A method and device for inverting surface reflectivity based on atmospheric parameter library

Abstract

This specification discloses a surface reflectivity inversion method based on an atmospheric parameter library, which can obtain remote sensing data to be inverted and determine the acquisition environment information corresponding to the remote sensing data to be inverted, and the acquisition environment information includes the imaging time and position information corresponding to the remote sensing data to be inverted. Then, according to the acquisition environment information, the target atmospheric parameters matching the remote sensing data to be inverted can be searched from the pre-constructed atmospheric parameter library, the atmospheric parameter library is obtained by data fusion of several different data sources, and the atmospheric parameter library contains atmospheric parameters corresponding to a certain time position. Finally, according to the target atmospheric parameters, the surface reflectivity of the remote sensing data to be inverted can be inverted to obtain the inverted preliminary surface reflectivity, and according to the preliminary surface reflectivity, the surface reflectivity image can be determined, so the method can extract the correct atmospheric parameters to invert the surface reflectivity, thereby improving the correctness and applicability of the surface reflectivity inversion.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing satellite inversion technology, and in particular to a method and device for inverting surface reflectivity based on an atmospheric parameter library. Background Art

[0002] The total radiation brightness of ground targets measured by remote sensing sensors is not a true reflection of the surface reflectivity, but a superposition of surface signals and atmospheric signals. Due to the influence of the atmosphere on radiation transmission, there are errors in atmospheric absorption and scattering in remote sensing images. These effects will not only cause the clarity and contrast of remote sensing images to decrease, but also affect the accuracy and reliability of remote sensing data in quantitative analysis and information extraction.

[0003] Currently, mainstream land observation satellites use an atmospheric correction method based on a radiation transfer model to invert surface reflectivity. The effect of atmospheric correction strongly depends on the accuracy of the input atmospheric parameters.

[0004] In the prior art, the atmospheric parameters can be inverted through the dense vegetation algorithm (Dense Dark Vegetation, DDV), and then the obtained atmospheric parameters are used to invert the surface reflectivity. However, this method is often not accurate enough and has low applicability.

[0005] Therefore, how to improve the accuracy and applicability of surface reflectivity inversion is an urgent problem to be solved. Summary of the invention

[0006] This specification provides a surface reflectivity inversion method and device based on an atmospheric parameter library to partially solve the above-mentioned problems existing in the prior art.

[0007] This manual adopts the following technical solutions:

[0008] This specification provides a surface reflectivity inversion method based on an atmospheric parameter library, including:

[0009] Obtain remote sensing data to be inverted;

[0010] Determine acquisition environment information corresponding to the remote sensing data to be inverted, wherein the acquisition environment information includes imaging time and position information corresponding to the remote sensing data to be inverted;

[0011] According to the collected environmental information, target atmospheric parameters matching the remote sensing data to be inverted are searched from a pre-built atmospheric parameter library, wherein the atmospheric parameter library is obtained by fusing data from a number of different data sources using a preset data assimilation model, and the atmospheric parameter library contains atmospheric parameters corresponding to different times and locations;

[0012] Inverting the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain an inverted preliminary surface reflectivity;

[0013] A surface reflectance image is determined based on the preliminary surface reflectance.

[0014] Optionally, constructing the atmospheric parameter library specifically includes:

[0015] Unifying the data resolutions of the several different data sources to obtain an adjusted data source;

[0016] The adjusted data source is input into a data assimilation model, and the adjusted data source is interpolated by an optimal interpolation algorithm in the data assimilation model to construct the atmospheric parameter library, wherein the interpolation includes filling in missing data in each data source in the adjusted data source according to other data sources except the data source.

[0017] Optionally, inverting the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain the inverted preliminary surface reflectivity specifically includes:

[0018] Determine the DN value in the remote sensing data to be inverted, and convert the DN value into apparent reflectivity;

[0019] The preliminary surface reflectivity is determined according to the target atmospheric parameters and the apparent reflectivity.

