Processing method and device for filtering satellite radar altimetry data of a lake and electronic equipment

Abstract

The application discloses a kind of lake satellite radar altimetry data screening processing method, device and electronic equipment.Therein, the method includes: obtaining random sample data from global lake satellite radar altimetry data;Wherein, the basic data required by random sample data includes global satellite altimetry data and lake boundary data;Random sample data is processed based on the preset graphical interface tool, and the echo waveform and multi-source auxiliary information corresponding to random sample data are shown;Wherein, multi-source auxiliary information includes multi-source footprint characteristic value and geographic location information;Random sample data is marked according to the category based on echo waveform and multi-source auxiliary information;Machine learning model training is carried out using random sample data of marked type, and the automatic classification model of lake satellite radar altimetry data is obtained.The application solves the technical problem of low sampling classification efficiency of lake satellite radar altimetry data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing information processing, in particular to a lake satellite radar altimetry data screening processing method and device and electronic equipment. BACKGROUND

[0002] With the development of satellite altimetry technology, satellite altimetry data is widely used in global lake water level monitoring. However, the radar satellite observation data used for altimetry (i.e. the area covered by the radar satellite signal on the ground, also known as the radar footprint) is often as high as several kilometers, and is easily affected by the surrounding environment, so the lake echo is prone to radar footprint pollution problems. However, since the altimetry satellite footprint spacing is usually only two or three hundred meters, a large number of radar footprints fall into the lake. In order to obtain accurate lake surface elevation, it is necessary to select radar echoes with lighter echo pollution from the along-track footprints.

[0003] In order to control the quality of the echo, it is necessary to analyze and classify all or part of the radar footprint data of the lake. In related technologies, clustering methods or threshold methods are used. For the clustering method, several key feature information of the echo is generally obtained, and then a clustering method such as k-means is used to remove echoes with poor quality. This method is time-consuming and inefficient. For the threshold method, the automatic gain control coefficient, backscatter coefficient and other typical field information are analyzed using local area footprint data to obtain a certain threshold, and then all footprint data is screened. This method is not suitable for large-scale radar footprint screening. SUMMARY

[0004] The embodiments of the present application provide a lake satellite radar altimetry data screening processing method and device and electronic equipment to at least solve the technical problem of low sampling classification efficiency of lake satellite radar altimetry data.

[0005] According to an aspect of the embodiments of the present application, a lake satellite radar altimetry data screening processing method is provided, comprising: obtaining random sample data from global lake satellite radar altimetry data; wherein the required basic data of the random sample data includes global satellite altimetry data and lake boundary data; processing the random sample data based on a pre-set graphical interface tool, and displaying the echo waveform and multi-source auxiliary information corresponding to the random sample data; wherein the multi-source auxiliary information includes multi-source footprint characteristic values and geographic location information; classifying the random sample data according to the echo waveform and multi-source auxiliary information; and training a machine learning model using the random sample data of the classified type to obtain an automatic classification model of the lake satellite radar altimetry data.

[0006] According to another aspect of the embodiments of the present application, a processing device for filtering lake satellite radar altimetry data is also provided, comprising: a first obtaining unit configured to obtain random sample data from global lake surface satellite radar altimetry data; wherein the required basic data of the random sample data comprises global satellite altimetry data and lake boundary data; a processing unit configured to process the random sample data based on a preset graphical interface tool, and display echo waveform and multi-source auxiliary information corresponding to the random sample data; wherein the multi-source auxiliary information comprises multi-source footprint characteristic values and geographic location information; a classification marking unit configured to mark the random sample data according to the echo waveform and the multi-source auxiliary information; and a training unit configured to train a machine learning model using the random sample data of the marked type to obtain an automatic classification model of lake satellite radar altimetry data.

[0007] According to still another aspect of the embodiments of the present application, an electronic device is also provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-mentioned processing method for filtering lake satellite radar altimetry data by using the computer program.

[0008] According to still another aspect of the embodiments of the present application, a computer readable storage medium is also provided, wherein the computer readable storage medium stores a computer program, and the computer program is configured to execute the above-mentioned processing method for filtering lake satellite radar altimetry data when running.

