A method, apparatus, equipment and medium for atmospheric layer identification and extraction

Abstract

This application discloses an atmospheric layer identification and extraction method, apparatus, device, and medium, applied to a pre-set hyperspectral lidar, relating to the field of atmospheric remote sensing technology. The method includes: preprocessing the detection signal from the pre-set hyperspectral lidar to calculate the corresponding atmospheric optical remote sensing signal based on the pre-processed lidar signal and pre-set system parameters; locating the atmospheric region using the pre-processed lidar signal and radar observation data to obtain a region location result; detecting multi-layer atmospheric features based on the region location result and the atmospheric optical remote sensing signal, and verifying and detecting the continuity of the detected multi-layer atmospheric feature matrix to obtain a target feature matrix; and determining the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix. Therefore, the atmospheric optical remote sensing signal detected by the hyperspectral channel of the hyperspectral lidar can be used for aerosol and cloud layer identification.

Description

Technical Field

[0001] This invention relates to the field of atmospheric remote sensing technology, and in particular to a method, apparatus, equipment, and medium for atmospheric layer identification and extraction. Background Technology

[0002] Aerosols and clouds, as important components of the atmospheric environment, influence climate directly or indirectly. LiDAR, especially hyperspectral lidar, offers significant advantages in aerosol and cloud detection, enabling the acquisition of high-resolution aerosol and cloud profiles. Spaceborne platforms allow for global observation. Based on lidar observation data, a hierarchical recognition and extraction algorithm is used to obtain the hierarchical characteristics of aerosols and clouds, revealing the macroscopic distribution of aerosols and clouds at different altitudes and locations.

[0003] Existing aerosol and cloud layer identification algorithms are mainly designed for polarization-based or multi-wavelength-based lidar. Currently, there are no dedicated layer identification algorithms for hyperspectral lidar, especially spaceborne hyperspectral lidar. Applying existing algorithms to hyperspectral lidar fails to fully utilize the unique advantages of its hyperspectral channels and cannot accurately identify layers in thin atmospheres, high altitudes, or high latitudes. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method, apparatus, device, and medium for atmospheric layer identification and extraction, which can utilize backscattered signals in atmospheric optical remote sensing signals detected by the hyperspectral channel of a hyperspectral lidar to identify aerosols and clouds, thereby achieving atmospheric layer identification and extraction using hyperspectral lidar. The specific solution is as follows:

[0005] In a first aspect, this application discloses an atmospheric layer identification and extraction method, applied to a pre-set hyperspectral lidar, comprising:

[0006] The detection signal of the preset hyperspectral lidar is acquired and the detection signal is preprocessed to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal and target atmospheric background signal based on the obtained preprocessed lidar signal and preset system parameters.

[0007] Atmospheric region location is determined by using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data from the preset hyperspectral lidar to obtain the regional location result.

[0008] Based on the regional positioning results, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient, multi-level atmospheric features are detected to obtain the multi-level atmospheric feature matrix to be verified.

[0009] The multi-level feature matrix of the atmosphere to be verified is verified and its continuity is detected to obtain the target feature matrix, and the target atmospheric aerosol features and atmospheric cloud layer features are determined based on the target feature matrix.

[0010] Optionally, acquiring the detection signal of the preset hyperspectral lidar and preprocessing the detection signal includes:

[0011] The detection signal of the preset hyperspectral lidar is acquired to obtain the total atmospheric detection channel signal and the target hyperspectral detection channel signal;

[0012] The spatiotemporal positions of the atmospheric total detection channel signal and the target hyperspectral detection channel signal corresponding to each profile are determined, and the atmospheric total detection channel signal and the target hyperspectral detection channel signal are geometrically corrected to a preset geodetic coordinate system according to the spatiotemporal positions to obtain the corrected radar signal;

[0013] The horizontal and vertical resolutions of the data in each channel of the corrected radar signal are averaged, and the corrected radar signal is then denoised to obtain a preprocessed radar signal.

[0014] Optionally, before calculating the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal based on the obtained preprocessed radar signal and preset system parameters, the method further includes:

[0015] Based on the preprocessed radar signal, auxiliary data matching is performed in a preset auxiliary dataset to obtain target auxiliary data;

[0016] Accordingly, the calculation of atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal based on the obtained preprocessed radar signal and preset system parameters includes:

[0017] Calculate the product of the single-pulse laser energy and the cosine of the laser beam zenith angle in the preset system parameters to obtain the first product, and determine the target distance between the height to be detected and the preset hyperspectral lidar;

[0018] Determine the ratio between the total atmospheric detection channel signal and the first product to obtain the first ratio, and use the product between the first ratio and the square of the target distance as the atmospheric attenuation backscattering coefficient;

[0019] Calculate the product between the target hyperspectral detection channel signal and the square of the target distance to obtain a second product, and calculate the product between the first product and the first preset transmittance to obtain a third product;

[0020] The ratio of the second product to the third product is taken as the molecular backscattering signal, and the ratio between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal is taken as the atmospheric scattering ratio.

[0021] The molecular two-way transmittance and molecular backscattering coefficient are determined based on the temperature and pressure in the target auxiliary data, and the ozone two-way transmittance is determined by the temperature, the pressure and the ozone mixing ratio in the target auxiliary data.

[0022] The product of the molecular backscattering coefficient, the ozone two-way transmittance, the molecular two-way transmittance, and the second preset transmittance is calculated to obtain the target atmospheric background signal.

[0023] Optionally, the step of performing atmospheric region location positioning using the total atmospheric detection channel signal in the preprocessed radar signal and the observation data from the preset hyperspectral lidar to obtain the region positioning result includes:

[0024] Extract the background noise of the preprocessed radar signal, set the detection signal saturation height threshold according to the preset system parameters, and set the detection signal unsaturation height threshold according to the background noise;

[0025] Based on the saturation height threshold and the unsaturation height threshold of the detection signal, the atmospheric total detection channel signal is located and identified to determine the regional signals corresponding to the surface region, the subsurface region, and the invalid observation region in the atmospheric total detection channel signal.