[0020] Optionally, determining the preliminary surface reflectivity according to the target atmospheric parameter and the apparent reflectivity specifically includes:

[0021] According to the target atmospheric parameters, determine the gas absorption transmittance, atmospheric path radiation, atmospheric hemispherical reflectivity, and atmospheric downward and upward scattering transmittance;

[0022] The preliminary surface reflectivity is determined based on the gas absorption transmittance, the atmospheric path radiation, the atmospheric hemispherical reflectivity, the atmospheric downward and upward scattering transmittance, the apparent reflectivity, the solar zenith angle corresponding to the remote sensing data to be inverted, the sensor observation zenith angle and the relative azimuth angle between the sensor and the sun, and the observation wavelength.

[0023] Optionally, determining a surface reflectance image according to the preliminary surface reflectance specifically includes:

[0024] Performing proximity effect correction on the preliminary surface reflectivity to determine a corrected surface reflectivity;

[0025] The surface reflectance image is determined according to the corrected surface reflectance.

[0026] Optionally, performing proximity effect correction on the preliminary surface reflectivity to determine the corrected surface reflectivity specifically includes:

[0027] For each pixel in the image corresponding to the preliminary surface reflectance, the average reflectance of neighboring pixels within a preset range of the pixel is determined, and proximity effect correction is performed according to the intensity of the proximity scattering effect and the average reflectance corresponding to the pixel to obtain the corrected surface reflectance corresponding to the pixel.

[0028] Optionally, determining the surface reflectance image according to the corrected surface reflectance specifically includes:

[0029] Identifying clouds in the image corresponding to the corrected surface reflectance and masking them;

[0030] Classifying the surface types of the image to obtain various categories;

[0031] Eliminate the cloud, cloud shadow and mountain projection shadow in the image to obtain an image after elimination, and determine the pixels with a slope not less than a preset slope from the image after elimination as the pixels to be corrected;

[0032] For each category, a linear regression is performed between the original reflectivity of the pixel to be corrected in that category and the cosine of the solar incident angle to obtain the terrain correction coefficient corresponding to that category;

[0033] According to the terrain correction coefficient corresponding to the category, terrain correction is performed on the pixels to be corrected in this category to obtain the surface reflectance image.

[0034] This specification provides a surface reflectivity inversion device based on an atmospheric parameter library, including:

[0035] An acquisition module is used to acquire remote sensing data to be inverted;

[0036] A determination module, used to determine the acquisition environment information corresponding to the remote sensing data to be inverted, wherein the acquisition environment information includes imaging time and position information of the remote sensing data to be inverted;

[0037] A search module is used to search for target atmospheric parameters that match the remote sensing data to be inverted from a pre-built atmospheric parameter library according to the collected environmental information, wherein the atmospheric parameter library is obtained by fusing data from a number of different data sources using a preset data assimilation model, and the atmospheric parameter library contains atmospheric parameters corresponding to different locations at different times;

[0038] The inversion module is used to invert the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain the inverted preliminary surface reflectivity, and determine the surface reflectivity image according to the preliminary surface reflectivity.

[0039] The present specification provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned surface reflectivity inversion method based on the atmospheric parameter library is implemented.

[0040] This specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned surface reflectivity inversion method based on an atmospheric parameter library when executing the program.

[0041] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0042] It can be seen from the above-mentioned surface reflectivity inversion method based on the atmospheric parameter library that the server can obtain the remote sensing data to be inverted and determine the acquisition environment information corresponding to the remote sensing data to be inverted, and the acquisition environment information includes the imaging time and position information corresponding to the remote sensing data to be inverted. Then, according to the acquisition environment information, the target atmospheric parameters matching the remote sensing data to be inverted can be searched from the pre-built atmospheric parameter library. The atmospheric parameter library is obtained by data fusion of several different data sources through a preset data assimilation model, and the atmospheric parameter library contains atmospheric parameters corresponding to different times and locations. Finally, according to the target atmospheric parameters, the surface reflectivity of the remote sensing data to be inverted can be inverted to obtain the inverted preliminary surface reflectivity, and the surface reflectivity image can be determined based on the preliminary surface reflectivity.

