Atmosphere identification and extraction method, device, equipment and medium

Abstract

The invention discloses an atmosphere layer identification and extraction method, device and equipment and a medium, is applied to a preset hyperspectral laser radar, and relates to the technical field of atmosphere remote sensing. Calculating a corresponding atmospheric optical remote sensing signal according to the obtained preprocessed radar signal and preset system parameters; performing atmospheric area location through the preprocessed radar signal and the observation data of the radar to obtain an area location result; carrying out atmospheric layer multi-level feature detection based on the region positioning result and the atmospheric optical remote sensing signal, and carrying out verification and continuous detection on a to-be-verified atmospheric layer multi-level feature matrix obtained through detection to obtain a target feature matrix; and according to the target feature matrix, determining a target atmosphere aerosol feature and an atmosphere cloud layer feature. Therefore, the atmospheric optical remote sensing signal detected by the hyperspectral channel of the hyperspectral laser radar can be used for carrying out aerosol and cloud level identification.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of atmospheric remote sensing, and in particular to an atmospheric hierarchical recognition extraction method, device, equipment and medium BACKGROUND

[0002] As an important component of the atmospheric environment, aerosols and clouds affect the climate through direct or indirect ways. Using lidar, especially high-spectral lidar, has great advantages in aerosol and cloud detection, which can obtain high-resolution aerosol and cloud observation profiles, and using a spaceborne platform can achieve global observation. Based on the observation data of lidar, the hierarchical recognition extraction algorithm is used to obtain the hierarchical characteristics of aerosols and clouds, and the macro distribution of aerosols and clouds at different altitudes and different positions is obtained.

[0003] The existing aerosol and cloud hierarchical recognition algorithm is mainly for polarization detection or multi-wavelength detection lidar, and there is currently no special hierarchical recognition algorithm for high-spectral lidar, especially spaceborne high-spectral lidar. When the existing old algorithm is applied to high-spectral lidar, it cannot fully utilize the technical advantages of the unique high-spectral channel of high-spectral lidar, and cannot accurately recognize the hierarchical conditions of thin layers, high altitudes, high latitudes and other atmospheric conditions. SUMMARY

[0004] Therefore, the purpose of the present application is to provide an atmospheric hierarchical recognition extraction method, device, equipment and medium, which can use the backscattering signal in the atmospheric optical remote sensing signal detected by the high-spectral channel of the high-spectral lidar to recognize the hierarchical characteristics of aerosols and clouds, so as to realize the atmospheric hierarchical recognition extraction of the high-spectral lidar. The specific scheme is as follows: In a first aspect, the present application discloses an atmospheric hierarchical recognition extraction method applied to a preset high-spectral lidar, comprising: obtaining the detection signal of the preset high-spectral lidar, and preprocessing the detection signal to calculate the atmospheric attenuation backscattering coefficient, the atmospheric scattering ratio, the molecular backscattering signal and the target atmospheric background signal according to the obtained preprocessed radar signal and the preset system parameters; locating the atmospheric region position by the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset high-spectral lidar to obtain a region positioning result; detecting the hierarchical characteristics of the atmospheric layer based on the region positioning result, the molecular backscattering signal, the target atmospheric background signal and the atmospheric attenuation backscattering coefficient to obtain an atmospheric layer hierarchical feature matrix to be verified; Verify and continuity detection are performed on the to-be-verified atmospheric multi-level feature matrix to obtain a target feature matrix, and a target atmospheric aerosol feature and an atmospheric cloud level feature are determined according to the target feature matrix.

[0005] Optionally, the detection signal of the preset hyperspectral lidar is acquired, and the detection signal is preprocessed, including: The detection signal of the preset hyperspectral lidar is acquired to obtain an atmospheric total detection channel signal and a target hyperspectral detection channel signal; The spatial and temporal positions of each profile corresponding to the atmospheric total detection channel signal and the target hyperspectral detection channel signal 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 spatial and temporal positions to obtain a corrected radar signal; The horizontal resolution and the vertical resolution of each channel data in the corrected radar signal are averaged, and the corrected radar signal is denoised to obtain a preprocessed radar signal.

[0006] Optionally, before the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal and the target atmospheric background signal are calculated according to the obtained preprocessed radar signal and the preset system parameters, further including: The preprocessed radar signal is matched with preset auxiliary data in a preset auxiliary data set to obtain target auxiliary data; Correspondingly, the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal and the target atmospheric background signal are calculated according to the obtained preprocessed radar signal and the preset system parameters, including: The product of the single-pulse laser energy and the cosine value of the laser beam zenith angle in the preset system parameters is calculated to obtain a first product, and a target distance between the to-be-detected height and the preset hyperspectral lidar is determined; The ratio between the atmospheric total detection channel signal and the first product is determined to obtain a first ratio, and the product between the first ratio and the square of the target distance is taken as the atmospheric attenuation backscatter coefficient; The product between the target hyperspectral detection channel signal and the square of the target distance is calculated to obtain a second product, and the product between the first product and a first preset transmittance is calculated to obtain a third product; The ratio between the second product and the third product is taken as the molecular backscatter signal, and the ratio between the atmospheric attenuation backscatter coefficient and the molecular backscatter signal is taken as the atmospheric scattering ratio; determine a molecular two-way transmittance and a molecular backscattering coefficient based on a temperature and a pressure in the target auxiliary data, and determine an ozone two-way transmittance based on the temperature, the pressure, and an ozone mixing ratio in the target auxiliary data; calculate a product between the molecular backscattering coefficient, the ozone two-way transmittance, the molecular two-way transmittance, and a second preset transmittance to obtain a target atmospheric background signal.

[0007] Optionally, the atmospheric region location positioning based on the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar to obtain a region positioning result comprises: extract a background noise of the preprocessed radar signal, set a detection signal saturated height threshold based on the preset system parameters, and set a detection signal unsaturated height threshold based on the background noise; perform positioning identification on the atmospheric total detection channel signal based on the detection signal saturated height threshold and the detection signal unsaturated height threshold to determine region signals corresponding to a ground surface region, a region below the ground surface, and an invalid observation region in the atmospheric total detection channel signal; correspondingly label the region signals based on regions corresponding to the region signals, and generate a corresponding region positioning result.