[0026] The region signal is marked accordingly based on the region corresponding to the region signal, and the corresponding region positioning result is generated.

[0027] Optionally, the step of performing multi-level atmospheric feature detection based on the region positioning result, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient to obtain the multi-level atmospheric feature matrix to be verified includes:

[0028] Based on the regional positioning results, the unmarked signals in the atmospheric total sounding channel signal are determined;

[0029] A stratospheric detection difference function is constructed based on the difference between the atmospheric attenuation backscattering coefficient and the target atmospheric background signal, and a convective boundary layer detection difference function is constructed based on the difference between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal.

[0030] The atmospheric multi-level characteristics in the atmospheric attenuation backscattering coefficient are determined based on the stratospheric detection difference function and the convective boundary layer detection difference function to obtain the first atmospheric multi-level characteristic matrix.

[0031] A stratospheric detection window is constructed based on the stratospheric detection difference function, and a convective boundary layer detection window is constructed based on the convective boundary layer detection difference function. The first atmospheric multi-level feature matrix is ​​verified and detected through the stratospheric detection window and the convective boundary layer detection window to obtain the second atmospheric multi-level feature matrix.

[0032] The first atmospheric multi-level feature matrix is ​​modified based on the second atmospheric multi-level feature matrix to obtain the atmospheric multi-level feature matrix to be verified.

[0033] Optionally, the step of verifying and performing continuity detection on the multi-level feature matrix of the atmosphere to be verified to obtain the target feature matrix, and determining the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix, includes:

[0034] The data quality ratio of each profile corresponding to the target hyperspectral detection channel signal is determined based on the background noise.

[0035] The atmospheric multi-level feature matrix to be verified is verified by the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient. Based on the verification results, the atmospheric multi-level feature matrix to be verified is then corrected to obtain the corrected feature matrix.

[0036] Construct a filling detection window and an elimination detection window, and perform continuity detection on the modified feature matrix based on the filling detection window and the elimination detection window, and perform continuity correction on the modified feature matrix according to the obtained detection results to obtain the target feature matrix;

[0037] The target feature matrix is ​​compared with a preset feature label table to determine the target atmospheric aerosol features and atmospheric cloud layer features based on the comparison results.

[0038] Optionally, the step of verifying the multi-level feature matrix of the atmosphere to be verified using the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient, and then performing a secondary correction on the multi-level feature matrix of the atmosphere to be verified based on the verification results to obtain a corrected feature matrix, includes:

[0039] The atmospheric scattering ratio is compared with a preset atmospheric scattering ratio threshold to generate a first determination matrix;

[0040] The data quality ratio is compared with a preset data quality ratio threshold to generate a second judgment matrix;

[0041] The atmospheric attenuation backscattering coefficient is compared with a preset atmospheric attenuation backscattering coefficient threshold to generate a third judgment matrix;

[0042] The first decision matrix, the second decision matrix, and the third decision matrix are weighted and summed using a preset weighting factor, and the summation result is compared with the preset weighting factor to obtain the corresponding comparison result.

[0043] Based on the comparison results, the multi-level feature matrix of the atmosphere to be verified is corrected a second time to obtain the corrected feature matrix.

[0044] Secondly, this application discloses an atmospheric layer identification and extraction device, applied to a pre-set hyperspectral lidar, comprising:

[0045] The parameter calculation module is used to acquire the detection signal of the preset hyperspectral lidar and preprocess the detection signal to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal and target atmospheric background signal based on the obtained preprocessed lidar signal and preset system parameters.

[0046] The regional positioning module is used to locate the atmospheric region using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar, so as to obtain the regional positioning result.

[0047] The feature detection module is used to perform multi-level atmospheric feature detection based on the region positioning result, the molecular backscattering signal, the target atmospheric background signal and the atmospheric attenuation backscattering coefficient, so as to obtain the multi-level atmospheric feature matrix to be verified.

[0048] The feature recognition module is used to verify and detect the continuity of the multi-level feature matrix of the atmosphere to be verified in order to obtain the target feature matrix, and to determine the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix.

[0049] Thirdly, this application discloses an electronic device, including:

[0050] Memory, used to store computer programs;

[0051] A processor is used to execute the computer program to implement the atmospheric layer identification and extraction method as described above.

[0052] Fourthly, this application discloses a computer-readable storage medium for storing a computer program, wherein the computer program, when executed by a processor, implements the atmospheric layer identification and extraction method as described above.

[0053] In this application, the detection signal of the preset hyperspectral lidar can be acquired and preprocessed to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal based on the preprocessed lidar signal and preset system parameters. Atmospheric region location is then determined using the total atmospheric detection channel signal in the preprocessed lidar signal and the observation data from the preset hyperspectral lidar to obtain a region location result. Based on the region location result, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient, multi-level atmospheric features are detected to obtain a multi-level atmospheric feature matrix to be verified. The multi-level atmospheric feature matrix to be verified is then verified and its continuity is detected to obtain a target feature matrix. Finally, the target atmospheric aerosol features and atmospheric cloud layer features are determined based on the target feature matrix.