[0009] In the embodiments of the present application, random sample data is obtained from global lake surface satellite radar altimetry data; wherein the required basic data of the random sample data comprises global satellite altimetry data and lake boundary data;

[0010] The random sample data is processed based on a preset graphical interface tool, and echo waveform and multi-source auxiliary information corresponding to the random sample data are displayed; wherein the multi-source auxiliary information comprises multi-source footprint characteristic values and geographic location information; the random sample data is marked according to the echo waveform and the multi-source auxiliary information; and a machine learning model is trained using the random sample data of the marked type to obtain an automatic classification model of lake satellite radar altimetry data. In the above-mentioned method, the training sample data is processed based on a preset graphical interface tool, and the training sample data is marked according to the graphical interface tool and trained by machine learning, thereby improving the sampling classification efficiency of radar echo data, and further solving the technical problem of low sampling classification efficiency of lake satellite radar altimetry data. BRIEF DESCRIPTION OF DRAWINGS

[0011] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0012] Figure 1 is a schematic diagram of an optional application environment of a processing method for filtering lake satellite radar altimetry data according to an embodiment of the application;

[0013] Figure 2 is a schematic diagram of another optional application environment of a processing method for filtering lake satellite radar altimetry data according to an embodiment of the application;

[0014] Figure 3 is a flowchart of an optional processing method for filtering lake satellite radar altimetry data according to an embodiment of the application;

[0015] Figure 4 is a flowchart of another optional processing method for filtering lake satellite radar altimetry data according to an embodiment of the application;

[0016] Figure 5 is a schematic diagram of an optional footprint feature of SARAL satellite data according to an embodiment of the application;

[0017] Figure 6 is a schematic diagram of an optional interface of radar echo sampling software according to an embodiment of the application;

[0018] Figure 7 is a schematic diagram of an optional radar echo sampling waveform of different types according to an embodiment of the application;

[0019] Figure 8 is a schematic diagram of an optional data table output by radar echo sampling software according to an embodiment of the application;

[0020] Figure 9 is a structural schematic diagram of an optional processing device for filtering lake satellite radar altimetry data according to an embodiment of the application;

[0021] Figure 10 is a structural schematic diagram of an optional electronic device according to an embodiment of the application. DETAILED DESCRIPTION

[0022] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application, so that those skilled in the art can better understand the technical solutions of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second" and the like in the description and claims of the present application and the above drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the application described herein can be implemented in other than the order illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units need not be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an aspect of the embodiments of the present application, a processing method for filtering lake satellite radar altimetry data is provided. Optionally, as an optional implementation, the above-mentioned processing method for filtering lake satellite radar altimetry data can be applied in the application environment as shown in Figure 1 The application environment includes a terminal device 102 for human-computer interaction with a user, a network 104, and a server 106. The user 108 can perform human-computer interaction with the terminal device 102, and the terminal device 102 runs a processing application program for filtering lake satellite radar altimetry data. The terminal device 102 includes a human-computer interaction screen 1022, a processor 1024, and a memory 1026. The human-computer interaction screen 1022 is used to display different types of radar footprint data; the processor 1024 is used to obtain training sample data; and the memory 1026 is used to store the training sample data.

[0025] In addition, the server 106 includes a database 1062 and a processing engine 1064, the database 1062 is used to store the training sample data described above. The processing engine 1064 is used to obtain global lake surface radar satellite footprint random sample data; wherein the basic data required by the method includes global satellite altimetry data and lake boundary data; the training sample data is processed based on a preset graphical interface tool, the echo waveform corresponding to the lake footprint sample and the multi-source auxiliary information (multi-source footprint characteristic value and geographic location information) are displayed; the sample data is labeled according to the echo waveform and the multi-source auxiliary information; the labeled sample data is used for machine learning model training, and an automatic classification model of lake satellite radar altimetry data can be obtained.

[0026] In one or more embodiments, the above-mentioned processing method for filtering lake satellite radar altimetry data can be applied to the application environment as shown. Figure 2 As shown in Figure 2 The user 202 and the user equipment 204 can perform human-computer interaction. The user equipment 204 includes a memory 206 and a processor 208. In this embodiment, the user equipment 204 can but not limited to refer to the operation performed by the terminal equipment 102 described above to obtain the classification result of the radar echo sampling data.

