Simulation generation method of ultraviolet band surface radiation data

Abstract

The invention provides an ultraviolet band surface radiation data simulation generation method, which is applied to the technical field of data processing and the technical field of remote sensing. The method comprises the steps that a ground feature spectral data set is acquired from a spectral database according to ground feature types, and the ground feature spectral data set comprises first ground feature spectral data; calculating a first equivalent ground feature reflectivity and a second equivalent ground feature reflectivity of the simulated ultraviolet band based on a spectrum equivalent mechanism; under the condition that the first equivalent ground feature reflectivity is matched with the second equivalent ground feature reflectivity, acquiring a first spectral image corresponding to the first ground feature spectral data; inputting the first ground feature spectral data and the first spectral image into a prediction model to obtain second ground feature spectral data and a second spectral image; based on the atmospheric effect parameter and the earth surface information of the second spectral image, calculating to obtain the sensor entrance pupil radiance of the simulated ultraviolet band; and performing image processing on the second spectral image by using the entrance pupil radiance of the sensor to obtain a target spectral image.

Description

Technical Field

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

[0002] The ultraviolet band has strong atmospheric absorption and scattering characteristics, which helps to identify and quantify changes and differences in atmospheric composition, surface features, and optical properties. It is widely used in space exploration, environmental monitoring, climate change and other fields. 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 technology: spectral band data is usually collected by sensors on aerospace platforms, but due to factors such as atmospheric absorption and weak reflection characteristics of the ultraviolet band, it is difficult for sensors carried by aerospace platforms to obtain 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 surface radiation data in an ultraviolet band, comprising: obtaining a ground object spectral data set from a spectral database according to the ground object type, the ground object spectral data set comprising first ground object spectral data, the first ground object spectral data representing the radiation characteristics of the ground object in the spectral band; based on a spectral equivalence mechanism, calculating a first equivalent ground object reflectance in the spectral band and a second equivalent ground object reflectance in the simulated ultraviolet band, the spectral response function of the simulated ultraviolet band being determined based on sensor parameters, the spectral response function of the simulated ultraviolet band representing the response characteristics of the sensor in the simulated ultraviolet band; calculating a first equivalent ground object reflectance and a second equivalent ground object reflectance in the simulated ultraviolet band based on a spectral equivalence mechanism; ... When the reflectance matches, a 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 the prediction model to obtain second ground object spectral data and a second spectral image of the simulated ultraviolet band on the surface, 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, the sensor entrance pupil radiance of the simulated ultraviolet band is calculated, the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transmission analytical model; the second spectral image is processed by using the sensor entrance pupil radiance to obtain the target spectral image.

[0006] Another aspect of the present invention provides a device for simulating and generating surface radiation data in an ultraviolet band, comprising: a first acquisition module, for acquiring a ground object spectral data set from a spectral database according to a ground object type, the ground object spectral data set comprising at least one first ground object spectral data, the first ground object spectral data representing the radiation characteristics of the ground object in the spectral band; a first calculation module, for calculating a first equivalent ground object reflectivity in the spectral band and a second equivalent ground object reflectivity in the simulated ultraviolet band based on a spectral equivalence mechanism, the spectral response function of the simulated ultraviolet band being determined based on sensor parameters, the spectral response function of the simulated ultraviolet band representing the response characteristics of the sensor in the simulated ultraviolet band; a second acquisition module, for calculating a first equivalent ground object reflectivity and a second equivalent ground object reflectivity in the simulated ultraviolet band based on a spectral equivalence mechanism When the effective reflectance of the ground object matches, a first spectral image corresponding to the first ground object spectral data is obtained; an input module is used to 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 of the simulated ultraviolet band on the surface, and the second ground object spectral data represents the radiation characteristics of the ground object in the simulated ultraviolet band; a second calculation module is used to calculate the sensor entrance pupil radiance of the simulated ultraviolet band based on the atmospheric effect parameters and surface information of the second spectral image, and the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transmission analytical model; an image processing module is used 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] When the one or more programs are executed by one or more processors, the one or more processors implement the above method.

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

[0012] Another aspect of the present invention provides a computer program product, which includes computer executable instructions, and the instructions are used to implement the above method when being executed.