[0054] Therefore, the method of this application allows for the preprocessing of detection signals acquired by a pre-set hyperspectral lidar after acquisition. Based on the preprocessed radar signal and pre-set system parameters, corresponding atmospheric optical remote sensing signals are calculated, such as atmospheric attenuated backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal. Further, the atmospheric region location is initially determined using the total atmospheric detection channel signal in the preprocessed radar signal and the radar's detection data. Then, based on the location results, molecular backscattering signal, target atmospheric background signal, and atmospheric attenuated backscattering coefficient, multi-level atmospheric features are detected to obtain a multi-level atmospheric feature matrix to be verified. Finally, the multi-level atmospheric feature matrix to be verified is validated and its continuity is checked to obtain the target feature matrix. Based on the target feature matrix, the aerosol characteristics and cloud layer characteristics of the target atmospheric layer are determined. In this way, the measured molecular backscattering signal detected by the hyperspectral channel of the hyperspectral lidar can be used for aerosol and cloud layer identification, achieving atmospheric layer identification and extraction by hyperspectral lidar. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0056] Figure 1 This is a flowchart of an atmospheric layer identification and extraction method disclosed in this application;

[0057] Figure 2This is a schematic diagram of an atmospheric layer identification and extraction process disclosed in this application;

[0058] Figure 3 This is a schematic diagram of an atmospheric layer identification result disclosed in this application;

[0059] Figure 4 This is a schematic diagram of the structure of an atmospheric layer identification and extraction device disclosed in this application;

[0060] Figure 5 This is a structural diagram of an electronic device disclosed in this application. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Existing aerosol and cloud layer identification algorithms are mainly designed for polarization-based or multi-wavelength-based lidar. Currently, there are no dedicated layer identification algorithms for hyperspectral lidar, especially spaceborne hyperspectral lidar. Applying existing algorithms to hyperspectral lidar fails to fully utilize the unique advantages of its hyperspectral channels and cannot accurately identify layers in thin atmospheres, high altitudes, or high latitudes.

[0063] To overcome the aforementioned technical deficiencies, this application discloses an atmospheric layer identification and extraction method, apparatus, device, and medium, which can utilize the backscattering signals in the atmospheric optical remote sensing signals detected by the hyperspectral channel of a hyperspectral lidar to identify the layers of aerosols and clouds, thereby realizing atmospheric layer identification and extraction by hyperspectral lidar.

[0064] See Figure 1 As shown, this embodiment of the invention discloses an atmospheric layer identification and extraction method, applied to a preset hyperspectral lidar, comprising:

[0065] Step S11: Obtain the detection signal of the preset hyperspectral lidar and preprocess the detection signal to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal and target atmospheric background signal based on the obtained preprocessed lidar signal and preset system parameters.

[0066] In this embodiment, as Figure 2As shown, the first step is to acquire the detection signal from a preset hyperspectral lidar and then preprocess the acquired signal. The preset hyperspectral lidar is a spaceborne hyperspectral lidar. Specifically, the detection signal from the preset hyperspectral lidar needs to be acquired to obtain the total atmospheric detection channel signal. and target hyperspectral detection channel signal It should be noted that if the radar has polarization detection capabilities, then... ,in This is a vertically polarized channel signal. This is the signal from the parallel polarization detection channel.

[0067] Furthermore, the acquired detection signals need to be preprocessed. Specifically, the spatiotemporal positions of each profile corresponding to the overall atmospheric detection channel signal and the target hyperspectral detection channel signal need to be determined, and the overall atmospheric detection channel signal and the target hyperspectral detection channel signal need to be geometrically corrected to a preset geodetic coordinate system based on the spatiotemporal positions to obtain the corrected radar signal. Further, the horizontal and vertical resolutions of the data from each channel in the corrected radar signal need to be averaged, and the corrected radar signal needs to be denoised to obtain the preprocessed radar signal. It should be noted that during averaging, the horizontal and vertical resolutions of the data from each channel need to be averaged according to the signal noise level and different daytime and nighttime scenarios. In this way, the accuracy of the atmospheric layer identification and extraction method can be effectively improved through signal preprocessing.

[0068] After obtaining the preprocessed radar signal, the corresponding atmospheric optical remote sensing signal needs to be calculated based on the preprocessed radar signal and preset system parameters. The calculated atmospheric optical remote sensing signal includes the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal. Specifically, such as... Figure 2 As shown, since the calculation of the target atmospheric background signal requires the use of corresponding auxiliary data, auxiliary data matching is required before calculating these signals. Specifically, auxiliary data matching is performed on the preprocessed radar signal in a preset auxiliary dataset to obtain the target auxiliary data. It should be noted that the auxiliary data includes temperature, pressure, and ozone mixing ratio, and the auxiliary data source can be datasets such as ERA5 (fifth generation ECMWF atmospheric reanalysis of the global climate) and MERRA2 (Modern-Era Retrospective analysis for Research and Applications), or other highly reliable data sources.

[0069] Furthermore, the calculation process for the atmospheric attenuation backscattering coefficient is as follows: First, it is necessary to calculate the product of the single-pulse laser energy and the cosine of the laser beam zenith angle in the preset system parameters to obtain the first product, and determine the target distance between the detection altitude and the preset hyperspectral lidar; determine the ratio between the total atmospheric detection channel signal and the first product to obtain the first ratio, and use the product of the first ratio and the square of the target distance as the atmospheric attenuation backscattering coefficient. That is, the atmospheric attenuation backscattering coefficient at height z, the specific expression of which is as follows:

[0070] ;

[0071] Where E is the single-pulse laser energy. R is the zenith angle of the laser beam, and R is the atmospheric distance from the lidar telescope at altitude z, which is also the target distance.

[0072] The atmospheric scattering ratio is calculated as follows: The product of the target hyperspectral detection channel signal and the square of the target distance is calculated to obtain the second product; the product of the first product and the first preset transmittance is calculated to obtain the third product; the ratio of the second product to the third product is taken as the molecular backscattering signal, and the ratio of the atmospheric attenuation backscattering coefficient to the molecular backscattering signal is taken as the atmospheric scattering ratio. That is, the atmospheric scattering ratio at altitude z, which is expressed as follows:

[0073] ;

[0074] ;

[0075] in, This is the molecular backscattering signal after system correction. This represents the target hyperspectral detection channel signal at height z. It is the transmittance of molecular Rayleigh backscattering through a hyperspectral narrowband filter (for spaceborne hyperspectral lidar, it is the transmittance of Rayleigh backscattering through an iodine molecule absorption cell), which is also the first preset transmittance.

