Multiple scattering correction method for spaceborne photon counting laser radar water body diffuse attenuation coefficient

Abstract

The invention belongs to the technical field of ocean laser radar remote sensing detection, and particularly relates to a multiple scattering correction method for a spaceborne photon counting laser radar water body diffuse attenuation coefficient. Comprising the following steps: preprocessing a point cloud signal; calculating an average sea level; extracting a water body photon signal, and performing refractive index correction; performing histogram statistics on the water photons to obtain pseudo echo signals; correcting the transient response of the detector; calculating the attenuation coefficient of the laser radar system; matching a water body diffuse attenuation coefficient; establishing a multiple scattering correction empirical model; and inputting the calculated attenuation coefficient of the laser radar system into the established multi-scattering correction model so as to obtain an accurate water body diffuse attenuation coefficient. According to the invention, data inconsistency caused by multiple scattering can be effectively reduced. Through the improved multiple scatter correction method, the Kd coefficient can be estimated from the laser radar data more accurately, so that more reliable data support is provided for marine scientific research and environmental monitoring.

Description

Technical Field

[0001] The invention belongs to the technical field of ocean laser radar remote sensing detection, and in particular relates to a multiple scattering correction method for water body diffuse attenuation coefficient of satellite-borne photon counting laser radar. Background Art

[0002] With the development of satellite remote sensing technology, photon counting lidar (such as the ATLAS system carried by ICESat-2) has become an important tool for obtaining ocean optical properties. These properties include the diffuse attenuation coefficient (Kd) of water bodies, which is a key parameter in ocean optics and bio-optics research and is crucial to understanding the availability of light in marine ecosystems. The Kd coefficient can reflect the degree of light attenuation in water bodies and is of great significance for assessing marine primary productivity and marine biogeochemical cycles. Although existing technologies have been able to estimate the Kd coefficient using passive remote sensing methods (such as MODIS data), passive remote sensing counting relies on sunlight and is difficult to provide effective observation data in high-latitude waters. Traditional lidar processing algorithms are usually based on continuous waveform data, while the discreteness of lidar point cloud data makes these algorithms difficult to apply directly. In addition, due to its working principle, the lidar system records the position information of each received photon, which requires a new data processing method to accurately invert the Kd coefficient. To make it more complicated, the propagation of lidar signals in water bodies is not only affected by single scattering, but also by multiple scattering. Multiple scattering will change the intensity and distribution of the signal received by the lidar, thus affecting the accurate inversion of the Kd coefficient. The attenuation coefficient directly inverted from the lidar data (usually expressed as the lidar system attenuation coefficient) is not completely equivalent to the Kd coefficient of the actual water body, because this part of the coefficient includes the interference of multiple scattering on the signal. Therefore, it is necessary to correct multiple scattering to more accurately invert the water body diffuse attenuation coefficient. Summary of the invention

[0003] In view of the above problems, the present invention provides a novel multiple scattering correction method specifically for spaceborne photon counting lidar data. The method includes a correction model for the effects of multiple scattering, which can effectively reduce the data inconsistency caused by multiple scattering. Through this improved multiple scattering correction method, the Kd coefficient can be estimated more accurately from the lidar data, thereby providing more reliable data support for marine scientific research and environmental monitoring.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A multiple scattering correction method for water diffuse attenuation coefficient of spaceborne photon counting laser radar, the method comprising the following steps:

[0006] Step 1: Preprocess the point cloud signal to remove data that affects the signal quality;

[0007] Step 2: Calculate mean sea level;

[0008] Step 3: Extract the photon signal in the water body and perform refractive index correction to adjust the photon position;

[0009] Step 4: Perform histogram statistics on the water body photon signal to generate a pseudo echo signal;

[0010] Step 5: Perform deconvolution correction on the transient response of the detector to obtain a corrected echo signal;

[0011] Step 6: Calculate the attenuation coefficient of the lidar system;

[0012] Step 7: Match the radar system attenuation coefficient and the water body diffuse attenuation coefficient according to time and longitude and latitude;

[0013] Step 8: Use the matched data and machine learning methods to establish an empirical model for multiple scattering correction;

[0014] Step 9: Use the calculated LiDAR system attenuation coefficient and apply the multiple scattering correction empirical model to obtain the corrected water diffuse attenuation coefficient.