[0043] It can be seen from the above content that compared with the prior art, the present invention utilizes the atmospheric parameters corresponding to each time position obtained by fusing multiple data sources, so that when inverting the surface reflectivity of remote sensing data, the correct atmospheric parameters can be directly extracted according to the collection time and location information corresponding to the remote sensing data, so as to invert the surface reflectivity, thereby improving the accuracy and applicability of the surface reflectivity inversion. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0045] Figure 1 A schematic diagram of a flow chart of a surface reflectivity inversion method based on an atmospheric parameter library provided in this specification;

[0046] Figure 2 The image of the apparent reflectance of the panchromatic band image of the GF-2 multispectral image provided in this specification;

[0047] Figure 3 The GF-2 multispectral image panchromatic band image surface reflectance image provided in this specification;

[0048] Figure 4 A schematic diagram of a surface reflectivity inversion device based on an atmospheric parameter library provided in this specification;

[0049] Figure 5 The corresponding Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0051] In the prior art, the way to obtain atmospheric parameters through the DDV algorithm is to determine the surface reflectance with the help of the empirical relationship between the surface reflectance of dense vegetation pixels in the short-wave infrared and red light bands on the image, and then invert the aerosol optical depth.

[0052] When the multispectral camera used by the satellite is a four-band visible light sensor (such as the Gaofen satellite series, the Resources satellite series, the Four-Dimensional Gaojing series of satellites, etc.), it is difficult to achieve the ground-atmosphere decoupling of the remote sensing observation radiation signal due to the lack of short-wave infrared bands, and it is more difficult to invert the aerosol optical thickness and aerosol type from the satellite data itself.

[0053] Therefore, a surface reflectivity inversion method based on an atmospheric parameter library is provided in this specification. By searching the atmospheric parameter library, the atmospheric parameters are obtained, and then the surface reflectivity is inverted. Thus, it can be applied to a variety of satellites, thereby improving the applicability and accuracy of the surface reflectivity inversion.

[0054] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0055] Figure 1 This is a schematic diagram of a surface reflectivity inversion method based on an atmospheric parameter library provided in this specification, which specifically includes the following steps:

[0056] S100: Acquire remote sensing data to be inverted.

[0057] S102: Determine acquisition environment information corresponding to the remote sensing data to be inverted, where the acquisition environment information includes imaging time and position information corresponding to the remote sensing data to be inverted.

[0058] S104: According to the collected environment information, target atmospheric parameters matching the remote sensing data to be inverted are searched from a pre-constructed atmospheric parameter library, wherein the atmospheric parameter library is obtained by fusing data from a plurality of different data sources through a preset data assimilation model.

[0059] S106: Inverting the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain an inverted preliminary surface reflectivity.

[0060] S108: Determine a surface reflectance image according to the preliminary surface reflectance.

[0061] In practical applications, in order to eliminate the influence of the atmosphere, it is necessary to invert the surface reflectivity of the remote sensing image. This specification provides a method for inverting the surface reflectivity.

[0062] The basis of this method is to construct an atmospheric parameter library with complete atmospheric parameters in time and space, and then based on the atmospheric parameter library, perform surface reflectivity inversion on the collected remote sensing data.

[0063] The above-mentioned atmospheric parameter library can be obtained by fusing several different data sources through a preset data assimilation model. The atmospheric parameter library contains atmospheric parameters corresponding to different times and locations. The data sources mentioned here may include: ECMWF CAMS reanalysis data, MODIS satellite products, Himawari, GOES, MSG and other geostationary satellite atmospheric products, HJ-2 PSAC satellite products, etc.

[0064] Since there are multiple data sources and there are certain differences between the data sources, it is necessary to perform the above-mentioned data fusion to obtain an atmospheric parameter library that is continuous and unified in time and space. The main performance parameters contained in the atmospheric parameter library can be shown in Table 1 below.

[0065] Table 1

[0066] Main performance parameters index parameter The main parameters include: aerosol optical thickness, total water vapor column, total ozone column, altitude, presence of clouds, aerosol type time 10:00-14:00 local time Spatial resolution 0.1° × 0.1° (10 km × 10 km) Data Source ECMWF database HJ-2 satellite atmospheric correction instrument PSA CMODIS satellite atmospheric products Sunflower and other geostationary meteorological satellite atmospheric products scope Global coverage Update time ≤7 days Time Range 2012-Present

[0067] It should be noted that after the atmospheric parameter library is constructed, the atmospheric parameter library needs to be continuously updated over time, and the update cycle can be set according to demand.