[0076] The calculation process of the target atmospheric background signal is as follows: Based on the temperature and pressure in the target auxiliary data, the molecular two-way transmittance and molecular backscattering coefficient are determined; the ozone two-way transmittance is determined using the temperature, the pressure, and the ozone mixing ratio in the target auxiliary data; the product of the molecular backscattering coefficient, the ozone two-way transmittance, the molecular two-way transmittance, and the second preset transmittance is calculated to obtain the target atmospheric background signal. That is, the ideal pure molecular atmospheric background signal at height z, its expression is as follows:

[0077] ;

[0078] in, The molecular backscattering coefficient under ideal conditions. For molecular two-way transmittance, For ozone two-way transmittance, The second preset transmittance is the transmittance of molecular Rayleigh scattering at height z through the hyperspectral lidar telescope and receiving system.

[0079] Step S12: Atmospheric region location is determined by using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar to obtain the region location result.

[0080] In this embodiment, as Figure 2 As shown, it is necessary to first utilize the signal from the total atmospheric sounding channel. The location of the impenetrable atmospheric layers in the hyperspectral lidar observation data needs to be determined. For spaceborne hyperspectral lidar, the local altitude calculated based on the satellite's latitude and longitude, combined with the saturation threshold of the detector's received signal, is also required to extract accurate ground layers. Furthermore, since spaceborne hyperspectral lidar data is stored in segments according to its orbital period, let's assume the processed data contains m profiles, and the height range for layer identification within each profile is... If both m and n are positive integers greater than 0, then a detection matrix can be set. First, the background noise of the preprocessed radar signal needs to be extracted. Then, a saturation height threshold for the detection signal is set according to preset system parameters, and an unsaturated height threshold is set based on the background noise. Specifically, based on the characteristics of the hyperspectral lidar system parameters, the threshold for the total atmospheric detection channel signal at detection saturation is set as follows: According to the system's noise detection The average value is used to set the signal threshold when it is not saturated. .

[0081] Then, based on the saturation height threshold and the unsaturation height threshold of the detection signal, it is necessary to locate and identify the signals of the total atmospheric sounding channel to determine the corresponding regional signals in the surface area, subsurface area, and invalid observation area. Based on the corresponding regions, the regional signals are then marked accordingly, and the corresponding regional location results are generated. Specifically, if the total atmospheric sounding channel signal is below 25km... All less than If the profile has no effective layers, it will be marked as -2 in the detection matrix. Locate the signal from the total atmospheric sounding channel. Greater than The height is denoted as And i is an integer greater than 1, if there exists a height If the profile is saturated at a certain altitude above the ground, it means that the entire profile is valid from high altitude to the ground. The location of this saturated signal is marked as -1, and the location below this altitude is marked as -1. The minimum value is marked as -3, where This refers to the altitude at the latitude and longitude location of this profile. If... If the profile does not have a valid echo at the ground surface, it means that the signal of the profile is at a height above the ground surface. At a certain altitude, insufficient signal energy prevents it from penetrating a certain layer of aerosol or cloud, rendering all data below that altitude invalid. This is marked as -2 in the detection matrix. It should be noted that the data marked in this step is not included in subsequent calculations. This way, by using the atmospheric total sounding channel signal to extract the surface and non-penetrated areas from hyperspectral lidar observations, and first extracting the layers of these areas, it helps to avoid the influence of these layer signals on subsequent aerosol and cloud layer identification.

[0082] Step S13: Based on the regional positioning results, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient, perform multi-level atmospheric feature detection to obtain the multi-level atmospheric feature matrix to be verified.

[0083] In this embodiment, as Figure 2 As shown, adaptive window sliding dual detection is required. Specifically, firstly, unlabeled signals in the atmospheric total detection channel signal need to be determined based on the regional positioning results. Then, a stratospheric detection difference function is constructed based on the difference between the atmospheric attenuation backscattering coefficient and the target atmospheric background signal. A convective boundary layer detection difference function is also constructed based on the difference between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal. It should be noted that the convective boundary layer is the troposphere-boundary layer. The stratospheric detection difference function and the convective boundary layer detection difference function are shown below:

[0084] ;

[0085] ;

[0086] in, This is the stratospheric detection difference function. For the convective boundary layer detection difference function, The atmospheric attenuation backscattering coefficient is... The molecular backscattering signal after system correction For the target atmospheric background signal, And k and i are both integers, representing the height of the k-th profile. Place.

[0087] During detection, the first step is to determine the multi-layered atmospheric characteristics in the atmospheric attenuation backscattering coefficient based on the stratospheric detection difference function and the convective boundary layer detection difference function, in order to obtain the first atmospheric multi-layered characteristic matrix. Specifically, when aerosols or clouds are present, the atmospheric echo signal will be higher than the molecular signal. However, when processing the measured signal, the influence of noise, signal attenuation, etc., will affect the signal quality. and The distribution of values ​​cannot directly reflect the accurate hierarchical characteristics of aerosols and clouds. The height of the k-th profile... Place or Then mark The value is 1. Because the measured hyperspectral channel signal magnitude is low, especially the measured signal in the stratosphere is weak, noise has a significant impact. There are more misjudgments at high altitudes. Furthermore, for thin cirrus clouds within the troposphere, or in cases involving multiple layers of aerosols and clouds, the simulation error of the ideal pure molecular atmospheric background signal increases, leading to... There were many missed cases.

[0088] Therefore, to avoid the aforementioned errors, it is necessary to construct a stratospheric detection window based on the stratospheric detection difference function and a convective boundary layer detection window based on the convective boundary layer detection difference function. These two windows are then used to verify and detect the multi-level feature matrix of the first atmosphere, thereby obtaining the multi-level feature matrix of the second atmosphere. Specifically, two detection windows of different sizes need to be created based on the horizontal and vertical resolution of the data. and , The values ​​are positive odd numbers, and secondary detection is performed on the stratosphere and troposphere-boundary layer respectively.