[0027] Optionally, the terminal equipment 102 and the user equipment 204 include but are not limited to mobile phones, tablet computers, notebook computers, PC machines, vehicle-mounted electronic devices, wearable devices and other terminals. The network 104 can include but is not limited to wireless networks or wired networks. The wireless network includes WIFI and other wireless communication networks. The wired network can include but is not limited to wide area networks, metropolitan area networks, and local area networks. The server 106 can include but is not limited to any hardware device capable of computing. The server can be a single server, a server cluster composed of multiple servers, or a cloud server. The above is only an example, and this embodiment does not make any limitation on this.

[0028] Due to the influence of land reflection signals, there is a certain difference in the quality of lake along-track footprint data. Radar footprint filtering is an important step to improve the accuracy of lake water level inversion. If the radar footprint is automatically filtered, the footprint data needs to be sampled and analyzed. However, there are mainly two kinds of existing footprint sampling methods: 1. Clustering analysis of all radar footprint data, which is time-consuming and inefficient. 2. Feature analysis of local area radar footprint data, which is only suitable for local areas and not necessarily suitable for large-scale lake footprint filtering.

[0029] In order to better serve the lake footprint screening, a large number of randomly distributed global lake footprint samples and multi-dimensional footprint feature information need to be utilized and manually labeled, and the information is used to train an automatic height measurement data classification model to serve the radar footprint automatic screening.

[0030] As an optional implementation, as shown in Figure 3 The embodiment of the present application provides a processing method for lake satellite radar height measurement data screening, including the following steps:

[0031] S302, obtaining random sample data from global lake satellite radar height measurement data; wherein, the required basic data of the random sample data includes global satellite height measurement data and lake boundary data.

[0032] In the embodiment of the present application, the radar satellite observation data includes radar satellite height measurement data, the random training sample is obtained from the radar satellite height measurement database, and the data in the radar satellite height measurement database includes but is not limited to T / P series satellite data, ERS series satellite data, Cryosat-2 data, Sentinel-3 satellite data and other height measurement data with echo waveform and quality control information.

[0033] Specifically, in an embodiment, the height measurement data of SARAL satellite is used as training sample data, the orbit height of SARAL satellite is 800km, the coverage is 81.5° in north and south latitude, SARAL carries AltiKa altimeter, which is the first Ka-band altimeter in the world. The ground track of SARAL satellite is fixed, the orbit interval at the equator is 80km, the pulse limited footprint diameter is about 1.5km, the radar ground footprint interval is 170m, and the threshold time interval is 2ns.

[0034] The height measurement data of the lake is obtained from the spatial oceanography satellite database, and the height measurement data adopts sensor geophysical data record (Sensor Geophysical Data Record, SGDR). The lake boundary vector of the global lake with an area greater than 10km 2 is obtained from the global lake boundary database HydroLAKES. The ground footprint track of the height measurement data is obtained according to the position information of the height measurement data, and then the radar footprint (i.e. the above-mentioned radar satellite observation data) falling into the lake boundary is obtained by using the lake boundary vector, and then 1000 lakes are randomly selected as the research area, and a radar satellite footprint sample is randomly selected for each lake.

[0035] The time, position, altimeter and data state of the sampling footprint data and other multi-source footprint feature information are obtained. For the above-mentioned 1000 sample data, as shown in Figure 5 a plurality of field information is extracted from the SGDR data.

[0036] Specifically, the SGDR data adopts a network general data format (NetCDF), which in the embodiments of the present application includes but is not limited to time (field "time_40hz") and position information (fields "lat_40hz" and "lon_40hz") read by the "ncread" function of the MATLAB graphical interface tool, altimeter and data state (such as the field "alt_state_flag_acq_mode_40hz", which represents the acquisition mode of the altimeter data), model fitting calculation characteristics (such as the field "width_leading_edge_40hz", which represents the rising edge width estimated in the marine heavy tracking mode), echo inherent characteristics (such as the field "agc_40hz", which represents the automatic gain control coefficient AGC of the echo energy), and calculation of echo partial shape characteristics (such as Figure 5 the maximum value, the minimum value, the average value, etc. shown in FIG. 2), elevation deviation diff (the deviation between the elevation corresponding to the preset tracking gate and the lake surface elevation, where the lake surface elevation comes from the global digital elevation model SRTM30), and the like.