[0008] Optionally, the atmospheric layer multi-level 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 a to-be-verified atmospheric layer multi-level feature matrix comprises: determine unmarked signals in the atmospheric total detection channel signal based on the region positioning result; construct a stratosphere detection difference function based on a difference between the atmospheric attenuation backscattering coefficient and the target atmospheric background signal, and construct a convective boundary layer detection difference function based on a difference between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal; determine atmospheric layer multi-level features in the atmospheric attenuation backscattering coefficient based on the stratosphere detection difference function and the convective boundary layer detection difference function to obtain a first atmospheric layer multi-level feature matrix; construct a stratosphere detection window based on the stratosphere detection difference function, and construct a convective boundary layer detection window based on the convective boundary layer detection difference function, to perform verification detection on the first atmospheric layer multi-level feature matrix through the stratosphere detection window and the convective boundary layer detection window to obtain a second atmospheric layer multi-level feature matrix; The first atmospheric multi-level feature matrix is corrected based on the second atmospheric multi-level feature matrix to obtain a to-be-verified atmospheric multi-level feature matrix.

[0009] Optionally, the to-be-verified atmospheric multi-level feature matrix is verified and continuity detected to obtain a target feature matrix, and target atmospheric aerosol features and atmospheric cloud level features are determined according to the target feature matrix, including: The data quality ratio of the target hyperspectral detection channel signal corresponding to each profile is determined based on the background noise; The to-be-verified atmospheric multi-level feature matrix is verified by the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient, and the to-be-verified atmospheric multi-level feature matrix is secondarily corrected according to a verification result to obtain a corrected feature matrix; A filling detection window and an elimination detection window are constructed, the corrected feature matrix is continuity detected based on the filling detection window and the elimination detection window, and the corrected feature matrix is continuity corrected according to a detection result obtained to obtain a target feature matrix; The target feature matrix is compared with a preset feature marker table to determine target atmospheric aerosol features and atmospheric cloud level features according to a comparison result.

[0010] Optionally, the to-be-verified atmospheric multi-level feature matrix is verified by the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient, and the to-be-verified atmospheric multi-level feature matrix is secondarily corrected according to a verification result to obtain a corrected feature matrix, including: The atmospheric scattering ratio is compared with a preset atmospheric scattering ratio threshold to generate a first judgment 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 judgment matrix, the second judgment matrix, and the third judgment matrix are weighted and summed by a preset weighting factor, and a sum result obtained is compared with the preset weighting factor to obtain a corresponding comparison result; The to-be-verified atmospheric multi-level feature matrix is secondarily corrected based on the comparison result to obtain a corrected feature matrix.

[0011] In a second aspect, the present application discloses an atmospheric level recognition and extraction device, applied to a preset hyperspectral laser radar, including: a parameter calculation module, configured to acquire a detection signal of the preset hyperspectral lidar, and perform preprocessing on the detection signal, so as to calculate an atmospheric attenuation backscatter coefficient, an atmospheric scattering ratio, a molecular backscatter signal and a target atmospheric background signal according to the obtained preprocessed radar signal and preset system parameters; a region positioning module, configured to perform atmospheric region position positioning through an atmospheric total detection channel signal in the preprocessed radar signal and observation data of the preset hyperspectral lidar, so as to obtain a region positioning result; a feature detection module, configured to perform atmospheric layer multi-level feature detection based on the region positioning result, the molecular backscatter signal, the target atmospheric background signal and the atmospheric attenuation backscatter coefficient, so as to obtain a to-be-verified atmospheric layer multi-level feature matrix; a feature recognition module, configured to perform verification and continuity detection on the to-be-verified atmospheric layer multi-level feature matrix, so as to obtain a target feature matrix, and determine a target atmospheric layer aerosol feature and an atmospheric layer cloud level feature according to the target feature matrix.

[0012] In a third aspect, the present application discloses an electronic device, comprising: a memory, configured to save a computer program; a processor, configured to execute the computer program, so as to realize the atmospheric layer level recognition and extraction method as described above.

[0013] In a fourth aspect, the present application discloses a computer readable storage medium, configured to save a computer program, wherein the computer program is executed by a processor to realize the atmospheric layer level recognition and extraction method as described above.

[0014] In the present application, the detection signal of the preset hyperspectral lidar can be acquired, and the detection signal can be preprocessed, so as to calculate an atmospheric attenuation backscatter coefficient, an atmospheric scattering ratio, a molecular backscatter signal and a target atmospheric background signal according to the obtained preprocessed radar signal and preset system parameters; atmospheric region position positioning can be performed through an atmospheric total detection channel signal in the preprocessed radar signal and observation data of the preset hyperspectral lidar, so as to obtain a region positioning result; atmospheric layer multi-level feature detection can be performed based on the region positioning result, the molecular backscatter signal, the target atmospheric background signal and the atmospheric attenuation backscatter coefficient, so as to obtain a to-be-verified atmospheric layer multi-level feature matrix; verification and continuity detection can be performed on the to-be-verified atmospheric layer multi-level feature matrix, so as to obtain a target feature matrix, and a target atmospheric layer aerosol feature and an atmospheric layer cloud level feature can be determined according to the target feature matrix.

