemented by computer program modules.

[0142] The present invention also provides a computer-readable storage medium, which may be included in the device / device / system described in the above embodiments; or may exist separately without being assembled into the device / device / system. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed, the method according to the embodiment of the present invention is implemented.

[0143] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. For example, it may include but is not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0144] For example, according to an embodiment of the present invention, the computer-readable storage medium may include one or more memories other than the above-described ROM 502 and / or RAM 503 and / or ROM 502 and RAM 503.

[0145] An embodiment of the present invention also includes a computer program product, which includes a computer program. The computer program contains program code for executing the method provided by the embodiment of the present invention. When the computer program product runs on an electronic device, the program code is used to enable the electronic device to implement the method for simulating and generating ultraviolet band surface radiation data provided by the embodiment of the present invention.

[0146] When the computer program is executed by the processor 501, the above functions defined in the system / apparatus of the embodiment of the present invention are executed. According to an embodiment of the present invention, the above-described systems, apparatuses, modules, units, etc. can be implemented by computer program modules.

[0147] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and be downloaded and installed through the communication part 509, and / or be installed from the removable medium 511. The program code contained in the computer program can be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0148] According to an embodiment of the present invention, program code for executing the computer program provided by the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. The programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (for example, by using an Internet service provider to connect through the Internet).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutively represented blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions. Those skilled in the art can understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features described in the various embodiments of the present invention can be combined and / or combined in various ways. All such combinations and / or combinations fall within the scope of the present invention.

[0150] The above describes the embodiments of the present invention. However, these embodiments are only for illustrative purposes and are not intended to limit the scope of the present invention. Although the embodiments are described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. Without departing from the scope of the present invention, those skilled in the art can make various substitutions and modifications, and all these substitutions and modifications should fall within the scope of the present invention.

Claims

1. A method for simulating and generating ultraviolet band surface radiation data, characterized in that, Including: Obtain a ground object spectral dataset from a spectral database according to the ground object type. The ground object spectral dataset includes first ground object spectral data, and the first ground object spectral data characterizes the radiation characteristics of the ground object in the spectral band. Obtain the first equivalent ground object reflectance of the spectral band by dividing the integral value of the product of multiple spectral response function values within the spectral band and the first ground object reflectance by the integral value of the multiple spectral response function values. Input the sensor parameters of the simulated ultraviolet band into the fitting response model to obtain the spectral response function values of the simulated ultraviolet band. Based on the spectral equivalence mechanism, calculate the second equivalent ground object reflectance according to the spectral response function values of the simulated ultraviolet band. The spectral response function of the simulated ultraviolet band is determined based on the sensor parameters, and the spectral response function of the ultraviolet band characterizes the response characteristics of the sensor in the simulated ultraviolet band. When the first equivalent ground object reflectance matches the second equivalent ground object reflectance, obtain a first spectral image corresponding to the first ground object spectral data. Input the first ground object spectral data and the first spectral image into the prediction model to obtain second ground object spectral data and a second spectral image on the ground surface in the simulated ultraviolet band. The second ground object spectral data characterizes the radiation characteristics of the ground object in the simulated ultraviolet band. Based on the atmospheric effect parameters and surface information of the second spectral image, calculate the sensor entrance pupil radiance in the simulated ultraviolet band. The atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transfer analysis model. Perform image processing on the second spectral image using the sensor entrance pupil radiance to obtain a target spectral image.

2. The method according to claim 1, characterized in that, The sensor parameters include at least one of the following: the sensor central wavelength and the spectral resolution.

3. The method according to claim 2, wherein The step of inputting the sensor parameters of the simulated ultraviolet band into the fitting response model to obtain the spectral response function values of the simulated ultraviolet band includes: Input the sensor central wavelength and the spectral resolution of each simulated ultraviolet sub-band in the simulated ultraviolet band into the fitting response model to obtain the spectral response function values of the respective simulated ultraviolet sub-bands.

4. The method according to claim 3, wherein The fitting function of the fitting response model is a Gaussian function constructed according to the sensor central wavelength and the wavelengths of each simulated ultraviolet band.

5. The method according to claim 1, wherein The step of, when the first equivalent ground object reflectance matches the second equivalent ground object reflectance, obtaining a first spectral image corresponding to the first ground object spectral data further includes: Calculate the correlation between the first equivalent ground object reflectance and the second equivalent ground object reflectance to obtain a correlation value. When the correlation value meets a preset correlation threshold, determine that the first equivalent ground object reflectance matches the second equivalent ground object reflectance.

6. The method according to claim 1, wherein The prediction model is trained based on the following method: Input the ground object spectral training data into the prediction model to obtain a predicted value. The training equivalent ground object reflectance of the ground object spectral training data matches the second equivalent ground object reflectance. Calculate the error between the true value corresponding to the ground object spectral training data and the predicted value. When the error meets the preset error value, a prediction model to be tested is obtained; Based on the test results of the prediction model to be tested, the prediction model to be tested is determined as the trained prediction model.

7. The method according to claim 6, characterized in that, The test results include regression test results and accuracy evaluation results. The process of determining the prediction model to be tested as the trained prediction model based on the test results of the prediction model to be tested includes: Conduct a regression test on the prediction model to be tested to obtain the regression test results; When the regression test results indicate that the regression test of the prediction model to be tested passes, conduct a model accuracy evaluation on the prediction model to be tested to obtain the accuracy evaluation results; When the accuracy evaluation results indicate that the model accuracy evaluation of the prediction model to be tested passes, the prediction model to be tested is determined as the trained prediction model.

8. The method according to claim 1, wherein The prediction model includes multiple sub-models, and the method further includes: Using the multiple sub-models to predict the ground object spectral training data to obtain reference prediction values of the multiple sub-models; Based on the multiple reference prediction values, determine the prediction value of the prediction model.

9. The method according to claim 1, wherein The atmospheric effect parameters include the atmospheric spherical albedo reflected upward from the ground and the coefficient simulating the solar radiation amount reaching the top of the atmosphere after scattering in the atmosphere in the ultraviolet band; The calculation of the sensor entrance pupil radiance in the simulated ultraviolet band based on the atmospheric effect parameters and surface information of the second spectral image includes: Based on the atmospheric spherical albedo reflected upward from the ground and the surface information, calculate the reference entrance pupil radiance; Based on the reference entrance pupil radiance and the coefficient of the solar radiation amount reaching the top of the atmosphere after scattering in the atmosphere in the simulated ultraviolet band, calculate the sensor entrance pupil radiance in the simulated ultraviolet band.

Images