[0037] S304, processing the random sample data based on a preset graphical interface tool, and displaying the echo waveform and multi-source auxiliary information corresponding to the random sample data; wherein the multi-source auxiliary information includes multi-source footprint characteristic values and geographic position information.

[0038] Specifically, the above-mentioned preset graphical interface tool includes but is not limited to a sampling software designed by MATLAB, which configures a graphical user interface (Graphical User Interface, GUI), as shown in FIG. 3. Figure 6 The software realizes functions such as sampling data import and reading, echo data window display, multi-source footprint characteristic display, echo type manual judgment selection, and sampling data export. These functions are mainly realized based on the button response "pushbutton_callback" function, the file interaction operation "uigetfile" function, the edit box response "edit_callback" function, the radio box response "uibuttongroup_SelectionChangedFcn" function, and the like. The design and operation steps of the software are as follows:

[0039] 1. Import the original satellite footprint sample file through the "Data Selection" button. By clicking the button, the button response is triggered, the file interaction operation function is called, the file selection window is popped up, the path of the file to be imported is specified, and the file is imported.

[0040] 2. The "View Data and Type Selection" button displays echo data and multi-source feature information. Clicking this button retrieves echo data and multi-source footprint feature data. The complete echo is displayed in the echo display window, with the horizontal axis converted to lake surface elevation and the vertical axis representing the echo energy value. Additionally, two lines are marked on the echo for reference: the horizontal line corresponds to the reference energy value, and the vertical line corresponds to the preset tracking gate position. Furthermore, the window displays information such as time, automatic gain control (AGC), echo backscattering coefficient (sig0), and elevation deviation (diff).

[0041] 3. Click the "View Location" button to view the footprint location information. This triggers the button's response, calling the "kmlwrite" function to link with Google Earth and directly display the sample's geographical location. This helps determine whether the lake footprints are completely submerged in water, among other things.

[0042] 4. Manually mark the echo type using the "button group". Select the echo type by combining the echo shape and multi-source auxiliary information (multi-source footprint feature values ​​and footprint position information).

[0043] 5. Click "View Data and Type Judgment" again to start reading the next footprint data, and repeat until the last footprint data.

[0044] 6. Click the "Export Data" button to export all radar footprint data with type tags.

[0045] S306, The random sample data is categorized based on the echo waveform and multi-source auxiliary information.

[0046] Specifically, radar echo sampling data can be decomposed to obtain multi-source footprint characteristic values ​​such as the number of wavelets and the optimal wavelet position. This information is displayed in the MATLAB sampling window to assist in determining the echo type. Combining echo shape, multi-source footprint characteristics (such as time, automatic gain control coefficient AGC, backscattering coefficient sig0, wavelet information, and the deviation between the preset tracking gate and the lake surface elevation), and Google Earth footprint location information, the echo type is determined. Figure 7 As shown, echoes are classified into the following four types:

[0047] 1. A more ideal echo, which is often located in the center of the lake, where the lake area is large or there are few strong reflective objects around it. This type of echo has a relatively regular shape and often has only one wavelet.

[0048] 2. The rising edge of the echo is not contaminated, so the echo is similar to the first type of echo, but it is contaminated, but the contaminated part is often located at the falling edge or other positions, and has no effect on the rising edge. The rising edge of this type of echo is very complete, which has little interference on the inversion of the lake water level.

[0049] 3. The rising edge of the echo is slightly contaminated. This type of echo is slightly worse than the second type of echo. The lake surface reflection wavelet is easy to distinguish, but the rising edge of the lake echo is contaminated to some extent, which has some interference on the inversion of the lake water level.