[0015] Therefore, by the method, the collected detection signal can be preprocessed after the preset hyperspectral lidar collects the detection signal, and the corresponding atmospheric optical remote sensing signal, such as the atmospheric attenuation backscattering coefficient, the atmospheric scattering ratio, the molecular backscattering signal, and the target atmospheric background signal, is calculated according to the preprocessed radar signal and the preset system parameter. Further, the atmospheric region position is preliminarily positioned by the atmospheric total detection channel signal in the preprocessed radar signal and the detection data of the radar, and then the atmospheric layer multi-level feature detection is performed according to the positioning result, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient to obtain a to-be-verified atmospheric layer multi-level feature matrix. Finally, the to-be-verified atmospheric layer multi-level feature matrix needs to be verified and continuously detected to obtain a target feature matrix, and the target atmospheric layer aerosol feature and the atmospheric layer cloud level feature are determined according to the target feature matrix. In this way, the measured molecular backscattering signal detected by the hyperspectral channel of the hyperspectral lidar can be used for aerosol and cloud level recognition to realize the atmospheric level recognition and extraction of the hyperspectral lidar. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0017] Figure 1 A flow chart of an atmospheric level recognition and extraction method disclosed by the present application; Figure 2 A schematic diagram of an atmospheric level recognition and extraction processing flow disclosed by the present application; Figure 3 A schematic diagram of an atmospheric level recognition result disclosed by the present application; Figure 4 A schematic diagram of an atmospheric level recognition and extraction device structure disclosed by the present application; Figure 5 A structural diagram of an electronic device disclosed by the present application. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0019] Existing aerosol and cloud layer identification algorithms are mainly aimed at polarization detection or multi-wavelength detection lidar, and there is currently no special layer identification algorithm for hyperspectral lidar, especially spaceborne hyperspectral lidar. When the existing old algorithm is applied to the hyperspectral lidar, the unique technical advantages of the hyperspectral channel of the hyperspectral lidar cannot be fully utilized, and the atmospheric conditions such as thin layer, high altitude and high latitude cannot be accurately identified.

[0020] In order to overcome the above technical defects, the present application discloses an atmospheric layer identification extraction method, device, equipment and medium, which can use the backscattering signal in the atmospheric optical remote sensing signal detected by the hyperspectral channel of the hyperspectral lidar to identify the aerosol and cloud layer, so as to realize the atmospheric layer identification extraction of the hyperspectral lidar.

[0021] Referring to Figure 1 The embodiment of the present application discloses an atmospheric layer identification extraction method applied to a preset hyperspectral lidar, which comprises the following steps: Step S11, obtaining the detection signal of the preset hyperspectral lidar, and preprocessing the detection signal to calculate the atmospheric attenuation backscattering coefficient, the atmospheric scattering ratio, the molecular backscattering signal and the target atmospheric background signal according to the obtained preprocessed radar signal and the preset system parameters.

[0022] In this embodiment, as Figure 2 shown, first, the detection signal of the preset hyperspectral lidar needs to be obtained, and the obtained detection signal needs to be preprocessed, wherein the preset hyperspectral lidar is a spaceborne hyperspectral lidar, specifically, the detection signal of the preset hyperspectral lidar needs to be obtained to obtain the atmospheric total detection channel signal and the target hyperspectral detection channel signal It should be noted that if the radar has the ability of polarization detection, then , wherein is the vertical polarization channel signal, is the parallel polarization detection channel signal.

[0023] Further, the obtained detection signal needs to be preprocessed. Specifically, the spatial and temporal positions of the total atmospheric detection channel signal and the target hyperspectral detection channel signal corresponding to each profile need to be determined, and the total atmospheric detection channel signal and the target hyperspectral detection channel signal are geometrically corrected to a preset geodetic coordinate system according to the spatial and temporal positions to obtain a corrected radar signal. Further, the horizontal resolution and vertical resolution of each channel data in the corrected radar signal need to be averaged, and the corrected radar signal needs to be denoised to obtain a preprocessed radar signal. It needs to be noted that when averaging, the horizontal resolution and vertical resolution of each channel data need to be averaged according to the signal noise, different scenes during the day and at night. In this way, the preprocessing of the signal can effectively improve the accuracy of the atmospheric level recognition and extraction method.

[0024] After obtaining the preprocessed radar signal, the corresponding atmospheric optical remote sensing signal needs to be calculated according to the preprocessed radar signal and the preset system parameters, and the calculated atmospheric optical remote sensing signal includes the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal, and the target atmospheric background signal. Specifically, as shown in Figure 2 Since the target atmospheric background signal needs to use corresponding auxiliary data for calculation, the auxiliary data needs to be matched before calculating these signals. Specifically, the auxiliary data needs to be matched based on the preprocessed radar signal in the preset auxiliary data set to obtain the target auxiliary data. It needs to be noted that the auxiliary data includes temperature, pressure, and ozone mixing ratio, and the auxiliary data sources can be ERA5 (fifth generation ECMWF atmospheric reanalysis of the global climate), MERRA2 (Modern-Era Retrospective analysis for Research and Applications), etc. data set, or other high reliability data sources.

[0025] Further, the calculation process of the atmospheric attenuation backscatter coefficient is as follows: first, the product of the single-pulse laser energy in the preset system parameters and the cosine value of the laser beam zenith angle is calculated to obtain a first product, and the target distance between the detection height and the preset hyperspectral laser radar is determined; the ratio between the total atmospheric detection channel signal and the first product is determined to obtain a first ratio, and the product between the first ratio and the square of the target distance is taken as the atmospheric attenuation backscatter coefficient , that is, the atmospheric attenuation backscatter coefficient at height z, and its specific expression is as follows: ; where E is the single-pulse laser energy, is the laser beam zenith angle, and R is the distance from the atmosphere at height z to the lidar telescope, i.e., the target distance.

[0026] The calculation process of the atmospheric scattering ratio is as follows: the product between the target hyperspectral detection channel signal and the square of the target distance is calculated to obtain a second product, and the product between the first product and the first preset transmittance is calculated to obtain a third product; the ratio of the second product to the third product is used as the molecular backscattering signal, and the ratio between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal is used as the atmospheric scattering ratio , that is, the atmospheric scattering ratio at height z, which is expressed as follows: ; ; in, is the molecular backscattering signal after system correction, is the target hyperspectral detection channel signal at height z, is the transmittance of molecular Rayleigh backscattering through the hyperspectral narrowband filter (for spaceborne hyperspectral lidar, it is the transmittance of Rayleigh backscattering through the iodine molecule absorption cell), which is also the first preset transmittance.