[0013] According to an embodiment of the present invention, a ground object spectral data set is obtained 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 reflectance of the spectral band and the second equivalent ground object reflectance of the simulated ultraviolet band are calculated respectively; the first equivalent ground object reflectance matches the second equivalent ground object reflectance, indicating 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 the prediction model to obtain the second ground object spectral data and the second spectral image of the simulated ultraviolet band, and the sensor entrance pupil radiance of the simulated ultraviolet band is calculated based on the atmospheric effect parameters and surface information of the second spectral image, and the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transmission analytical model; the second spectral image is image processed using the sensor entrance pupil radiance to obtain the target spectral image. Therefore, the ultraviolet surface spectral data and the ultraviolet surface spectral image are extracted from the spectral database. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0015] Figure 1A A diagram showing an application scenario of a method for simulating and generating ultraviolet band surface radiation data according to an embodiment of the present invention;

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

[0017] Figure 2 A curve diagram showing the spectral response function value of the simulated ultraviolet band according to an embodiment of the present invention;

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

[0019] Figure 4 A block diagram of a device for simulating and generating 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 and generating ultraviolet band surface radiation data according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0021] Below, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of concepts of the present invention.

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

[0023] All terms (including technical and scientific terms) used herein 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] When using expressions such as "at least one of A, B, and C, etc.", they should generally be interpreted according to the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

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

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

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

[0028] To solve the above problems, satellite data of adjacent bands and ground object spectral library data are used to generate ultraviolet band surface reflectance data. An embodiment of the present invention provides a method for simulating and generating ultraviolet band surface radiation data, including: acquiring 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, the first ground object spectral data representing the radiation characteristics of the ground object in the spectral band; based on a spectral equivalence mechanism, calculating a first equivalent ground object reflectance of the spectral band and a second equivalent ground object reflectance of the simulated ultraviolet band, the spectral response function of the simulated ultraviolet band being determined based on sensor parameters, the spectral response function of the simulated ultraviolet band representing the response characteristics of the sensor in the simulated ultraviolet band. property; when the first equivalent ground object reflectance matches the second equivalent ground object reflectance, a 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 the prediction model to obtain the second ground object spectral data and the second spectral image of the simulated ultraviolet band on the ground surface, the second ground object spectral data represents the radiation characteristics of the ground object on the ground object in the simulated ultraviolet band; 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, the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transmission analytical model; the second spectral image is processed by using the sensor entrance pupil radiance to obtain the target spectral image.

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

[0030] like Figure 1A As 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 a communication link 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 optical fiber cables, etc.

[0031] The user can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 through 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 may be various electronic devices having display screens and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

[0033] The server 105 may be a server that provides various services, such as storing spectral data and spectral images (only as an example) for users using the first terminal device 101, the second terminal device 102, and the third terminal device 103. A request to extract spectral information of the ultraviolet band on the surface from the spectral data and spectral images is sent to the server. The background management server may analyze and process the received spectral images and spectral data using a prediction model to obtain spectral information of the ultraviolet band on the surface, and feed back the spectral information of the ultraviolet band on the surface to the terminal device.

[0034] It should be noted that the ultraviolet band surface radiation data simulation generation method provided in the embodiment of the present invention can generally be executed by the server 105. Accordingly, the ultraviolet band surface radiation data simulation generation device provided in the embodiment of the present invention can generally be set in the server 105. The ultraviolet band surface radiation data simulation generation method provided in the embodiment of the present invention can also be performed by a server or server cluster that is different from the server 105 and can communicate with the first terminal device 101, the second terminal device 102, the third terminal device 103 and / or the server 105. Correspondingly, the ultraviolet band surface radiation data simulation generation device provided in the embodiment of the present invention can also be set in a server or server cluster that is different from the server 105 and can communicate 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 number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

[0036] Figure 1B A flow chart of a method for simulating and generating ultraviolet band surface radiation data according to an embodiment of the present invention is shown.

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

[0038] In operation S110 , a ground object spectrum data set is acquired from a spectrum database according to the ground object type, where the ground object spectrum data set includes first ground object spectrum data and second ground object spectrum 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 may be the ground object reflectivity, emissivity, etc.

[0040] According to an embodiment of the present invention, the spectral database may be an existing database invented externally that contains a large amount of spectral data of materials on the earth's surface (such as the USGS Spectral Library). Spectral databases are generally widely used in the fields of remote sensing and environmental monitoring, and can better analyze and identify various materials on the earth's surface. The data in the spectral database are usually actual measured values, such as the relationship between the reflectivity (or emissivity) and wavelength of different materials or objects.