[0015] Preferably, in step 1, the point cloud signal is preprocessed to remove data with atmospheric aerosol optical thickness greater than 3 and the number of sea surface reflected photons of a single laser pulse greater than 12, thereby avoiding the influence of clouds and sea surface mirror reflection on the echo signal. The influence of these factors on the echo signal can be effectively reduced, thereby improving the purity of the data and the accuracy of subsequent calculations.

[0016] Preferably, the average sea level calculated in step 2 is the accumulation of photons, and the position corresponding to the peak value is the location of the average sea level, which ensures the accuracy of geographical and water body photon signals during the calculation process and lays the foundation for subsequent refractive index correction and pseudo echo signal statistics.

[0017] Preferably, in step 3, water body photon signals are extracted and refractive index correction is performed:

[0018]

[0019] Among them, H0 is the original height of the point cloud, H mean is the calculated mean sea level, n is the water refractive index, H z Photon positions after refractive index correction.

[0020] By performing refractive index correction on water body photon signals, the present invention can adjust the position of the photon signals according to the optical properties of the water body, making subsequent photon signal processing more accurate and improving the authenticity and reliability of the data.

[0021] Preferably, in step 4, water body photons are accumulated along the track and histogram statistics are performed to obtain a pseudo echo signal.

[0022] Preferably, the transient response correction of the detector in step 5 is achieved by deconvolution:

[0023] S′(z)=F -1 S(z);

[0024] Wherein, S(z) is the pseudo echo signal obtained in step 4, F is the transient response of the detector, S'(z) is the corrected echo signal, and z is the depth.

[0025] Preferably, the laser radar system attenuation coefficient K is calculated in step 6. lidar for:

[0026]

[0027] Preferably, in step 7, the radar system attenuation coefficient K is matched according to time and longitude and latitude. lidar and water body diffuse attenuation coefficient Kd, which adopts water color satellite observation data or measured observation data;

[0028] In step 8, a K-based lidar A data-driven multiple scattering correction empirical model with the LiDAR system parameters as input and the water diffuse attenuation coefficient Kd as the reference value;

[0029] The multiple scattering corrected empirical model utilizes machine learning algorithms to identify patterns and relationships in the data and is represented as follows:

[0030] K d =f(K lidar , FOV, DIV, H, θ);

[0031] Where FOV is the receiving field of view, DIV is the laser beam divergence angle, H is the height of the lidar, and θ is the nadir angle of the laser beam.

[0032] Furthermore, the present invention also discloses a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-mentioned multiple scattering correction method.

[0033] Furthermore, the present invention also discloses a computer-readable storage medium on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the multiple scattering correction method described above is implemented.

[0034] The beneficial effects of the present invention are as follows:

[0035] (1) By developing a correction model for the influence of multiple scattering and using machine learning methods, the interference of multiple scattering on the signal can be effectively corrected, so that the attenuation coefficient inverted from the lidar data is closer to the diffuse attenuation coefficient of the actual water body, which can effectively reduce the data inconsistency caused by multiple scattering, thereby improving the inversion accuracy of the Kd coefficient.

[0036] (2) The method of the present invention is particularly suitable for spaceborne photon counting lidar systems, such as the ATLAS system carried by ICESat-2. Such systems are becoming increasingly important in marine optical research, and the method provided by the present invention effectively makes up for the deficiency of traditional algorithms in being unable to adapt to point cloud data, and expands the application scope of lidar technology in marine exploration.

[0037] (3) By correcting the multiple scattering effect, the present invention greatly improves the accuracy of water body diffuse attenuation coefficient inversion, which is of great significance for marine optics and bio-optics research. Accurate attenuation coefficient data can be used to evaluate marine primary productivity, marine biogeochemical cycles, and water quality monitoring, providing more reliable data support for marine scientific research and environmental protection.