[0027] The target atmospheric background signal is calculated as follows: based on the temperature and pressure in the target auxiliary data, the molecular two-way transmittance and the molecular backscattering coefficient are determined, 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. , which is the ideal pure molecular atmospheric background signal at height z, is expressed as follows: ; in, is the molecular backscattering coefficient under ideal conditions, is the molecular two-way transmittance, is the ozone two-way transmittance, is the second preset transmittance, that is, the transmittance of molecular Rayleigh scattering at height z through the hyperspectral lidar telescope and receiving system.

[0028] Step S12: performing atmospheric regional positioning using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar to obtain a regional positioning result.

[0029] In this embodiment, Figure 2 As shown, it is necessary to first use the total atmospheric detection channel signal Locate the position of the unpenetrated atmosphere in the hyperspectral lidar observation data. For spaceborne hyperspectral lidar, it is also necessary to extract the accurate ground layer based on the local altitude calculated based on the satellite's latitude and longitude position, combined with the saturation threshold of the detector receiving signal. In addition, since the spaceborne hyperspectral lidar data is stored in segments according to the orbital period. Assume that the processed data contains m profiles, and the height range for layer recognition in each profile is , and m and n are both positive integers greater than 0, then the detection matrix can be set First, it is necessary to extract the background noise of the pre-processed radar signal, and 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. Specifically, according to the parameter characteristics of the hyperspectral lidar system, the atmospheric total detection channel signal threshold at detection saturation is set to , according to the system detection noise The average value of the signal threshold when it is not saturated is set to .

[0030] Then, it is necessary to locate and identify the atmospheric total detection channel signal based on the detection signal saturation height threshold and the detection signal unsaturation height threshold to determine the regional signals corresponding to the surface area, the area below the surface, and the invalid observation area in the atmospheric total detection channel signal, and mark the regional signals accordingly based on the areas corresponding to the regional signals, and generate corresponding regional positioning results. Specifically, if the atmospheric total detection channel signal below 25km All less than , then the profile has no valid level and is marked as -2 in the detection matrix. Find the total atmospheric detection channel signal Greater than The height of , and i is an integer greater than 1, if there is a height , then the signal of the profile is saturated at the surface height position, that is, the profile is effectively observed from high altitude to the ground, and the position of the saturated signal is marked as -1, and the position below the height is marked as The minimum value is marked as -3, where is the altitude at the latitude and longitude of the profile. , then the profile does not have a valid echo on the surface, indicating that the signal of the profile is at a height above the surface. Because the signal energy is insufficient to penetrate a certain layer of aerosol or cloud, the data below this height are all invalid data, marked as -2 in the detection matrix. It should be noted that the data marked in this step does not participate in subsequent calculations. In this way, using the total atmospheric detection channel signal to extract the surface and unpenetrated areas from the hyperspectral lidar observations and first extracting the layers of these areas helps to avoid the influence of the layer signal on the subsequent aerosol and cloud layer identification.

[0031] Step S13: Performing atmospheric multi-level feature detection based on the regional positioning result, the molecular backscatter signal, the target atmospheric background signal, and the atmospheric attenuation backscatter coefficient to obtain an atmospheric multi-level feature matrix to be verified.

[0032] In this embodiment, Figure 2 As shown, adaptive window sliding double detection is required. Specifically, it is necessary to first determine the unlabeled signal in the total atmospheric detection channel signal based on the regional positioning result, and then construct a stratospheric detection difference function based on the difference between the atmospheric attenuation backscatter coefficient and the target atmospheric background signal, and construct a convective boundary layer detection difference function based on the difference between the atmospheric attenuation backscatter coefficient and the molecular backscatter signal. It should be noted that the convective boundary layer is the troposphere-boundary layer, wherein the stratospheric detection difference function and the convective boundary layer detection difference function are as follows: ; ; in, is the stratospheric detection difference function, is the difference function for convective boundary layer detection, is the atmospheric attenuation backscatter coefficient, The molecular backscattering signal after system correction is the target atmospheric background signal, , and k, i are both integers, representing the height of the kth profile Place.

[0033] When performing detection, it is first necessary to determine the multi-level characteristics of the atmosphere in the atmospheric attenuation backscatter coefficient based on the stratospheric detection difference function and the convective boundary layer detection difference function to obtain the first atmospheric multi-level characteristic matrix. Specifically, when there is a layer of aerosol or cloud, its atmospheric echo signal will be higher than the molecular signal. However, when processing the measured signal, due to the influence of noise, signal attenuation, etc. and The value distribution of cannot directly reflect the accurate layer characteristics of aerosols and clouds. Department or , then mark =1. Due to the low level of the measured hyperspectral channel signal, especially the weak measured signal in the stratosphere, the noise influence is large. There are more misjudgments at high altitudes. For thin cirrus clouds in the troposphere, or multi-layer aerosols and clouds, the simulation error of the ideal pure molecular atmospheric background simulation signal increases, resulting in There are many missed judgments.

[0034] Therefore, in order to avoid the above errors, it is necessary to construct a stratospheric detection window based on the stratospheric detection difference function, and to construct a convective boundary layer detection window based on the convective boundary layer detection difference function, so as to verify the multi-level characteristic matrix of the first atmosphere layer through the stratospheric detection window and the convective boundary layer detection window to obtain the multi-level characteristic matrix of the second atmosphere layer. Specifically, it is necessary to create two detection windows of different sizes according to the horizontal and vertical resolution of the data. and , are positive odd numbers, respectively, and the stratosphere and troposphere-boundary layer are detected twice.

[0035] Among them, for the stratosphere, if Then mark the second atmosphere multi-level characteristic matrix is 0, for the troposphere-boundary layer, if , marking the second atmosphere multi-level characteristic matrix is 1. Based on the second atmosphere multi-level characteristic matrix, the first atmosphere multi-level characteristic matrix is ​​corrected to obtain the atmosphere multi-level characteristic matrix to be verified. That is, under the same conditions, the marking result of the second atmosphere multi-level characteristic matrix shall prevail.