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

[0042] According to an embodiment of the present invention, the spectrum database provides spectrum data of multiple spectrum bands ranging from near ultraviolet to far infrared wavelengths. The spectrum database may include information such as spectrum data tables, spectrum graphs, and spectrum curves.

[0043] In operation S120, based on a spectral equivalence mechanism, a first equivalent object reflectance in a spectral band and a second equivalent object reflectance in a simulated ultraviolet band are calculated.

[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 of collecting ultraviolet bands. For example, the simulated ultraviolet band can be a near ultraviolet band.

[0045] According to an embodiment of the present invention, the spectral equivalence mechanism may 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 represents the reflectance of a spectral band on the ground surface after atmospheric influence is removed.

[0047] According to an embodiment of the present invention, the second equivalent ground object reflectivity represents the reflectivity on the ground surface in the simulated ultraviolet band excluding the influence of the atmosphere.

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

[0049] According to an embodiment of the present invention, based on the correlation between adjacent bands, the first equivalent ground object reflectance can be matched with the second equivalent ground object reflectance. The matching of the first equivalent ground object reflectance with the second equivalent ground object reflectance indicates that the spectral band and the ultraviolet band are adjacent bands or the same band. Based on the setting of the simulated ultraviolet band range, the band characteristics of the existing spectral data are comprehensively analyzed according to the target requirements, and the appropriate first ground object 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 object reflectance is matched with the second equivalent ground object reflectance to obtain first ground object spectral data similar to or identical to the multiple subdivided near ultraviolet bands.

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

[0052] According to an embodiment of the present invention, the second ground object spectrum data characterizes the radiation characteristics of the ground object in the ultraviolet band. The ultraviolet band generally includes two bands: near ultraviolet and mid-ultraviolet.

[0053] According to an embodiment of the present invention, the prediction model may be a machine learning algorithm, such as a neural network, a 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 feature spectral data may be obtained from the second spectral image.

[0054] According to an embodiment of the present invention, the first ground object spectral data is preprocessed, classified and sorted to ensure that the data is adapted to the input requirements of the prediction model. For example, the first ground object spectral data is normalized.

[0055] In operation S150, based on the atmospheric effect parameters and the ground 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 the atmospheric radiation transfer analytical 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 radiation transfer analytical 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 may be a MODTRAN (MODerate resolution atmospheric TRANsmission, used for atmospheric radiation transfer calculation) model.

[0058] Atmospheric effect parameters can be transmittance, reflectivity, radiation brightness, etc.

[0059] In operation S160, the second spectral image is processed 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 from the scene received by the sensor.

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

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

[0063] According to an embodiment of the present invention, a ground object spectral data set is obtained 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 reflectance of the spectral band and the second equivalent ground object reflectance of the simulated ultraviolet band are calculated respectively; the first equivalent ground object reflectance matches the second equivalent ground object reflectance, indicating 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 the prediction model to obtain the second ground object spectral data and the second spectral image of the ultraviolet band; based on the atmospheric effect parameters and the surface information of the second spectral image, the sensor entrance pupil radiance of the simulated ultraviolet band is calculated, and the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transmission analytical model; the second spectral image is image processed using the sensor entrance pupil radiance to obtain the target spectral image. Therefore, the ultraviolet surface spectral data and the ultraviolet surface spectral image are extracted from the spectral database.

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

[0065] For the second spectrum image generated by simulation, various atmospheric radiation effects are simulated based on the atmospheric radiation transfer analytical model, and the atmospheric effect parameters are calculated using the atmospheric radiation transfer simulation software, and finally the radiance at the sensor entrance pupil is obtained by combining the surface information and the atmospheric effect. For example, the atmospheric radiation transfer analytical model can be the MODTRAN (MODerateresolution atmospheric TRANsmission, used for atmospheric radiation transfer calculation) model.

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

[0067] (1);

[0068] The first term on the right side of the equation represents the radiance of direct sunlight hitting the target and then reflected into the sensor; the second term represents the radiance of scattered light from space hitting the background target and then reaching the sensor; the third term represents the path radiation. Surface information includes and . is the surface reflectance of the target pixel in the second spectral image; The ground reflectance representing the background in the second spectral image is defined as the average reflectance value of the surrounding pixels; , , and is the atmospheric effect parameter, where The large balloon-shaped albedo representing the ground reflecting upward; A coefficient representing the total amount of solar radiation reaching the top of the atmosphere after being reflected by the target pixel; A coefficient that represents the total amount of solar radiation reaching the top of the atmosphere that is reflected by the background; A coefficient that represents the amount of solar radiation that reaches the top of the atmosphere only after being scattered in the atmosphere. is the reference entrance pupil radiance.