[0089] For the stratosphere, if Then mark the multi-level feature matrix of the second atmosphere. For the troposphere-boundary layer, if the value is 0, then... Mark the multi-level feature matrix of the second atmosphere The value is 1. The multi-level feature matrix of the first atmosphere is corrected based on the multi-level feature matrix of the second atmosphere to obtain the multi-level feature matrix of the atmosphere to be verified. That is, under the same conditions, the labeling result of the multi-level feature matrix of the second atmosphere is taken as the standard.

[0090] Step S14: Verify and perform continuity detection on the multi-level feature matrix of the atmosphere to be verified to obtain the target feature matrix, and determine the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix.

[0091] In this embodiment, the high-altitude and high-latitude regions being detected are more susceptible to noise. Therefore, atmospheric scattering ratio can be used. Data quality ratio and atmospheric attenuation backscattering coefficient Multi-parameter thresholding is used for auxiliary determination. Specifically, firstly, the data quality ratio of each profile corresponding to the target hyperspectral detection channel signal needs to be determined based on the background noise, and the expression for the data quality ratio is as follows:

[0092] ;

[0093] in, The data quality ratio at the z-height of the profile. The original signal power at height z This represents the background noise of the profile.

[0094] Furthermore, the multi-level feature matrix of the atmosphere to be verified needs to be validated using atmospheric scattering ratio, data quality ratio, and atmospheric attenuation backscattering coefficient. Based on the validation results, the multi-level feature matrix needs to be corrected to obtain a corrected feature matrix. Additionally, a multi-threshold judgment matrix needs to be set. The specific process is as follows:

[0095] S141. Compare the atmospheric scattering ratio with a preset atmospheric scattering ratio threshold to generate a first judgment matrix. The atmospheric scattering ratio threshold is... The threshold value is between 1 and 2, with a higher value used for high altitudes and a lower value used for low altitudes, depending on the actual detection situation. If Then the first judgment matrix Marked as 1.

[0096] S142. Compare the data quality ratio with a preset data quality ratio threshold to generate a second judgment matrix. The data quality ratio threshold is... This threshold is determined based on the quality distribution of the system's measured data. Taking ACDL (Aerosol and Carbon dioxide Detection Lidar) as an example, the threshold is selected as 4. If... Then the second decision matrix Marked as 1.

[0097] S143. Compare the atmospheric attenuation backscattering coefficient with a preset atmospheric attenuation backscattering coefficient threshold to generate a third judgment matrix. Calculate the denoised average value of each atmospheric attenuation backscattering coefficient profile at an altitude of 30km-35km, and use this as the threshold for profile k. .like Then the third judgment matrix Marked as 1.

[0098] S144. The first, second, and third decision matrices are weighted and summed using a preset weighting factor. The summation result is then compared with the preset weighting factor to obtain the corresponding comparison result. Specifically, this requires considering the characteristics of aerosol and cloud layer distribution in the stratosphere, troposphere-boundary layer, and high latitudes, and utilizing the weighting factor. For multi-threshold decision matrices Perform weighted summations separately. The values ​​of the weighting factors can be adjusted according to the effect of the hierarchical determination, and are usually selected based on the degree of influence on different regions. For example, in the stratosphere, a weighting factor is typically selected. Based on the weighting of the multi-threshold decision matrix, the detection matrix is... A second determination is performed.

[0099] like Then mark It is 0.

[0100] S145. Based on the comparison results, a secondary correction is performed on the multi-level feature matrix of the atmosphere to be verified to obtain the corrected feature matrix. Finally, based on the comparison results, the multi-level feature matrix of the atmosphere to be verified needs to be corrected to obtain the corrected feature matrix.

[0101] Furthermore, since the distribution of aerosols and cloud layers in the atmosphere is spatiotemporally continuous, for re-detection based on local continuity, it is necessary to construct a filling detection window and an elimination detection window. Continuity detection is then performed on the corrected feature matrix based on the filling and elimination detection windows, and the corrected feature matrix is ​​further corrected based on the detection results to obtain the target feature moments. Specifically, this requires... Based on the recognition status, create a fill detection window. and eliminate detection window , They are all positive odd numbers.

[0102] Then you need to use the fill detection window. right Slide detection needs to be performed, and it needs to be divided into several specific cases, if... Then mark If it is 1; Then the window Adaptive expansion is performed to detect the existence of effective layers within a larger outer region, thereby improving the local continuity of hierarchical features. Then mark The value is 1. Furthermore, it is necessary to use the detection window elimination function. right Perform the corresponding sliding detection: If , The values ​​differ between the stratosphere and the troposphere-boundary layer, so the markings are... The value is 0. Finally, the detection window can be iteratively filled based on the degree of continuity in hierarchical recognition. and eliminate detection window Repeat the above steps for the given size, and use the final matrix as the target feature matrix.

[0103] Finally, the target feature matrix needs to be compared with a preset feature label table to determine the target atmospheric aerosol features and atmospheric cloud layer features based on the comparison results, and the atmospheric layer identification results are as follows: Figure 3 As shown in Table 1, each type of label in the matrix corresponds to a feature. The specific feature of each label can be determined by a preset feature label table, which is shown in Table 1 below:

[0104] Table 1 Preset Feature Marker Table

[0105] .

[0106] In this embodiment, after the pre-set hyperspectral lidar acquires the detection signal, the acquired detection signal is preprocessed. Based on the preprocessed radar signal and pre-set system parameters, corresponding atmospheric optical remote sensing signals are calculated, such as atmospheric attenuated backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal. Further, the atmospheric region location is initially determined using the total atmospheric detection channel signal in the preprocessed radar signal and the radar's detection data. Then, based on the location results, molecular backscattering signal, target atmospheric background signal, and atmospheric attenuated backscattering coefficient, multi-level atmospheric features are detected to obtain a multi-level atmospheric feature matrix to be verified. Finally, the multi-level atmospheric feature matrix to be verified is verified and its continuity is detected to obtain the target feature matrix. Based on the target feature matrix, the aerosol characteristics and cloud layer characteristics of the target atmosphere are determined. In this way, taking into account the characteristics and technical principles of spaceborne hyperspectral lidar detection data, and fully utilizing its unique hyperspectral channels, a method for aerosol and cloud layer identification over a wide latitude and altitude range can be developed, and this method is stably applicable under various atmospheric conditions.