[0036] Step S14: verifying and continuity testing the multi-level characteristic matrix of the atmosphere to be verified to obtain a target characteristic matrix, and determining the target atmospheric aerosol characteristics and atmospheric cloud layer characteristics according to the target characteristic matrix.

[0037] In this embodiment, the high altitude and high latitude areas are more susceptible to noise. Therefore, the atmospheric scattering ratio can be used. , data quality ratio and atmospheric attenuation backscatter coefficient Perform auxiliary judgment of multi-parameter thresholds. Specifically, first, it is necessary to determine the data quality ratio of the target hyperspectral detection channel signal corresponding to each profile based on the background noise, and the expression of the data quality ratio is as follows: ; in, is the data quality ratio at the profile height z, is the original signal power at height z Background noise of the profile.

[0038] Further, the to-be-verified atmospheric multi-level feature matrix is verified by the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient, and the to-be-verified atmospheric multi-level feature matrix is secondarily corrected according to the verification result to obtain a corrected feature matrix, and a multi-threshold judgment matrix needs to be set , and the specific process is as follows: S141, comparing 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, and according to the actual detection situation, a higher value is taken in high altitude and a lower value is taken in low altitude. If , the first judgment matrix is marked as 1.

[0039] S142, comparing the data quality ratio with a preset data quality ratio threshold to generate a second judgment matrix. The data quality ratio threshold is , and the threshold is determined according to the system measured data quality distribution, and for example, the threshold is selected as 4 for ACDL (Aerosol and Carbon dioxide Detection Lidar, atmospheric detection laser radar). If , the second judgment matrix is marked as 1.

[0040] S143, comparing the atmospheric attenuation backscattering coefficient with a preset atmospheric attenuation backscattering coefficient threshold to generate a third judgment matrix. The average value of each atmospheric attenuation backscattering coefficient profile after denoising at a height of 30km-35km is taken as the threshold of the profile k . If , the third judgment matrix is marked as 1.

[0041] S144, the first judgment matrix, the second judgment matrix, and the third judgment matrix are weighted and summed by a preset weighting factor, and the obtained sum result is compared with the preset weighting factor to obtain a corresponding comparison result. Specifically, according to the characteristics of the aerosol and cloud level distribution in the stratosphere, the troposphere-boundary layer and the high latitude area, the multi-threshold judgment matrix is weighted and summed by a weighting factor . The value of the weighting factor can be adjusted according to the level judgment effect, and the value is usually selected according to the degree of influence in different regions, for example, in the stratosphere region, the value is usually selected as . According to the weighting of the multi-threshold judgment matrix, the detection matrix is secondarily judged.​

[0042] If is 0.

[0043] S145, based on the comparison result, the to-be-verified atmospheric multi-level feature matrix is modified again to obtain a modified feature matrix. Finally, the to-be-verified atmospheric multi-level feature matrix needs to be modified according to the comparison result to obtain the modified feature matrix.

[0044] Further, since the distribution of aerosol and cloud levels in the atmosphere is spatially and temporally continuous, the local continuity-based re-detection of the is needed to construct a filling detection window and an elimination detection window, and based on the filling detection window, the modified feature matrix is detected and eliminated, and the modified feature matrix is modified according to the obtained detection result to obtain the target feature matrix. Specifically, the filling detection window and the elimination detection window are created according to the identification of , respectively, which are positive odd numbers.

[0045] Then the filling detection window is used to detect in a sliding manner, and needs to be specifically divided into several cases: if , the mark is 1; if , the window is adaptively expanded to detect whether there is an effective level in a larger outer ring range to improve the local continuity of the level feature, if , the mark is 1. And the elimination detection window is also used to detect in a sliding manner: if , the value of the stratosphere and the troposphere boundary layer is different, the mark is 0. Finally, according to the level recognition continuity degree, the size of the filling detection window and the elimination detection window is iterated, the above steps are repeated, and the final matrix is taken as the target feature matrix.

[0046] Finally, the target feature matrix needs to be compared with the preset feature mark table to determine the target atmospheric aerosol feature and the atmospheric cloud level feature according to the comparison result, and the atmospheric level recognition result is Figure 3 ​It needs to be pointed out that each type of mark in the matrix corresponds to a feature, and the specific features of each mark can be determined by a preset feature mark table, and the preset feature mark table is shown in Table 1, and Table 1 is as follows: Table 1: Preset feature mark table .

[0047] In this embodiment, after the preset hyperspectral lidar collects the detection signal, the collected detection signal can be preprocessed, and the corresponding atmospheric optical remote sensing signal such as the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal and the target atmospheric background signal can be calculated according to the preprocessed radar signal and the preset system parameters. Further, the atmospheric region position needs to be preliminarily positioned by the atmospheric total detection channel signal in the preprocessed radar signal and the detection data of the radar, and then the multi-level feature detection of the atmospheric layer is performed according to the positioning result, the molecular backscatter signal, the target atmospheric background signal and the atmospheric attenuation backscatter coefficient, so as to obtain the to-be-verified multi-level feature matrix of the atmospheric layer. Finally, the to-be-verified multi-level feature matrix of the atmospheric layer needs to be verified and continuously detected, so as to obtain the target feature matrix, and the target atmospheric aerosol feature and the atmospheric layer cloud level feature are determined according to the target feature matrix. In this way, the method for identifying aerosol and cloud level in a large latitude range and a large height range can be developed by fully utilizing the unique hyperspectral channel of the spaceborne hyperspectral lidar according to the detection data characteristics and technical principles of the spaceborne hyperspectral lidar, and the method can be stably applied under various atmospheric conditions.

[0048] Referring to Figure 4 The embodiment of the present application discloses an atmospheric level recognition and extraction device applied to a preset hyperspectral lidar, which comprises: A parameter calculation module 11 is configured to obtain the detection signal of the preset hyperspectral lidar, and preprocess the detection signal to calculate the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal and the target atmospheric background signal according to the obtained preprocessed radar signal and the preset system parameters; An area positioning module 12 is configured to position the atmospheric region position by the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar, so as to obtain an area positioning result; A feature detection module 13 is configured to perform multi-level feature detection of the atmospheric layer based on the area positioning result, the molecular backscatter signal, the target atmospheric background signal and the atmospheric attenuation backscatter coefficient, so as to obtain a to-be-verified multi-level feature matrix of the atmospheric layer; The feature recognition module 14 is configured to verify and continuously detect the to-be-verified atmospheric multi-level feature matrix to obtain a target feature matrix, and determine target atmospheric aerosol features and atmospheric cloud level features according to the target feature matrix.