[0069] To simulate and calculate the atmospheric effect parameters of the target area using atmospheric radiation transmission software, it is necessary to obtain the imaging conditions of the second spectral image, 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 consistent with those of the first spectral image. For wide-band images, the atmospheric conditions between pixels vary greatly. In order to ensure the simulation accuracy, the atmospheric effect parameters of each pixel should be obtained, which can be achieved by establishing an atmospheric effect parameter lookup table and interpolating each pixel.

[0070] According to an embodiment of the present invention, based on the spectral equivalence mechanism, respectively calculating the first equivalent ground object reflectance of the spectral band and the second equivalent ground object reflectance of the simulated ultraviolet band includes: inputting the sensor parameters of the simulated ultraviolet band into the fitting response model to obtain the spectral response function value of the ultraviolet band; based on the spectral equivalence mechanism, calculating the second equivalent ground object reflectance according to the spectral response function value 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 value at each wavelength in the ultraviolet band or use function fitting to simulate the spectral response function value at each wavelength in the spectral band. The spectral response function of the spectral band can also be obtained on the official website of the aerospace platform. By bringing each wavelength of the first ground feature spectral band of the spectral band into the spectral response function, the spectral response function value at each wavelength in the spectral band can be obtained.

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

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

[0074] According to an embodiment of the present invention, the sensor center wavelength is a 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, the sensor parameters of the simulated ultraviolet band are input into the fitting response model to obtain the spectral response function value of the ultraviolet band, including: inputting the sensor center wavelength and spectral resolution of each simulated ultraviolet sub-band in the simulated ultraviolet band into the fitting response model to obtain the spectral response function value of each ultraviolet sub-band.

[0076] The fitting function of the fitting response model is a Gaussian function constructed based on the central wavelength of the sensor and the wavelengths of each simulated ultraviolet band.

[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 sensor center wavelength, λ i To simulate the wavelengths of each band in the ultraviolet band, λ0 is the central wavelength of the sensor, the central 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 10nm (nanometers), the FWHM (full width at half maximum) value is equal to 10.

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

[0082] Figure 2 A curve diagram showing the spectral response function values ​​of the simulated ultraviolet band according to an embodiment of the present invention is shown.

[0083] When the spectral resolution is 10nm, the simulated ultraviolet band is in the range of 300-400nm, the horizontal axis is the wavelength, and the vertical axis is the spectral response value. The simulated ultraviolet band is subdivided into 10 ultraviolet sub-bands (S1-S10). The sensor center wavelength λ0 of each simulated ultraviolet sub-band in the simulated ultraviolet band is set to 305nm, 315nm, 325nm, 335nm, 345nm, 355nm, 365nm, 375nm, 385nm, and 395nm respectively. Each simulated ultraviolet sub-band can also be used as each simulation channel. The spectral response function value curve of each ultraviolet sub-band (S1-S10) is as follows: Figure 2 shown.

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

[0085] For the spectral data in the spectral database, equivalent calculations are performed using the spectral response function values ​​of the spectral band and the spectral response function values ​​of the simulated ultraviolet band, thereby obtaining the equivalent surface reflectance of the spectral channel and the simulated channel.

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

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

[0088] For example, the first ground object spectrum data may be the first ground object reflectivity, and the predicted second ground object spectrum data may be the second ground object reflectivity 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 also includes: calculating the correlation between the first equivalent ground object reflectance and the second equivalent ground object reflectance to obtain a correlation value; and when the correlation value satisfies a preset correlation threshold, determining that the first equivalent ground object reflectance matches the second equivalent ground object reflectance.

[0090] The first equivalent ground object reflectance of the spectral channel and the second equivalent ground object reflectance of the simulation channel are subjected to Pearson's correlation analysis. Based on the correlation analysis results, the bands of the ground object spectral data set that can participate in the production of training data sets and prediction model training are determined. The band with a high correlation with the ultraviolet band is generally the visible light band, and the closer it is to the ultraviolet band spectral channel, the stronger its information correlation is.