[0038] (4) The present invention uses machine learning methods to construct a correction model, making full use of matching data and system parameters, so that the correction model has higher flexibility and accuracy. This data-driven correction method is highly adaptable and can be continuously optimized as data increases and the system improves, thereby improving the performance of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of the multiple scattering correction method for the diffuse attenuation coefficient of water bodies in spaceborne photon counting lidar.

[0040] Figure 2 It is the point cloud distribution after refraction correction, as well as the waveform result after pseudo waveform accumulation and transient response correction.

[0041] Figure 3 It is the result of establishing the empirical model for multiple scattering correction.

[0042] Figure 4 It is a comparison between the inverted water optical parameters before and after multiple scattering correction and the measured results. DETAILED DESCRIPTION

[0043] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, the multiple scattering correction method for the water body diffuse attenuation coefficient of the space-borne photon counting laser radar of the present invention specifically comprises the following steps:

[0045] Step 1: Preprocess the point cloud signal;

[0046] This embodiment uses ICESat-2 satellite-borne photon counting lidar data to invert the water body diffuse attenuation coefficient as an example, and pre-processes the point cloud signal to remove the data with an atmospheric aerosol optical thickness greater than 3 and the number of sea surface reflected photons of a single laser pulse greater than 12, thereby avoiding the influence of clouds and sea surface mirror reflection on the echo signal;

[0047] Step 2: Calculate the mean sea level, accumulate the photons, and the position corresponding to the peak value is the mean sea level;

[0048] Step 3: Extract water photon signals and perform refractive index correction:

[0049]

[0050] Among them, H0 is the original height of the point cloud, H mean is the average sea level calculated in step 3, n is the refractive index of water, H z The photon position after refractive index correction. The corrected photon point cloud distribution is as follows Figure 2 As shown in the left picture.

[0051] Step 4: Accumulate water photons along the track and perform histogram statistics to obtain pseudo echo signals. The accumulated waveform is as follows: Figure 2 As shown in the picture on the right.

[0052] Step 5: Correct the transient response of the detector by deconvolution:

[0053] S′(z)=F -1 S(z)

[0054] Among them, S(z) is the pseudo echo signal obtained in step 4, F is the transient response of the detector, S′(z) is the corrected echo signal, and z is the depth. The corrected waveform is as follows Figure 2 As shown in the picture on the right.

[0055] Step 6: Calculate the LiDAR system attenuation coefficient Klidar for:

[0056]

[0057] Figure 2 The K calculated by fitting in the right figure lidar =0.55m -1 .

[0058] Step 7: Match the radar system attenuation coefficient and the water body diffuse attenuation coefficient according to time and longitude and latitude. The water body diffuse attenuation coefficient can use water color satellite observation data or measured observation data. In this case, the satellite remote sensing product MODIS of the same month is matched to build a training data set, and the measured buoy data is used for verification.

[0059] Step 8: Based on the matching data set, construct a K lidar The empirical model of multiple scattering correction driven by data takes the parameters of the laser radar system as input and the water diffuse attenuation coefficient Kd as the reference value. The empirical multiple scattering model uses the powerful ability of machine learning algorithms to identify patterns and relationships in the data. This process is expressed as follows: K d =f(K lidar , FOV, DIV, H, θ). Where FOV is the receiving field of view, DIV is the laser beam divergence angle, H is the height of the lidar, and θ is the nadir angle of the laser beam. These features were selected based on their significant contribution to multiple scattering. To determine the accuracy of the model, the matching data was randomly divided into training and test subsets, with 70% of the training set and 30% of the test set. This case uses the random forest machine learning method to convert the inverted K lidar And the laser radar system parameters: laser beam divergence angle, laser radar height, laser beam nadir angle as input, water body diffuse attenuation coefficient K d As the true value, a multiple scattering empirical model is constructed. The model is constructed as follows Figure 3 As shown, the left figure is the model training result, and the right figure is the evaluation result of the model on the test set. The correlation coefficients of the two are both above 0.8, indicating the accuracy of the constructed correction model.