[0049] In this embodiment, after the preset hyperspectral lidar collects the detection signal, the collected detection signal is preprocessed, and the corresponding atmospheric optical remote sensing signals such as atmospheric attenuation backscattering coefficients, atmospheric scattering ratios, molecular backscattering signals, and target atmospheric background signals are calculated according to the preprocessed radar signal and the preset system parameters. Further, the atmospheric region position is preliminarily positioned by the atmospheric total detection channel signal in the preprocessed radar signal and the detection data of the radar, and then the atmospheric multi-level feature detection is performed according to the positioning result, the molecular backscattering signal, the target atmospheric background signal, and the atmospheric attenuation backscattering coefficient to obtain a to-be-verified atmospheric multi-level feature matrix. Finally, the to-be-verified atmospheric multi-level feature matrix is verified and continuously detected to obtain a target feature matrix, and target atmospheric aerosol features and atmospheric cloud level features are determined according to the target feature matrix. In this way, the measured molecular backscattering signal detected by the high spectral channel of the hyperspectral lidar can be used for aerosol and cloud level recognition to realize the atmospheric level recognition and extraction of the hyperspectral lidar.

[0050] In some embodiments, the parameter calculation module 11 can specifically include: The signal acquisition unit is configured to acquire the detection signal of the preset hyperspectral lidar to obtain an atmospheric total detection channel signal and a target hyperspectral detection channel signal. The signal correction unit is configured to determine the space-time position of each profile corresponding to the atmospheric total detection channel signal and the target hyperspectral detection channel signal, and geometrically correct the atmospheric total detection channel signal and the target hyperspectral detection channel signal to a preset geodetic coordinate system according to the space-time position to obtain a corrected radar signal. The signal averaging unit is configured to average the horizontal resolution and vertical resolution of each channel data in the corrected radar signal, and denoise the corrected radar signal to obtain a preprocessed radar signal.

[0051] In some embodiments, the atmospheric level recognition and extraction device can further include: The data matching unit is configured to perform auxiliary data matching in a preset auxiliary data set based on the preprocessed radar signal to obtain target auxiliary data.

[0052] In some embodiments, the parameter calculation module 11 can specifically include: The product of the single-pulse laser energy in the preset system parameter and the cosine value of the laser beam zenith angle is calculated to obtain a first product, and a target distance between the to-be-detected height and the preset hyperspectral lidar is determined; The first signal determination unit is configured to determine a ratio between the total atmospheric detection channel signal and the first product to obtain a first ratio, and take the product between the first ratio and the square of the target distance as an atmospheric attenuation backscatter coefficient; The first signal calculation unit is configured to 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 a first preset transmittance to obtain a third product; The second signal determination unit is configured to take the ratio between the second product and the third product as a molecular backscatter signal, and take the ratio between the atmospheric attenuation backscatter coefficient and the molecular backscatter signal as an atmospheric scattering ratio; The second signal calculation unit is configured to determine a molecular two-way transmittance and a molecular backscatter coefficient based on the temperature and the pressure in the target auxiliary data, and determine an ozone two-way transmittance by the temperature, the pressure, and an ozone mixing ratio in the target auxiliary data; The third signal determination unit is configured to calculate the product between the molecular backscatter coefficient, the ozone two-way transmittance, the molecular two-way transmittance, and a second preset transmittance to obtain a target atmospheric background signal.

[0053] In some embodiments, the region positioning module 12 can specifically include: The threshold setting unit is configured to extract background noise of the preprocessed radar signal, set a detection signal saturated height threshold according to the preset system parameter, and set a detection signal unsaturated height threshold according to the background noise; The signal positioning and identification unit is configured to perform positioning and identification on the total atmospheric detection channel signal based on the detection signal saturated height threshold and the detection signal unsaturated height threshold, to determine region signals corresponding to a ground surface region, a region below the ground surface, and an invalid observation region in the total atmospheric detection channel signal; The region positioning unit is configured to mark the region signals according to corresponding regions of the region signals based on the region signals, and generate a corresponding region positioning result.

[0054] In some embodiments, the feature detection module 13 can specifically include: The signal determination unit is configured to determine an unmarked signal in the total atmospheric detection channel signal based on the region positioning result; a function construction unit configured to construct a stratosphere detection difference function based on a 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 a difference between the atmospheric attenuation backscattering coefficient and the molecular backscattering signal; a first feature determination unit configured to determine atmospheric layer multi-level features in the atmospheric attenuation backscattering coefficient based on the stratosphere detection difference function and the convective boundary layer detection difference function, to obtain a first atmospheric layer multi-level feature matrix; a second feature determination unit configured to construct a stratosphere detection window based on the stratosphere detection difference function, and to construct a convective boundary layer detection window based on the convective boundary layer detection difference function, to perform verification detection on the first atmospheric layer multi-level feature matrix through the stratosphere detection window and the convective boundary layer detection window, to obtain a second atmospheric layer multi-level feature matrix; a first feature correction unit configured to correct the first atmospheric layer multi-level feature matrix based on the second atmospheric layer multi-level feature matrix, to obtain a to-be-verified atmospheric layer multi-level feature matrix.