[0068] When constructing the atmospheric parameter library, the data resolutions of several data sources can be unified to obtain an adjusted data source, and the adjusted data source can be input into the data assimilation model. The adjusted data source can be interpolated through the optimal interpolation algorithm in the data assimilation model to construct the atmospheric parameter library, wherein interpolation can refer to filling in the missing data in each data source according to other data sources except the data source.

[0069] Specifically, the atmospheric data can be automatically downloaded through the atmospheric parameter automatic download and storage and standardization processing module to perform standardized preprocessing.

[0070] Then, it is necessary to assimilate data of multi-source atmospheric parameters and build a long-term atmospheric parameter library covering the global range. The atmospheric parameters mainly include aerosol optical thickness, aerosol type, total water vapor column and total ozone column.

[0071] Before processing through the data assimilation model, the data source format needs to be adjusted (unified resolution) to obtain the adjusted data source. The overall driving data of the specific model uses ECWFM or NCEP reanalysis data (ECWFM and / or NCEP reanalysis data can be used as the adjusted benchmark data source here because the data resolution of these two data sources is larger than that of other data sources, and the data source with larger resolution can be adjusted to be consistent with the resolution of other data sources). The benchmark data source can be locally nested in a high-precision grid (the local area range can be 10km, and the highest grid resolution can be 1m). The local mentioned here can refer to the data corresponding to the unit resolution in the data source.

[0072] Then, the above-mentioned standardized preprocessed high-quality data can be input into the data assimilation model for assimilation to generate a high-resolution grid spatiotemporally continuous sample data set. The data assimilation model uses the optimal interpolation method.

[0073] When performing surface reflectance inversion of remote sensing images through the constructed atmospheric parameter library, the server can obtain the remote sensing data to be inverted. The acquired data includes the remote sensing images of the remote sensing data to be inverted and the auxiliary data preparation. For a remote sensing data, the remote sensing images of each band and their corresponding header files (*.xml) can be read. In addition to the images, the auxiliary data include load calibration coefficients and channel response files.

[0074] Determine the acquisition environment information corresponding to the remote sensing data to be inverted, and the acquisition environment information includes the imaging time and location information corresponding to the remote sensing data to be inverted. The location information corresponding to the remote sensing data to be inverted mentioned here may refer to the longitude and latitude of the geographical location reflected by the remote sensing data to be inverted. The geographical location can be determined by the position and angle of the sensor when collecting the remote sensing data to be inverted.

[0075] Then, according to the collected environment information, the target atmospheric parameters matching the remote sensing data to be inverted can be searched from the atmospheric parameter library. In other words, the target atmospheric parameters refer to the parameters of the atmosphere at the location of the above position information at the above imaging time.

[0076] Then, the surface reflectivity of the remote sensing data to be inverted can be inverted according to the target atmospheric parameters to obtain an inverted preliminary surface reflectivity, and a surface reflectivity image can be determined according to the preliminary surface reflectivity.

[0077] Among them, before inverting the surface reflectivity through the target atmospheric parameters, the DN value in the remote sensing data to be inverted can be determined first, and the DN value can be converted into apparent reflectivity.

[0078] Formula (1) is used to convert the DN value measured by the satellite sensor into apparent radiance using the satellite calibration coefficient. Then, the apparent radiance is converted into apparent reflectance using the solar zenith angle, the band’s extra-atmospheric solar irradiance, and the sun-earth distance correction factor, i.e., the reflectance of the top of the atmosphere received by the satellite.

[0079] L = Gain × DN + Bias (1)

[0080] Where DN is the observed digital value of the satellite image, L is the equivalent radiance at the entrance pupil of the satellite payload channel, that is, the apparent radiance, and the unit is , Gain and Bias are the calibration coefficient gain and offset respectively (determined by the above load calibration coefficient).