[0091] The 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 that the spectral band is positively correlated with the simulated ultraviolet band; if r<0, it indicates that the spectral band is negatively correlated with the simulated ultraviolet band.

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

[0093] According to an embodiment of the present invention, the prediction model is trained based on the following method: inputting ground object spectrum training data and ground object spectrum training images into the prediction model to obtain a prediction value, and matching the training equivalent ground object reflectivity of the ground object spectrum training data with the second equivalent ground object reflectivity; calculating the error between the true value and the prediction value corresponding to the ground object spectrum training data; obtaining the prediction model to be tested when the error satisfies a preset error value; and determining the prediction model to be tested as the trained prediction model based on the test result of the prediction model to be tested.

[0094] For example, the spectral training data of ground objects in the spectral database can be divided into a training set and a test set in a ratio of 7:3. The error can be a ten-fold cross-validation error, and the preset error value can be the minimum value reached by the error. The training set is input into the prediction model, and the parameters are tuned according to the principle of minimum ten-fold cross-validation error to determine the prediction model to be tested with the optimal parameter configuration. Based on the optimal parameter configuration, the prediction model to be tested is regressed using the test set and the model accuracy is evaluated to 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 a trained prediction model includes: performing a regression test on the prediction model to be tested to obtain a regression test result; when the regression test result indicates that the prediction model to be tested has passed the regression test, performing a model accuracy evaluation on the prediction model to be tested to obtain an accuracy evaluation result; when the accuracy evaluation result indicates that the model accuracy evaluation of the prediction model to be tested has passed, determining the prediction model to be tested as a trained prediction model.

[0096] The prediction model includes multiple sub-models, and the above method also includes: using the multiple sub-models to predict the ground object spectral training data to obtain reference prediction values ​​of the multiple sub-models; and determining the prediction value of the prediction model based on the multiple reference prediction values.

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

[0098] (3);

[0099] Where t=1,2,…,T, It 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-model can be a decision tree. 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 training subsets and test subsets for each spectral channel. The training subsets and test subsets are used to train each decision tree.

[0101] For example, the prediction results of each decision tree are used to make the final prediction. In regression tasks, the random forest regression model usually uses the average of all decision tree predictions 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, a portion of features are randomly selected from all features at each node, and then the optimal split point is found based on these features.

[0102] The prediction process of the prediction model can extract the first ground feature spectrum data of the visible light band adjacent to the simulated ultraviolet band with high correlation from the spectrum database. The first spectrum image is preprocessed, such as format conversion, reprojection, cloud removal, etc. The image data with a reflectivity range of 0-10000 after preprocessing is subjected to band operation so that the reflectivity range is between 0-1, and the visible light band data is band synthesized to ensure that the data format is suitable for the prediction model input format.

[0103] The second ground object spectrum data and the second spectrum image are generated by using sub-models of different spectral channels. For example, the pre-processed first spectrum image data and the first ground object spectrum data are input into the trained random forest regression model, and the random forest regression model outputs the ultraviolet surface background radiation value of each pixel, that is, the reflectivity value (second ground object spectrum data), and generates an ultraviolet surface reflectivity image (second spectrum image) based on the second ground object spectrum data.

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

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

[0106] In operation S310 , a ground object spectrum data set is acquired from a spectrum database, where the ground object spectrum data set includes at least one ground object spectrum data.

[0107] In operation S320, a spectral response function value of a simulated ultraviolet band and a spectral response function value corresponding to at least one ground object spectral data are obtained.

[0108] In operation S330 , for each ground object spectrum data, based on a spectrum equivalence mechanism, a training equivalent ground object reflectance of the ground object spectrum training data and a second equivalent ground object reflectance in a simulated ultraviolet band are calculated.

[0109] In operation S340, when the Pearson correlation between the training equivalent object reflectance and the second equivalent object reflectance meets a preset correlation threshold, the object spectrum data is determined as the object spectrum training data; and an object spectrum training image corresponding to the object spectrum training data is obtained from the spectrum database.

[0110] In operation S350 , a model data set is constructed based on the ground object spectrum training data and the ground object spectrum training image, and the model data set is divided into a training set and a test set.

[0111] In operation S360, the prediction model is trained using the training set to obtain a prediction model to be tested.

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

[0113] In operation S380, when the test result indicates that the prediction model to be tested has passed the test, a trained prediction model is obtained.