[0055] In some embodiments, the feature recognition module 14 can specifically include: a quality ratio determination sub-module configured to determine a data quality ratio of each profile corresponding to the target hyperspectral detection channel signal based on the background noise; a first feature correction sub-module configured to verify the to-be-verified atmospheric layer multi-level feature matrix through the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscattering coefficient, and to perform secondary correction on the to-be-verified atmospheric layer multi-level feature matrix according to a verification result, to obtain a corrected feature matrix; a second feature correction sub-module configured to construct a filling detection window and an elimination detection window, to perform continuity detection on the corrected feature matrix based on the filling detection window and the elimination detection window, and to perform continuity correction on the corrected feature matrix according to a detection result obtained, to obtain a target feature matrix; a feature determination sub-module configured to compare the target feature matrix with a preset feature marker table, to determine a target atmospheric layer aerosol feature and an atmospheric layer cloud level feature according to a comparison result.

[0056] In some embodiments, the first feature correction sub-module can further include: a first determination matrix generation unit configured to compare the atmospheric scattering ratio with a preset atmospheric scattering ratio threshold, to generate a first determination matrix; a second determination matrix generation unit configured to compare the data quality ratio with a preset data quality ratio threshold, to generate a second determination matrix; a third determination matrix generating unit configured to compare the atmospheric attenuation backscattering coefficient with a preset atmospheric attenuation backscattering coefficient threshold to generate a third determination matrix; a data comparison unit configured to perform weighted summation on the first determination matrix, the second determination matrix and the third determination matrix by a preset weighting factor, and compare the obtained summation result with the preset weighting factor to obtain a corresponding comparison result; a second feature correction unit configured to perform secondary correction on the to-be-verified atmospheric layer multi-level feature matrix based on the comparison result to obtain a corrected feature matrix.

[0057] Further, the embodiment of the present application further discloses an electronic device, Figure 5 is a structural diagram of an electronic device 20 according to an exemplary embodiment, and the contents in the figure cannot be considered as any limitation on the use range of the present application.

[0058] Figure 5 A structural diagram of an electronic device 20 is provided in the embodiment of the present application. The electronic device 20 specifically can 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 is used to store a computer program, which is loaded and executed by the processor 21 to implement the related steps in the atmospheric layer identification and extraction method disclosed in any of the preceding embodiments. In addition, the electronic device 20 in the embodiment can be an electronic computer.

[0059] In the embodiment, the power supply 23 is used to provide working 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 followed by the communication interface 24 can be any communication protocol applicable to the technical solution of the present application, which is not limited specifically herein; the input / output interface 25 is used to obtain external input data or output data to the outside, and the specific interface type can be selected according to the specific application needs, which is not limited specifically herein.

[0060] In addition, the memory 22 as a carrier for resource storage can be a read-only memory, a random access memory, a magnetic disk or an optical disk, etc., and the resources stored thereon can include an operating system 221, a computer program 222, etc., and the storage mode can be temporary storage or permanent storage.

[0061] 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 can be Windows Server, Netware, Unix, Linux, etc. In addition to including the computer program capable of completing the atmospheric level recognition extraction method performed by the electronic device 20 disclosed in any of the preceding embodiments, the computer program 222 can further include a computer program capable of completing other specific work.

[0062] Further, the present application also discloses a computer readable storage medium for storing a computer program; wherein the computer program is executed by a processor to implement the atmospheric level recognition extraction method disclosed above. For the specific steps of the method, please refer to the corresponding content disclosed in the preceding embodiments, which will not be repeated here.

[0063] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between various embodiments can be mutually referred to. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method part.

[0064] The skilled person can further realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0065] The steps of the method or algorithm described in combination with the embodiments disclosed herein can be directly implemented by hardware, a software module executed by a processor, or a combination of both. The software module can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0066] Finally, it needs to be pointed out that in this document, relational terms such as first and second and the like can only be intended to distinguish one entity or operation from another entity or operation without necessarily requiring or implying any actual such relationship or order between such entities or operations. Moreover, the terms "comprising", "including", or any other variant thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without more limitations, an element defined by the statement "comprising a" does not exclude the existence of additional identical elements in the process, method, article, or apparatus including the stated element.

[0067] The above detailed description of the technical solutions provided by the present application has been provided, and the principles and implementation manners of the present application have been described by applying specific examples. The above description of the examples is only for the purpose of helping to understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation manner and application range will be changed, and the above description of the content of the specification should not be understood as a limitation of the present application.

Claims

1. A method for identifying and extracting atmospheric layers, characterized in that: Applicable to preset hyperspectral lidar, including: Acquire the detection signal of the preset hyperspectral lidar and preprocess the detection signal to calculate the atmospheric attenuation backscattering coefficient, the atmospheric scattering ratio, the molecular backscattering signal and the target atmospheric background signal based on the obtained preprocessed radar signal and preset system parameters; Positioning the atmospheric region using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral lidar to obtain a regional positioning result; Performing atmospheric multi-level feature detection based on the regional positioning result, the molecular backscatter signal, the target atmospheric background signal, and the atmospheric attenuation backscatter coefficient to obtain a multi-level feature matrix of the atmosphere to be verified; The multi-level characteristic matrix of the atmosphere to be verified is verified and continuity tested to obtain a target characteristic matrix, and the target atmospheric aerosol characteristics and atmospheric cloud layer characteristics are determined according to the target characteristic matrix.

2. The atmospheric layer identification and extraction method according to claim 1, wherein The acquiring of the detection signal of the preset hyperspectral laser radar and preprocessing the detection signal includes: Acquire the detection signal of the preset hyperspectral lidar to obtain the total atmospheric detection channel signal and the target hyperspectral detection channel signal; Determine the spatiotemporal position of the total atmospheric detection channel signal and the target hyperspectral detection channel signal corresponding to each profile, and 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 a corrected radar signal; The horizontal resolution and vertical resolution of each channel data in the corrected radar signal are averaged, and the corrected radar signal is denoised to obtain a preprocessed radar signal.