[0081] After the apparent reflectivity is determined, the preliminary surface reflectivity can be determined based on the apparent reflectivity and the target atmospheric parameters through the 6S radiation transmission model. Among them, the gas absorption transmittance, atmospheric path radiation, atmospheric hemispherical reflectivity, and atmospheric downward and upward scattering transmittance can be determined based on the target atmospheric parameters, and then the preliminary surface reflectivity can be determined based on the gas absorption transmittance, atmospheric path radiation, atmospheric hemispherical reflectivity, atmospheric downward and upward scattering transmittance, and apparent reflectivity, the solar zenith angle corresponding to the remote sensing data to be inverted, the sensor observation zenith angle and the relative azimuth angle between the sensor and the sun, and the observation wavelength. The specific calculation formula is given below.

[0082] The apparent radiation received by satellite-borne optical sensors mainly comes from the following aspects: information on scattered solar radiation from atmospheric molecules, aerosol particles, etc. within the sensor's field of view, usually called path radiation; radiation information that directly enters the sensor after reflecting sunlight from the ground; and radiation information from the environment surrounding the sensor's field of view.

[0083] Therefore, the apparent reflectivity of the top of the atmosphere is The relationship between the reflectivity and the surface reflectivity can be approximately expressed as the following formula, which comes from papers related to radiation transfer models (Vermote et al., 1997; Levy et al., 2007):

[0084] (2)

[0085] In the formula are the solar zenith angle, the sensor observation zenith angle, and the relative azimuth angle between the sensor and the sun, λ is the observation wavelength, is the total gas absorption transmittance of absorbing gases such as water vapor and ozone; the first term on the right side of the above formula is the atmospheric radiation term; the second term is the joint contribution of the surface and the atmosphere, is the atmospheric hemispherical reflectivity, is the surface bidirectional reflectivity, and are the total transmittance of the downward and upward atmospheric radiation respectively. , atmospheric hemispherical reflectivity , and downlink and uplink scattering transmittance All are functions of aerosol optical thickness τ.

[0086] By modifying the above formula (2), the following formula (3) can be obtained, where the meanings of the letter elements in formula (3) are the same as those in formula (2). Based on the searched target atmospheric parameters and the imaging geometry angle extracted from the header file, the 6S radiation transfer model can be used to calculate the absorption transmittance, scattering rate, atmospheric molecular path radiation and other parameters corresponding to the atmospheric parameters and the imaging geometry angle, and the surface reflectance can be calculated pixel by pixel according to formula (3).

[0087] (3)

[0088] In the above formula The surface reflectance to be calculated is the preliminary surface reflectance mentioned above. Part of the right side of formula (3) is data that can be obtained through atmospheric parameters, and part of it is data related to sensors (i.e., remote sensors). Therefore, it can be calculated through target atmospheric parameters and apparent reflectance.

[0089] After determining the above preliminary surface reflectivity, the determined surface reflectivity can be further corrected, wherein the neighboring scattering effect can be first performed. In addition to the atmospheric radiation and the radiation reflected by the surface, for a pixel, the signal received by the sensor also includes some solar radiation reflected by the neighboring pixels around the pixel. This part of the scattering needs to be corrected when inverting the surface reflectivity.

[0090] Based on this, the average reflectivity of each pixel in the image corresponding to the preliminary surface reflectivity can be determined, and the proximity effect correction can be performed according to the intensity of the neighboring scattering effect and the average reflectivity corresponding to the pixel to obtain the corrected surface reflectivity corresponding to the pixel.

[0091] Specifically, based on the surface reflectivity obtained in step S106, the average reflectivity of the neighboring pixels within the preset range of the target pixel (x, y) is calculated. , the target pixel can refer to each pixel in the image corresponding to the preliminary surface reflectance:

[0092] (4)

[0093] N is the number of neighboring pixels within 1 km. It is the preliminary surface reflectance of the pixel (i, j) in the neighborhood calculated in step S106.

[0094] The preliminary surface reflectance of the target pixel is corrected for the proximity effect according to the following formula to obtain the corrected surface reflectance:

[0095] (5)

[0096] q represents the strength of the proximity scattering effect, which is the ratio of diffuse scattering to direct uplink transmittance (surface to satellite sensor):

[0097] (6)

[0098] It should also be noted that if the remote sensing image is taken for a mountainous area with undulating terrain, terrain radiation correction is required. Therefore, if the remote sensing data to be inverted does not involve undulating terrain, the final surface reflectance image can be directly obtained through the above-mentioned corrected surface reflectance. If the remote sensing data to be inverted involves undulating terrain, the following terrain radiation correction needs to be performed on the corrected surface reflectance.