[0114] The method for simulating and generating ultraviolet band surface radiation data combines the real satellite image (first spectral image) and the first ground object spectral data, and inputs the first ground object spectral data and the first spectral image similar to the simulated ultraviolet band into the prediction model, and the ultraviolet band ground surface background radiation data (second ground object spectral data) and the second spectral image can be obtained. This technical point makes it possible to obtain ultraviolet ground surface background radiation data and spectral images through this method even when ultraviolet satellite images are scarce, which has important practical application value.

[0115] First, the background radiation data of the ground objects in the ultraviolet band can be obtained. At present, the background radiation data inverted by satellite observation data are mainly concentrated in the visible light, near infrared, short-wave infrared and thermal infrared channels. In contrast, there is a lack of surface background radiation images covering the near-ultraviolet band, and it is difficult to generate a surface background radiation model in the ultraviolet band through methods such as ground object radiation models, spectral mixing models and spectral channel correlation modeling. The embodiment of the present invention is based on the visible light band data and ground object spectral data of satellite data. 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 image of the ultraviolet band are generated.

[0116] Secondly, it can provide high-resolution and high-quality ultraviolet band spectral images. 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 simulation channel and setting the spectral resolution of the simulation channel, a near-ultraviolet band hyperspectral image can be generated. In addition, by inputting high-resolution and cloudless high-quality satellite data into the random forest regression model, a near-ultraviolet band spectral image 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 atmospheric effect of the second spectral image, the second spectral image is input into the atmospheric radiation transmission analytical model to obtain the atmospheric effect parameters. 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; the second spectral image is processed using the sensor entrance pupil radiance to obtain the target spectral image. This makes the spectral image of 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 data sources. 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 calculated by spectral equivalent calculation to obtain the training equivalent ground object reflectance, and the training equivalent ground object reflectance is correlated with the second equivalent reflectance to obtain the ground object spectral training data and ground object spectral training images of the bands adjacent to the simulated ultraviolet band; the ground object spectral training data and ground object spectral training images are used to train the prediction model, 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 A block diagram of a device for simulating and generating ultraviolet band surface radiation data according to an embodiment of the present invention is shown.

[0120] like Figure 4 As 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 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, the first ground object spectral data representing 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 repeated here.

[0122] The first calculation module 420 is used to calculate the first equivalent ground object reflectance of the spectral band and the second equivalent ground object reflectance of the simulated ultraviolet band based on the spectral equivalence mechanism, 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. In one embodiment, the calculation module 420 can be used to perform the operation S120 described above, which will not be repeated here.

[0123] The second acquisition module 430 is used 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. In one embodiment, the second acquisition module 430 can be used to perform the operation S130 described above, which will not be described in detail here.

[0124] The input module 440 is used to input the first ground object spectral data and the first spectral image into the prediction model to obtain the second ground object spectral data and the second spectral image of the simulated ultraviolet band on the ground surface, and the second ground object spectral data represents the radiation characteristics of the ground object in the simulated ultraviolet band. In one embodiment, the second input module 440 can be used to perform the operation S140 described above, which will not be repeated here.

[0125] The second calculation module 450 is used to calculate the sensor entrance pupil radiance of the simulated ultraviolet band based on the atmospheric effect parameters and surface information of the second spectral image, and the atmospheric effect parameters are obtained by inputting the second spectral image into the atmospheric radiation transmission analytical model. In one embodiment, the calculation module 450 can be used to perform the operation S150 described above, which will not be repeated here.

[0126] The image processing module 460 is used 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 can be used to perform the operation S160 described above, which will not be described in detail here.

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

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

[0129] According to an embodiment of the present invention, the first input submodule includes a first input unit for inputting the sensor center wavelength and spectral resolution of each simulated ultraviolet sub-band in the simulated ultraviolet band into the 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 submodule and a determination submodule. The second calculation submodule is used to calculate the correlation between the first equivalent ground object reflectivity and the second equivalent ground object reflectivity to obtain a correlation value; the determination submodule is used to determine that the first equivalent ground object reflectivity matches the second equivalent ground object reflectivity when the correlation value meets a preset correlation threshold.