[0107] See Figure 4 As shown, this embodiment of the invention discloses an atmospheric layer identification and extraction device, applied to a preset hyperspectral lidar, comprising:

[0108] The parameter calculation module 11 is used to acquire the detection signal of the preset hyperspectral lidar and preprocess the detection signal to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal and target atmospheric background signal based on the obtained preprocessed lidar signal and preset system parameters.

[0109] The regional positioning module 12 is used to perform atmospheric regional location positioning through the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar, so as to obtain the regional positioning result.

[0110] The feature detection module 13 is used to perform multi-level atmospheric feature detection based on the region positioning result, the molecular backscattering signal, the target atmospheric background signal and the atmospheric attenuation backscattering coefficient, so as to obtain the multi-level atmospheric feature matrix to be verified.

[0111] The feature recognition module 14 is used to verify and detect the continuity of the multi-level feature matrix of the atmosphere to be verified, so as to obtain the target feature matrix, and determine the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix.

[0112] In this embodiment, after the hyperspectral lidar acquires the detection signal, the acquired signal is preprocessed. Based on the preprocessed radar signal and preset system parameters, corresponding atmospheric optical remote sensing signals are calculated, such as atmospheric attenuated backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal. Further, the atmospheric region location is initially determined using the total atmospheric detection channel signal in the preprocessed radar signal and the radar's detection data. Then, based on the location result, molecular backscattering signal, target atmospheric background signal, and atmospheric attenuated backscattering coefficient, multi-level atmospheric features are detected to obtain a multi-level atmospheric feature matrix to be verified. Finally, the multi-level atmospheric feature matrix to be verified is verified and its continuity is detected to obtain the target feature matrix. Based on the target feature matrix, the aerosol characteristics and cloud layer characteristics of the target atmospheric layer are determined. In this way, the measured molecular backscattering signal detected by the hyperspectral channel of the hyperspectral lidar can be used for aerosol and cloud layer identification, achieving atmospheric layer identification and extraction by hyperspectral lidar.

[0113] In some embodiments, the parameter calculation module 11 may specifically include:

[0114] The signal acquisition unit is used to acquire the detection signal of the preset hyperspectral lidar to obtain the atmospheric total detection channel signal and the target hyperspectral detection channel signal;

[0115] The signal correction unit is used to determine the spatiotemporal position of each profile corresponding to the total atmospheric detection channel signal and the target hyperspectral detection channel signal, and to geometrically correct the total atmospheric detection channel signal and the target hyperspectral detection channel signal to a preset geodetic coordinate system according to the spatiotemporal position to obtain the corrected radar signal;

[0116] The signal averaging unit is used to average the horizontal and vertical resolutions of the data from each channel in the corrected radar signal and to perform noise reduction processing on the corrected radar signal to obtain the preprocessed radar signal.

[0117] In some embodiments, the atmospheric layer identification and extraction device may further include:

[0118] The data matching unit is used to perform auxiliary data matching in a preset auxiliary dataset based on the preprocessed radar signal to obtain target auxiliary data.

[0119] In some embodiments, the parameter calculation module 11 may specifically include:

[0120] Calculate the product of the single-pulse laser energy and the cosine of the laser beam zenith angle in the preset system parameters to obtain the first product, and determine the target distance between the height to be detected and the preset hyperspectral lidar;

[0121] The first signal determination unit is used to determine the ratio between the total atmospheric detection channel signal and the first product to obtain the first ratio, and the product between the first ratio and the square of the target distance is used as the atmospheric attenuation backscattering coefficient.

[0122] The first signal calculation unit is used to calculate the product between the target hyperspectral detection channel signal and the square of the target distance to obtain a second product, and to calculate the product between the first product and the first preset transmittance to obtain a third product;

[0123] The second signal determination unit is used to take the ratio of the second product to the third product as the molecular backscattering signal, and the ratio between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal as the atmospheric scattering ratio.

[0124] The second signal calculation unit is used to determine the molecular two-way transmittance and molecular backscattering coefficient based on the temperature and pressure in the target auxiliary data, and to determine the ozone two-way transmittance through the temperature, the pressure and the ozone mixing ratio in the target auxiliary data.

[0125] The third signal determination unit is used to calculate the product of the molecular backscattering coefficient, the ozone two-way transmittance, the molecular two-way transmittance, and the second preset transmittance to obtain the target atmospheric background signal.

[0126] In some embodiments, the area positioning module 12 may specifically include:

[0127] A threshold setting unit is used to extract the background noise of the preprocessed radar signal, set the detection signal saturation height threshold according to the preset system parameters, and set the detection signal unsaturation height threshold according to the background noise.

[0128] The signal location and identification unit is used to locate and identify the signal of the total atmospheric sounding channel based on the saturation height threshold and the unsaturation height threshold of the detection signal, so as to determine the regional signals corresponding to the surface area, the subsurface area and the invalid observation area in the total atmospheric sounding channel signal.

[0129] The area positioning unit is used to mark the area signal based on the area corresponding to the area signal and generate the corresponding area positioning result.

[0130] In some embodiments, the feature detection module 13 may specifically include:

[0131] A signal determination unit is used to determine unmarked signals in the atmospheric total sounding channel signals based on the regional positioning results.

[0132] The function construction unit is used to construct a stratospheric detection difference function based on the difference between the atmospheric attenuation backscattering coefficient and the target atmospheric background signal, and to construct a convective boundary layer detection difference function based on the difference between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal.

[0133] The first feature determination unit is used to determine the multi-level atmospheric features in the atmospheric attenuation backscattering coefficient based on the stratospheric detection difference function and the convective boundary layer detection difference function, so as to obtain the first atmospheric multi-level feature matrix.