[0099] For mountainous areas with undulating terrain (with a slope greater than 5° as the standard), the DEM and correction model can be combined to perform terrain radiation correction to generate the final surface reflectance image.

[0100] (1) Identify clouds in the image corresponding to the corrected surface reflectance and mask them;

[0101] (2) Classify the surface types of the image and obtain each category;

[0102] (3) Eliminate the cloud, cloud shadow, and mountain projection shadow portions of the image to obtain an image after elimination, and determine pixels with a slope not less than a preset slope from the image after elimination as pixels to be corrected. Pixels with a slope greater than 5° can be selected as pixels to be corrected.

[0103] (4) For each category, the original reflectance of the pixel is the cosine of the solar incidence angle Perform linear regression to obtain the terrain correction coefficient of this category of pixels .

[0104] (7)

[0105] in, , is the solar celestial angle, is the slope, is the solar azimuth, For the slope direction.

[0106] (5) Perform terrain correction on the image to obtain the reflectivity after terrain correction :

[0107] (8)

[0108] Represents the average surface reflectance of this type of pixels in the image.

[0109] The image corresponding to the reflectivity after the terrain correction can be used as the final surface reflectivity image.

[0110] The following examples illustrate the effects of the method provided by this application. Figure 2 , Figure 3 shown.

[0111] Figure 2 It is the image of the apparent reflectance of the panchromatic band image of the GF-2 multispectral image provided in this specification.

[0112] Figure 3 It is the GF-2 multispectral image panchromatic band image surface reflectance image provided in this specification.

[0113] It can be seen that Figure 2 and Figure 3Comparison between the surface reflectance image and the apparent reflectance image of the same remote sensing data shows that a clearer surface reflectance image can be obtained after performing surface reflectance inversion on the remote sensing data using the surface reflectance inversion method in this application.

[0114] Table 2 below shows the relative errors between the surface reflectance inverted by the three methods and the actually measured surface reflectance, where the first row is the method provided by this application, and the other two are existing methods. It can be seen that the method provided by this specification has the least average error in each band.

[0115] Table 2

[0116] First Band Second Band Band 3 Band 4 Atmospheric Parameters Library 6.84% 3.01% 2.86% 5.05% MDOM 10.10% 12.05% 14.63% 2.78% SREM 4.45% 6.67% 5.98% 10.68%

[0117] The present invention has the following advantages:

[0118] 1) High precision: Verified by ground-based measured data synchronized with satellite transit, the product generated by the present invention has high precision and small error, meeting application requirements;

[0119] 2) Practicality: The present invention can be used for the commercial production of multi-spectral cameras for more than 20 civilian and commercial land satellites, and has high practicality;

[0120] 3) Scalability: The present invention can be widely applied to the production of surface reflectance products of four-band (blue, green, red, near-infrared) multispectral sensors, and can be expanded to civil and commercial satellites to be launched in the future.

[0121] It should be noted that, for the sake of convenience, the execution subject of the method is described as a server, and the execution subject of the method can be a computer, a large service platform, etc., which is not limited here. It should also be noted that the features in the following embodiments and implementations can be combined with each other if there is no conflict.

[0122] The above are one or more surface reflectivity inversion methods based on the atmospheric parameter library of this specification. Based on the same idea, this specification also provides a surface reflectivity inversion device based on the atmospheric parameter library, such as Figure 4 shown.

[0123] Figure 4 A schematic diagram of a surface reflectivity inversion device based on an atmospheric parameter library provided for this specification includes:

[0124] An acquisition module 401 is used to acquire remote sensing data to be inverted;

[0125] A determination module 402 is used to determine the acquisition environment information corresponding to the remote sensing data to be inverted, wherein the acquisition environment information includes imaging time and location information of the remote sensing data to be inverted;

[0126] A search module 403 is used to search for target atmospheric parameters that match the remote sensing data to be inverted from a pre-built atmospheric parameter library according to the collected environmental information, wherein the atmospheric parameter library is obtained by fusing data from a number of different data sources using a preset data assimilation model, and the atmospheric parameter library contains atmospheric parameters corresponding to different locations at different times;

[0127] The inversion module 404 is used to invert the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain the inverted preliminary surface reflectivity, and determine the surface reflectivity image according to the preliminary surface reflectivity.