[0131] According to an embodiment of the present invention, the prediction model is trained in the following manner: inputting ground object spectral training data into the prediction model to obtain a prediction value, and matching the training equivalent ground object reflectivity of the ground object spectral training data with the second equivalent ground object reflectivity; constructing a model data set based on the first equivalent ground object reflectivity and the second equivalent surface reflectivity of the ground object 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 object spectral training data; obtaining a prediction model to be tested when the error satisfies a preset error value; and determining the prediction model to be tested as the trained prediction model based on the test result of the prediction model to be tested.

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

[0133] According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits, or at least part of the functions of any one of them can be implemented in one module. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be split into multiple modules for implementation. According to the embodiments of the present invention, any one or more of the modules, submodules, units, and subunits can be at least partially implemented as hardware circuits, such as field programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems on chips, systems on substrates, systems on packages, application specific integrated circuits (ASICs), or can be implemented by hardware or firmware in any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware, and firmware, or by a proper combination of any of them. Alternatively, according to the embodiments of the present invention, one or more of the modules, submodules, units, and subunits can be at least partially implemented as computer program modules, and when the computer program modules are run, the corresponding functions can be executed.

[0134] For example, any multiple 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 can be combined in one module / unit / sub-unit for implementation, or any one of the modules / units / sub-units can 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 can 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 an embodiment 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 a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented by hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or by any one of the three implementation methods of software, hardware and firmware or by a proper combination of any 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 function may be executed.

[0135] It should be noted that the ultraviolet band surface radiation data simulation and generation device part in the embodiment of the present invention corresponds to the ultraviolet band surface radiation data simulation and generation method part in the embodiment of the present invention. The description of the ultraviolet band surface radiation data simulation and generation device part specifically refers to the ultraviolet band surface radiation data simulation and generation method part, which will not be repeated here.

[0136] Figure 5 A block diagram of an electronic device suitable for implementing a method for simulating and generating ultraviolet band surface radiation data according to an embodiment of the present invention is shown.

[0137] Figure 5 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0138] like Figure 5 As 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 a program stored in a read-only memory (ROM) 502 or a program loaded from a storage part 508 to a random access memory (RAM) 503. The processor 501 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 501 may also include an onboard 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 an embodiment of the present invention.

[0139] In RAM 503, various programs and data required for the operation of the electronic device are stored. The processor 501, ROM 502 and RAM 503 are connected to each other via a bus 504. The processor 501 performs various operations of the method flow according to the embodiment of the present invention by executing the programs in ROM 502 and / or RAM 503. It should be noted that the program can also be stored in one or more memories other than ROM 502 and RAM 503. The processor 501 can also perform various operations of the method flow according to the embodiment 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, which 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 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 drive 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 installed on the drive 510 as needed, so that a computer program read therefrom is installed into the storage portion 508 as needed.

[0141] According to an embodiment of the present invention, the method flow according to an embodiment of the present invention can 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 includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part 509, and / or installed from the removable medium 511. When the computer program is executed by the processor 501, the above-mentioned functions defined in the system of the embodiment of the present invention are executed. According to an embodiment of the present invention, the system, equipment, device, module, unit, etc. described above can be implemented by a computer program module.

[0142] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiment; or may exist independently without being assembled into the device / apparatus / 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: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, an apparatus or a device.

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

[0145] An embodiment of the present invention also includes a computer program product, which includes a computer program, and 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 / device of the embodiment of the present invention are executed. According to the embodiment of the present invention, the system, device, module, unit, etc. described above can be implemented by a computer program module.

[0147] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices, magnetic storage devices, etc. In another embodiment, the computer program may also be transmitted and distributed in the form of signals on a network medium, and downloaded and installed through the communication part 509, and / or installed from the removable medium 511. The program code contained in the computer program may be transmitted using any appropriate 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, the program code for executing the computer program provided by the embodiment 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 process and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, Java, C++, python, "C" language or similar programming languages. The program code can be executed entirely on the user computing device, partially on the user device, partially on the remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the 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 box may also occur in an order different from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions. It can be understood by those skilled in the art that the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways, even if such a combination or combination is not explicitly recorded in the present invention. In particular, without departing from the spirit and teachings of the present invention, the features recorded in the various embodiments of the present invention can be combined and / or combined in various ways. All these combinations and / or combinations fall within the scope of the present invention.