[0134] The second feature determination unit is used to construct a stratospheric detection window based on the stratospheric detection difference function and a convective boundary layer detection window based on the convective boundary layer detection difference function, so as to verify and detect the first atmospheric multi-level feature matrix through the stratospheric detection window and the convective boundary layer detection window, so as to obtain the second atmospheric multi-level feature matrix.

[0135] The first feature correction unit is used to correct the first atmospheric multi-level feature matrix based on the second atmospheric multi-level feature matrix to obtain the atmospheric multi-level feature matrix to be verified.

[0136] In some embodiments, the feature recognition module 14 may specifically include:

[0137] The quality ratio determination submodule is used to determine the data quality ratio of each profile of the target hyperspectral detection channel signal based on the background noise;

[0138] The first feature correction submodule is used to verify the multi-level feature matrix of the atmosphere to be verified by the atmospheric scattering ratio, the data quality ratio and the atmospheric attenuation backscattering coefficient, and to perform a second correction on the multi-level feature matrix of the atmosphere to be verified based on the verification results to obtain the corrected feature matrix.

[0139] The second feature correction submodule is used to construct a filling detection window and an elimination detection window, and to perform continuous detection on the corrected feature matrix based on the filling detection window and the elimination detection window, and to continuously correct the corrected feature matrix according to the obtained detection results to obtain the target feature matrix.

[0140] The feature determination submodule is used to compare the target feature matrix with a preset feature label table to determine the target atmospheric aerosol features and atmospheric cloud layer features based on the comparison results.

[0141] In some embodiments, the first feature correction submodule may further include:

[0142] The first determination matrix generation unit is used to compare the atmospheric scattering ratio with a preset atmospheric scattering ratio threshold to generate a first determination matrix.

[0143] The second judgment matrix generation unit is used to compare the data quality ratio with a preset data quality ratio threshold to generate a second judgment matrix.

[0144] The third determination matrix generation unit is used to compare the atmospheric attenuation backscattering coefficient with a preset atmospheric attenuation backscattering coefficient threshold to generate a third determination matrix.

[0145] The data comparison unit is used to perform a weighted summation of the first judgment matrix, the second judgment matrix, and the third judgment matrix using a preset weighting factor, and compare the summation result with the preset weighting factor to obtain a corresponding comparison result.

[0146] The second feature correction unit is used to perform a secondary correction on the multi-level feature matrix of the atmosphere to be verified based on the comparison results, so as to obtain the corrected feature matrix.

[0147] Furthermore, embodiments of this application also disclose an electronic device, Figure 5 This is a structural diagram of an electronic device 20 according to an exemplary embodiment. The content of the diagram should not be construed as limiting the scope of this application.

[0148] Figure 5 This is a schematic diagram of the structure of an electronic device 20 provided in an embodiment of this application. Specifically, the electronic device 20 may include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. The memory 22 stores a computer program, which is loaded and executed by the processor 21 to implement the relevant steps in the atmospheric layer identification and extraction method disclosed in any of the foregoing embodiments. Alternatively, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0149] In this embodiment, the power supply 23 is used to provide operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows can be any communication protocol applicable to the technical solution of this application, and is not specifically limited here; the input / output interface 25 is used to acquire external input data or output data to the outside world, and its specific interface type can be selected according to specific application needs, and is not specifically limited here.

[0150] In addition, the memory 22, as a carrier for resource storage, can be a read-only memory, random access memory, disk or optical disk, etc. The resources stored thereon can include operating system 221, computer program 222, etc., and the storage method can be temporary storage or permanent storage.

[0151] The operating system 221 is used to manage and control the various hardware devices on the electronic device 20 and the computer program 222, which may be Windows Server, Netware, Unix, Linux, etc. In addition to including a computer program capable of performing the atmospheric layer identification and extraction method executed by the electronic device 20 as disclosed in any of the foregoing embodiments, the computer program 222 may further include computer programs capable of performing other specific tasks.

[0152] Furthermore, this application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the aforementioned atmospheric layer identification and extraction method. Specific steps of this method can be found in the corresponding content disclosed in the foregoing embodiments, and will not be repeated here.

[0153] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section.

[0154] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0155] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0156] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0157] The technical solutions provided in this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for identifying and extracting atmospheric layers, characterized in that, Applied to pre-defined hyperspectral lidar, including: The detection signal of the preset hyperspectral lidar is acquired and the detection signal is preprocessed to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal and target atmospheric background signal based on the obtained preprocessed lidar signal and preset system parameters. Atmospheric region location is determined by using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data from the preset hyperspectral lidar to obtain the regional location result. Based on the regional positioning results, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient, multi-level atmospheric features are detected to obtain the multi-level atmospheric feature matrix to be verified. The multi-level feature matrix of the atmosphere to be verified is verified and its continuity is detected to obtain the target feature matrix, and the target atmospheric aerosol features and atmospheric cloud layer features are determined based on the target feature matrix.

2. The atmospheric layer identification and extraction method according to claim 1, characterized in that, The step of acquiring the detection signal from the preset hyperspectral lidar and preprocessing the detection signal includes: The detection signal of the preset hyperspectral lidar is acquired to obtain the total atmospheric detection channel signal and the target hyperspectral detection channel signal; The spatiotemporal positions of the atmospheric total detection channel signal and the target hyperspectral detection channel signal corresponding to each profile are determined, and the atmospheric total detection channel signal and the target hyperspectral detection channel signal are geometrically corrected to a preset geodetic coordinate system according to the spatiotemporal positions to obtain the corrected radar signal; The horizontal and vertical resolutions of the data in each channel of the corrected radar signal are averaged, and the corrected radar signal is then denoised to obtain a preprocessed radar signal.