3. The atmospheric layer identification and extraction method according to claim 2, wherein: Before calculating the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal, and the target atmospheric background signal based on the obtained pre-processed radar signal and the preset system parameters, the method further includes: performing auxiliary data matching in a preset auxiliary data set based on the preprocessed radar signal to obtain target auxiliary data; Accordingly, the calculation of the atmospheric attenuation backscatter coefficient, the atmospheric scattering ratio, the molecular backscatter signal, and the target atmospheric background signal based on the obtained pre-processed radar signal and the preset system parameters includes: Calculating the product of the single-pulse laser energy and the cosine value of the laser beam zenith angle in the preset system parameters to obtain a first product, and determining the target distance between the height to be detected and the preset hyperspectral laser radar; Determining a ratio between the total atmospheric detection channel signal and the first product to obtain a first ratio, and taking the product of the first ratio and the square of the target distance as an atmospheric attenuation backscatter coefficient; Calculating the product of the target hyperspectral detection channel signal and the square of the target distance to obtain a second product, and calculating the product of the first product and a first preset transmittance to obtain a third product; taking the ratio of the second product to the third product as a molecular backscattering signal, and taking the ratio of the atmospheric attenuation backscattering coefficient to the molecular backscattering signal as an atmospheric scattering ratio; determining a molecular two-way transmittance and a molecular backscattering coefficient based on the temperature and pressure in the target auxiliary data, and determining an ozone two-way transmittance 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 a second preset transmittance is calculated to obtain a target atmospheric background signal.

4. The atmospheric layer identification and extraction method according to claim 2, wherein: The atmospheric regional position positioning is performed using the atmospheric total detection channel signal in the preprocessed radar signal and the observation data of the preset hyperspectral laser radar to obtain a regional positioning result, including: Extracting background noise from the preprocessed radar signal, and setting a detection signal saturation height threshold according to the preset system parameters, and setting a detection signal unsaturation height threshold according to the background noise; Positioning and identifying the atmospheric total detection channel signal based on the detection signal saturation height threshold and the detection signal unsaturation height threshold to determine regional signals corresponding to the surface area, the subsurface area, and the invalid observation area in the atmospheric total detection channel signal; The area signal is marked accordingly based on the area corresponding to the area signal, and a corresponding area 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 multi-level characteristic detection of the atmosphere is performed based on the regional positioning result, the molecular backscatter signal, the target atmospheric background signal, and the atmospheric attenuation backscatter coefficient to obtain a multi-level characteristic matrix of the atmosphere to be verified, including: Determine an unmarked signal in the total atmospheric detection channel signal based on the regional positioning result; Constructing a stratospheric detection difference function based on the difference between the atmospheric attenuation backscatter coefficient and the target atmospheric background signal, and constructing a convective boundary layer detection difference function based on the difference between the atmospheric attenuation backscatter coefficient and the molecular backscatter signal; Determining the atmospheric multi-level characteristics in the atmospheric attenuation backscatter coefficient according to the stratospheric detection difference function and the convective boundary layer detection difference function to obtain a first atmospheric multi-level characteristic matrix; Constructing a stratospheric detection window according to the stratospheric detection difference function, and constructing a convective boundary layer detection window according to the convective boundary layer detection difference function, so as to verify and detect the first atmospheric multi-level characteristic matrix through the stratospheric detection window and the convective boundary layer detection window to obtain a second atmospheric multi-level characteristic matrix; The first atmosphere multi-level characteristic matrix is ​​corrected based on the second atmosphere multi-level characteristic matrix to obtain the atmosphere multi-level characteristic matrix to be verified.

6. The atmospheric layer identification and extraction method according to claim 4, wherein: The verification and continuity detection of the multi-level characteristic matrix of the atmosphere to be verified to obtain a target characteristic matrix, and determining the target atmospheric aerosol characteristics and atmospheric cloud layer characteristics according to the target characteristic matrix, include: Determine the data quality ratio of each profile of the target hyperspectral detection channel signal based on the background noise; Verifying the multi-level characteristic matrix of the atmosphere to be verified by using the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscatter coefficient, and performing a secondary correction on the multi-level characteristic matrix of the atmosphere to be verified according to the verification result to obtain a corrected characteristic matrix; Constructing a filling detection window and an elimination detection window, and performing continuity detection on the corrected feature matrix based on the filling detection window and the elimination detection window, and performing continuity correction on the corrected feature matrix according to the obtained detection results to obtain a 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 according to the comparison results.

7. The atmospheric layer identification and extraction method according to claim 6, characterized in that: The method further comprises verifying the multi-level characteristic matrix of the atmosphere to be verified by using the atmospheric scattering ratio, the data quality ratio, and the atmospheric attenuation backscatter coefficient, and performing a secondary correction on the multi-level characteristic matrix of the atmosphere to be verified according to the verification result to obtain a corrected characteristic matrix, including: Comparing the atmospheric scattering ratio with a preset atmospheric scattering ratio threshold to generate a first determination matrix; Comparing the data quality ratio with a preset data quality ratio threshold to generate a second determination matrix; Comparing the atmospheric attenuation backscatter coefficient with a preset atmospheric attenuation backscatter coefficient threshold to generate a third determination matrix; Performing weighted summation on the first determination matrix, the second determination matrix, and the third determination matrix using a preset weighting factor, and comparing the summation result with the preset weighting factor to obtain a corresponding comparison result; Based on the comparison result, the multi-level characteristic matrix of the atmosphere to be verified is corrected twice to obtain a corrected characteristic matrix.

8. An atmospheric layer identification and extraction device, characterized in that: Applicable to preset hyperspectral lidar, including: a parameter calculation module for obtaining the detection signal of the preset hyperspectral lidar and preprocessing the detection signal to calculate the atmospheric attenuation backscattering coefficient, the atmospheric scattering ratio, the molecular backscattering signal, and the target atmospheric background signal based on the obtained preprocessed radar signal and preset system parameters; A 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 to obtain a regional positioning result; A feature detection module is used to perform multi-level feature detection of the atmosphere based on the regional positioning result, the molecular backscatter signal, the target atmospheric background signal and the atmospheric attenuation backscatter coefficient to obtain a multi-level feature matrix of the atmosphere 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 to obtain a target feature matrix, and determine the target atmospheric aerosol characteristics and atmospheric cloud layer characteristics based on the target feature matrix.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor is used to execute 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 when the computer program is executed by a processor, the atmospheric layer identification and extraction method as described in any one of claims 1 to 7 is implemented.

Images