[0128] Optionally, the device further comprises:

[0129] The construction module 405 is used to unify the data resolutions of the several different data sources to obtain an adjusted data source; input the adjusted data source into the data assimilation model, and interpolate the adjusted data source through the optimal interpolation algorithm in the data assimilation model to construct the atmospheric parameter library, wherein the interpolation includes filling in the missing data in each data source in the adjusted data source according to other data sources except the data source.

[0130] Optionally, the inversion module 404 is specifically used to determine the DN value in the remote sensing data to be inverted, and convert the DN value into an apparent reflectivity; and determine the preliminary surface reflectivity according to the target atmospheric parameters and the apparent reflectivity.

[0131] Optionally, the inversion module 404 is specifically used to determine the gas absorption transmittance, atmospheric path radiation, atmospheric hemispherical reflectivity, and atmospheric downward and upward scattering transmittance according to the target atmospheric parameters; and determine the preliminary surface reflectivity according to the gas absorption transmittance, the atmospheric path radiation, the atmospheric hemispherical reflectivity, the atmospheric downward and upward scattering transmittance, and the apparent reflectivity, the solar zenith angle corresponding to the remote sensing data to be inverted, the sensor observation zenith angle and the relative azimuth angle between the sensor and the sun, and the observation wavelength.

[0132] Optionally, the inversion module 404 is specifically configured to perform proximity effect correction on the preliminary surface reflectivity to determine a corrected surface reflectivity; and determine the surface reflectivity image based on the corrected surface reflectivity.

[0133] Optionally, the inversion module 404 is specifically used to determine, for each pixel in the image corresponding to the preliminary surface reflectance, the average reflectance of neighboring pixels within a preset range of the pixel, perform proximity effect correction based on the intensity of the neighboring scattering effect and the average reflectance corresponding to the pixel, and obtain the corrected surface reflectance corresponding to the pixel.

[0134] Optionally, the inversion module 404 is specifically used to identify clouds in the image corresponding to the corrected surface reflectance and perform masking; classify the surface type of the image to obtain each category; eliminate the parts of the image that belong to clouds, cloud shadows and mountain projection shadows to obtain a eliminated image, and determine from the eliminated image pixels with a slope not less than a preset slope as pixels to be corrected; for each category, perform linear regression on the original reflectance of the pixels to be corrected in the category and the cosine of the solar incidence angle to obtain a terrain correction coefficient corresponding to the category; and perform terrain correction on the pixels to be corrected in the category according to the terrain correction coefficient corresponding to the category to obtain the surface reflectance image.

[0135] The present specification also provides a computer-readable storage medium, which stores a computer program. The computer program can be used to execute the above-mentioned surface reflectivity inversion method based on the atmospheric parameter library.

[0136] This manual also provides Figure 5 The schematic structure diagram of the electronic device shown in FIG. Figure 5 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the surface reflectivity inversion method based on the atmospheric parameter library.

[0137] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0138] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0139] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0140] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A surface reflectivity inversion method based on an atmospheric parameter library, characterized in that: include: Obtain remote sensing data to be inverted; Determine acquisition environment information corresponding to the remote sensing data to be inverted, wherein the acquisition environment information includes imaging time and position information corresponding to the remote sensing data to be inverted; According to the collected environmental information, target atmospheric parameters matching the remote sensing data to be inverted are searched from a pre-built atmospheric parameter library, wherein the atmospheric parameter library is obtained by fusing data from a number of different data sources using a preset data assimilation model, and the atmospheric parameter library contains atmospheric parameters corresponding to different times and locations; Inverting the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain an inverted preliminary surface reflectivity; Determining a surface reflectance image according to the preliminary surface reflectance; in Constructing the atmospheric parameter library specifically includes: Unifying the data resolutions of the several different data sources to obtain an adjusted data source; The adjusted data source is input into a data assimilation model, and the adjusted data source is interpolated by an optimal interpolation algorithm in the data assimilation model to construct the atmospheric parameter library, wherein the interpolation includes filling in missing data in each data source in the adjusted data source according to other data sources except the data source.