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

Claims

1. A method for simulating and generating ultraviolet band surface radiation data, characterized in that: include: According to the type of the ground object, a ground object spectrum data set is acquired from a spectrum database, wherein the ground object spectrum data set includes first ground object spectrum data, and the first ground object spectrum data represents the radiation characteristics of the ground object in a spectral band; Based on the spectral equivalence mechanism, a first equivalent ground object reflectance of the spectral band and a second equivalent ground object reflectance of the simulated ultraviolet band are calculated, wherein 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, acquiring a first spectral image corresponding to the first ground object spectral data; Inputting the first ground object spectrum data and the first spectrum image into a prediction model to obtain second ground object spectrum data and a second spectrum image of a simulated ultraviolet band on the ground surface, wherein the second ground object spectrum data represents the radiation characteristics of the ground object in the simulated ultraviolet band; Based on the atmospheric effect parameters of the second spectral image and the surface information, the sensor entrance pupil radiance of the simulated ultraviolet band is calculated, wherein the atmospheric effect parameters are obtained by inputting the second spectral image into an atmospheric radiation transmission analytical model; The second spectral image is processed using the sensor entrance pupil radiance to obtain a target spectral image.

2. The method according to claim 1, characterized in that The method of calculating the first equivalent ground object reflectance of the spectral band and the second equivalent ground object reflectance of the simulated ultraviolet band based on the spectral equivalence mechanism comprises: Inputting the sensor parameters of the simulated ultraviolet band into a fitting response model to obtain a spectral response function value of the ultraviolet band; Based on the spectral equivalence mechanism, the second equivalent ground object reflectance is calculated according to the spectral response function value of the simulated ultraviolet band.

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

4. The method according to claim 3, characterized in that The step of inputting the sensor parameters of the simulated ultraviolet band into a fitting response model to obtain the spectral response function value of the ultraviolet band comprises: The sensor center wavelength and the spectral resolution of each simulated ultraviolet sub-band in the simulated ultraviolet band are input into a fitting response model to obtain the spectral response function value of each ultraviolet sub-band.

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

6. The method according to claim 1, characterized in that The step of acquiring 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 also includes: Calculating the correlation between the first equivalent ground object reflectivity and the second equivalent ground object reflectivity to obtain a correlation value; When the correlation value satisfies a preset correlation threshold, it is determined that the first equivalent terrain object reflectivity matches the second equivalent terrain object reflectivity.

7. The method according to claim 1, characterized in that The prediction model is trained based on the following method: Inputting the ground object spectrum training data into the prediction model to obtain a prediction value, wherein the training equivalent ground object reflectance of the ground object spectrum training data matches the second equivalent ground object reflectance; Calculating the error between the true value corresponding to the ground object spectral training data and the predicted value; When the error satisfies a preset error value, a prediction model to be tested is obtained; Based on the test result of the prediction model to be tested, the prediction model to be tested is determined as a trained prediction model.

8. The method according to claim 7, characterized in that The test result includes a regression test result and an accuracy evaluation result, and the step of determining the prediction model to be tested as a trained prediction model based on the test result of the prediction model to be tested includes: Performing a regression test on the prediction model to be tested to obtain the regression test result; When the regression test result indicates that the prediction model to be tested has passed the regression test, performing a model accuracy assessment on the prediction model to be tested to obtain the accuracy assessment result; In a case where the accuracy evaluation result indicates that the prediction model to be tested has passed the model accuracy evaluation, the prediction model to be tested is determined as the trained prediction model.

9. The method according to claim 1, characterized in that: The prediction model includes a plurality of sub-models, and the method further includes: Using the plurality of sub-models to predict the ground object spectrum training data, and obtaining reference prediction values ​​of the plurality of sub-models; The prediction value of the prediction model is determined based on the multiple reference prediction values.

10. The method according to claim 1, characterized in that The atmospheric effect parameters include the albedo of the large balloon reflected upward by the ground and the coefficient of the solar radiation amount that simulates the ultraviolet band and reaches the top of the atmosphere after being scattered in the atmosphere; The step of calculating the sensor entrance pupil radiance of the simulated ultraviolet band based on the atmospheric effect parameters and the surface information of the second spectral image includes: Based on the large balloon-shaped albedo reflected upward by the ground and the surface information, a reference entrance pupil radiance is calculated; The sensor entrance pupil radiance of the simulated ultraviolet band is calculated based on the reference entrance pupil radiance and the coefficient of the solar radiation amount that reaches the top of the atmosphere after the simulated ultraviolet band is scattered in the atmosphere.

Images