[0060] Step 9: Input the LiDAR system attenuation coefficient calculated in step 6 into the multiple scattering correction empirical model established in step 8 to obtain the accurate water body diffuse attenuation coefficient. The results before and after multiple scattering correction are as follows: Figure 4 As shown, it can be seen that the corrected attenuation coefficient is more consistent with the water body attenuation coefficient and has higher accuracy. Figure 4 This is a comparison chart of the LiDAR water parameter inversion results and the in-situ measurement results. The closer the discrete points are to the 1:1 line, the more accurate the inversion results are. Figure 4The discrete points in the right figure are concentrated near the 1:1 line, and the correlation coefficient R between the multiple scattering correction results and the in-situ measured data is above 0.7, which shows the necessity of multiple scattering correction and the accuracy of the established multiple scattering correction empirical model.

[0061] The above is a description of the embodiments of the present invention. Through the above description of the disclosed embodiments, professionals and technicians in the field can implement or use the present invention. Various modifications to these embodiments will be apparent to professionals and technicians in the field. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown in this article, but will conform to the widest range consistent with the principles and novelties disclosed herein.

Claims

1. A multiple scattering correction method for water diffuse attenuation coefficient of spaceborne photon counting laser radar, characterized in that: The method comprises the following steps: Step 1: Preprocess the point cloud signal to remove data that affects the signal quality; Step 2: Calculate mean sea level; Step 3: Extract the photon signal in the water body and perform refractive index correction to adjust the photon position; Step 4: Perform histogram statistics on the water body photon signal to generate a pseudo echo signal; Step 5: Perform deconvolution correction on the transient response of the detector to obtain a corrected echo signal; Step 6: Calculate the attenuation coefficient of the lidar system; Step 7: Match the radar system attenuation coefficient and the water body diffuse attenuation coefficient according to time and longitude and latitude; Step 8: Use the matched data and machine learning methods to establish an empirical model for multiple scattering correction; Step 9: Use the calculated LiDAR system attenuation coefficient and apply the multiple scattering correction empirical model to obtain the corrected water diffuse attenuation coefficient.

2. The method according to claim 1, characterized in that: In step 1, the point cloud signal is preprocessed to remove data with atmospheric aerosol optical thickness greater than 3 and the number of sea surface reflected photons of a single laser pulse greater than 12, thereby avoiding the influence of clouds and sea surface mirror reflection on the echo signal.

3. The method according to claim 1, characterized in that: The mean sea level calculated in step 2 is the accumulation of photons, and the position corresponding to the peak value is the location of the mean sea level.

4. The method according to claim 1, characterized in that: In step 3, water photon signals are extracted and refractive index correction is performed: Among them, H0 is the original height of the point cloud, H mean is the calculated mean sea level, n is the water refractive index, H z Photon positions after refractive index correction.

5. The method according to claim 4, characterized in that: In step 4, the water body photons are accumulated along the track and histogram statistics are performed to obtain a pseudo echo signal.

6. The method according to claim 5, characterized in that: In step 5, the detector transient response correction is achieved by deconvolution: S′(z)=F -1 S(z) Wherein, S(z) is the pseudo echo signal obtained in step 4, F is the transient response of the detector, S'(z) is the corrected echo signal, and z is the depth.

7. The method according to claim 6, characterized in that: In step 6, calculate the attenuation coefficient K of the laser radar system lidar for:

8. The method according to claim 7, characterized in that: In step 7, the radar system attenuation coefficient K is matched according to the time and longitude and latitude. lidar and water body diffuse attenuation coefficient Kd, the water body diffuse attenuation coefficient Kd uses water color satellite observation data or measured observation data; in step eight, a K lidar A data-driven multiple scattering correction empirical model with the LiDAR system parameters as input and the water diffuse attenuation coefficient Kd as the reference value; The multiple scattering corrected empirical model utilizes machine learning algorithms to identify patterns and relationships in the data and is represented as follows: K d =f(K lidar ,FOV,DIV,H,θ); Where FOV is the receiving field of view, DIV is the laser beam divergence angle, H is the height of the lidar, and θ is the nadir angle of the laser beam.

9. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the multiple scattering correction method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a processor, the multiple scattering correction method according to any one of claims 1 to 8 is implemented.

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