3. The atmospheric layer identification and extraction method according to claim 2, characterized in that, Before calculating the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal based on the obtained preprocessed radar signal and preset system parameters, the method further includes: Based on the preprocessed radar signal, auxiliary data matching is performed in a preset auxiliary dataset to obtain target auxiliary data; Accordingly, the calculation of atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal, and target atmospheric background signal based on the obtained preprocessed radar signal and preset system parameters includes: Calculate the product of the single-pulse laser energy and the cosine of the laser beam zenith angle in the preset system parameters to obtain the first product, and determine the target distance between the height to be detected and the preset hyperspectral lidar; Determine the ratio between the total atmospheric detection channel signal and the first product to obtain the first ratio, and use the product between the first ratio and the square of the target distance as the atmospheric attenuation backscattering coefficient; Calculate the product between the target hyperspectral detection channel signal and the square of the target distance to obtain a second product, and calculate the product between the first product and the first preset transmittance to obtain a third product; The ratio of the second product to the third product is taken as the molecular backscattering signal, and the ratio between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal is taken as the atmospheric scattering ratio. The molecular two-way transmittance and molecular backscattering coefficient are determined based on the temperature and pressure in the target auxiliary data, and the ozone two-way transmittance is determined by the temperature, the pressure and the ozone mixing ratio in the target auxiliary data. The product of the molecular backscattering coefficient, the ozone two-way transmittance, the molecular two-way transmittance, and the second preset transmittance is calculated to obtain the target atmospheric background signal.

4. The atmospheric layer identification and extraction method according to claim 2, characterized in that, The atmospheric region location is determined by using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data from the preset hyperspectral lidar to obtain the region location result, including: Extract the background noise of the preprocessed radar signal, set the detection signal saturation height threshold according to the preset system parameters, and set the detection signal unsaturation height threshold according to the background noise; Based on the saturation height threshold and the unsaturation height threshold of the detection signal, the atmospheric total detection channel signal is located and identified to determine the regional signals corresponding to the surface region, the subsurface region, and the invalid observation region in the atmospheric total detection channel signal. The region signal is marked accordingly based on the region corresponding to the region signal, and the corresponding region positioning result is generated.

5. The atmospheric layer identification and extraction method according to any one of claims 1 to 4, characterized in that, The process of detecting multi-level atmospheric features based on the region localization result, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient to obtain a multi-level atmospheric feature matrix to be verified includes: Based on the regional positioning results, the unmarked signals in the atmospheric total sounding channel signal are determined; A stratospheric detection difference function is constructed based on the difference between the atmospheric attenuation backscattering coefficient and the target atmospheric background signal, and a convective boundary layer detection difference function is constructed based on the difference between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal. The atmospheric multi-level characteristics in the atmospheric attenuation backscattering coefficient are determined based on the stratospheric detection difference function and the convective boundary layer detection difference function to obtain the first atmospheric multi-level characteristic matrix. A stratospheric detection window is constructed based on the stratospheric detection difference function, and a convective boundary layer detection window is constructed based on the convective boundary layer detection difference function. The first atmospheric multi-level feature matrix is ​​verified and detected through the stratospheric detection window and the convective boundary layer detection window to obtain the second atmospheric multi-level feature matrix. The first atmospheric multi-level feature matrix is ​​modified based on the second atmospheric multi-level feature matrix to obtain the atmospheric multi-level feature matrix to be verified.

6. The atmospheric layer identification and extraction method according to claim 4, characterized in that, The process of verifying and detecting the continuity of the multi-level feature matrix of the atmosphere to be verified to obtain the target feature matrix, and determining the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix, includes: The data quality ratio of each profile corresponding to the target hyperspectral detection channel signal is determined based on the background noise. The atmospheric multi-level feature matrix to be verified is verified by the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient. Based on the verification results, the atmospheric multi-level feature matrix to be verified is then corrected to obtain the corrected feature matrix. Construct a filling detection window and an elimination detection window, and perform continuity detection on the modified feature matrix based on the filling detection window and the elimination detection window, and perform continuity correction on the modified feature matrix according to the obtained detection results to obtain the target feature matrix; The target feature matrix is ​​compared with a preset feature label table to determine the target atmospheric aerosol features and atmospheric cloud layer features based on the comparison results.

7. The atmospheric layer identification and extraction method according to claim 6, characterized in that, The process involves verifying the multi-level feature matrix of the atmosphere to be verified using the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient. Based on the verification results, the multi-level feature matrix is ​​then corrected to obtain a corrected feature matrix, including: The atmospheric scattering ratio is compared with a preset atmospheric scattering ratio threshold to generate a first determination matrix; The data quality ratio is compared with a preset data quality ratio threshold to generate a second judgment matrix; The atmospheric attenuation backscattering coefficient is compared with a preset atmospheric attenuation backscattering coefficient threshold to generate a third judgment matrix; The first decision matrix, the second decision matrix, and the third decision matrix are weighted and summed using a preset weighting factor, and the summation result is compared with the preset weighting factor to obtain the corresponding comparison result. Based on the comparison results, the multi-level feature matrix of the atmosphere to be verified is corrected a second time to obtain the corrected feature matrix.

8. An atmospheric layer identification and extraction device, characterized in that, Applied to pre-defined hyperspectral lidar, including: The parameter calculation module is used to acquire the detection signal of the preset hyperspectral lidar and preprocess the detection signal to calculate the atmospheric attenuation backscattering coefficient, atmospheric scattering ratio, molecular backscattering signal and target atmospheric background signal based on the obtained preprocessed lidar signal and preset system parameters. The regional positioning module is used to locate the atmospheric region using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar, so as to obtain the regional positioning result. The feature detection module is used to perform multi-level atmospheric feature detection based on the region positioning result, the molecular backscattering signal, the target atmospheric background signal and the atmospheric attenuation backscattering coefficient, so as to obtain the multi-level atmospheric feature matrix to be verified. The feature recognition module is used to verify and detect the continuity of the multi-level feature matrix of the atmosphere to be verified in order to obtain the target feature matrix, and to determine the target atmospheric aerosol features and atmospheric cloud layer features based on the target feature matrix.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor for executing the computer program to implement the atmospheric layer identification and extraction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program, wherein the computer program, when executed by a processor, implements the atmospheric layer identification and extraction method as described in any one of claims 1 to 7.

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