2. The method according to claim 1, characterized in that According to the target atmospheric parameters, the surface reflectivity of the remote sensing data to be inverted is inverted to obtain the inverted preliminary surface reflectivity, specifically including: Determine the DN value in the remote sensing data to be inverted, and convert the DN value into apparent reflectivity; The preliminary surface reflectivity is determined according to the target atmospheric parameters and the apparent reflectivity.

3. The method according to claim 2, characterized in that Determining the preliminary surface reflectivity according to the target atmospheric parameter and the apparent reflectivity specifically includes: According to the target atmospheric parameters, determine the gas absorption transmittance, atmospheric path radiation, atmospheric hemispherical reflectivity, and atmospheric downward and upward scattering transmittance; The preliminary surface reflectivity is determined based on the gas absorption transmittance, the atmospheric path radiation, the atmospheric hemispherical reflectivity, the atmospheric downward and upward scattering transmittance, the apparent reflectivity, the solar zenith angle corresponding to the remote sensing data to be inverted, the sensor observation zenith angle and the relative azimuth angle between the sensor and the sun, and the observation wavelength.

4. The method according to claim 1, characterized in that Determining a surface reflectance image according to the preliminary surface reflectance includes: Performing proximity effect correction on the preliminary surface reflectivity to determine a corrected surface reflectivity; The surface reflectance image is determined according to the corrected surface reflectance.

5. The method according to claim 4, characterized in that Proximity effect correction is performed on the preliminary surface reflectivity to determine the corrected surface reflectivity, specifically including: For each pixel in the image corresponding to the preliminary surface reflectance, the average reflectance of neighboring pixels within a preset range of the pixel is determined, and proximity effect correction is performed according to the intensity of the proximity scattering effect and the average reflectance corresponding to the pixel to obtain the corrected surface reflectance corresponding to the pixel.

6. The method according to claim 4, characterized in that Determining the surface reflectance image according to the corrected surface reflectance specifically includes: Identifying clouds in the image corresponding to the corrected surface reflectance and masking them; Classifying the surface types of the image to obtain various categories; Eliminate the cloud, cloud shadow and mountain projection shadow in the image to obtain an image after elimination, and determine the pixels with a slope not less than a preset slope from the image after elimination as the pixels to be corrected; For each category, a linear regression is performed between the original reflectivity of the pixel to be corrected in that category and the cosine of the solar incident angle to obtain the terrain correction coefficient corresponding to that category; According to the terrain correction coefficient corresponding to the category, terrain correction is performed on the pixels to be corrected in this category to obtain the surface reflectance image.

7. A surface reflectivity inversion device based on an atmospheric parameter library, characterized in that: include: An acquisition module is used to acquire remote sensing data to be inverted; A determination module, used to determine the acquisition environment information corresponding to the remote sensing data to be inverted, wherein the acquisition environment information includes imaging time and position information of the remote sensing data to be inverted; A search module is used to search for target atmospheric parameters that match the remote sensing data to be inverted from a pre-built atmospheric parameter library according to the collected environmental information, wherein the atmospheric parameter library is obtained by fusing data from a number of different data sources using a preset data assimilation model, and the atmospheric parameter library contains atmospheric parameters corresponding to different times and locations; An inversion module is used to invert the surface reflectivity of the remote sensing data to be inverted according to the target atmospheric parameters to obtain an inverted preliminary surface reflectivity, and determine a surface reflectivity image according to the preliminary surface reflectivity; in Constructing the atmospheric parameter library specifically includes: Unifying the data resolutions of the several different data sources to obtain an adjusted data source; The adjusted data source is input into a data assimilation model, and the adjusted data source is interpolated by an optimal interpolation algorithm in the data assimilation model to construct the atmospheric parameter library, wherein the interpolation includes filling in missing data in each data source in the adjusted data source according to other data sources except the data source.

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

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 6 is implemented.

Images