[0050] 4. Severely contaminated or tracking window error echo. For severely contaminated echoes, there are often multiple wavelets, the automatic gain control coefficient AGC value is low, and the backscattering coefficient sig0 value is low. It is difficult to find the lake surface echo from the echo. For tracking window error echo, the elevation deviation diff of this type of echo is generally dozens of meters or even hundreds of meters. This is often caused by window error of the tracker. This type of data is useless data.

[0051] S308, using the labeled type of random sample data to train a machine learning model to obtain an automatic classification model of lake satellite radar altimetry data.

[0052] The random sample data is processed based on a preset graphical interface tool, and the random sample data corresponding echo waveform and multi-source auxiliary information are displayed. The multi-source auxiliary information includes multi-source footprint characteristic value and geographic location information. The random sample data is labeled according to the echo waveform and multi-source auxiliary information. The method for obtaining an automatic classification model of lake satellite radar altimetry data by using the labeled type of random sample data to train a machine learning model. In the above method, since the sample data is processed, displayed and labeled by using the preset graphical interface tool, and then the labeled sample data is used to train a machine learning model, the sampling and classification efficiency of the radar altimetry data is improved, thereby solving the technical problem of low sampling and classification efficiency of lake satellite radar altimetry data.

[0053] In one or more embodiments, according to the echo waveform and multi-source auxiliary information, the random sample data is labeled according to the echo waveform and multi-source auxiliary information.

[0054] According to the above graphical interface tool, the echo shape and multi-source auxiliary information are obtained.

[0055] According to the echo waveform and multi-source auxiliary information, the random sample data is labeled according to the echo waveform and multi-source auxiliary information.

[0056] Specifically, as Figure 6As shown, through the MATLAB graphical interface tool, the echo waveform shape can be obtained, and the multi-source auxiliary information includes but is not limited to multi-source footprint characteristic information such as date, automatic gain control coefficient AGC, backscatter coefficient sig0, elevation deviation diff and the like.

[0057] In one or more embodiments, the above-mentioned classifying the random sample data according to the echo waveform and the multi-source auxiliary information comprises:

[0058] Based on the echo shape and the multi-source auxiliary information, the random sample data is divided into multiple radar echo types.

[0059] Specifically, the first type of echo (such as Figure 7 Type 1 shown in the figure) is the echo of the target type, and such echo is often located in the center of the lake, the lake area is large or there are few strong reflecting objects around, the echo shape is relatively regular, and there is usually only one subwave, and the waveform of the echo rising front has no change.

[0060] The second type of echo (such as Figure 7 Type 2 shown in the figure) is the echo with the rising front not being polluted, such echo is similar to the first type of echo, but is polluted to a certain extent, but the polluted part is often located at the falling edge or other positions and does not affect the rising edge, such echo has a very complete rising edge, which has very little interference to the inversion of the lake water level, but includes at least two subwaves.

[0061] The third type of echo (such as Figure 7 Type 3 shown in the figure) is the echo with the rising front slightly polluted, such echo is slightly worse than the second type of echo, the echo is polluted to a certain extent, but the lake surface reflection subwave can be easily distinguished, but the lake echo rising edge is polluted to a certain extent, which has certain interference to the inversion of the lake water level.

[0062] The fourth type of echo is the echo with severe pollution or tracking window error (such as Figure 4 Type 4 shown in the figure). For the echo with severe pollution, there are usually multiple subwaves, the automatic gain control coefficient AGC value is low, the backscatter coefficient sig0 value is low, and it is difficult to find the lake surface echo from the echo. For the echo with tracking window error, it can often be judged according to the deviation information of the preset tracking gate and the lake elevation, and the elevation deviation of such echo is often several tens of meters or even hundreds of meters, which is often caused by the window error of the tracker. The data of this type is useless data.

[0063] In one or more embodiments, the above-mentioned classifying the random sample data according to the echo waveform and the multi-source auxiliary information comprises:

[0064] According to the echo shape (mainly through the pollution condition of the optimal rising edge) and multi-source auxiliary information (multi-source footprint characteristic value and geographical position information), the random sample data is divided into multiple radar echo types.

[0065] In one or more embodiments, the preset graphical interface tool is used to process the random sample data, and the echo waveform and multi-source auxiliary information corresponding to the random sample data are displayed.

[0066] A two-dimensional coordinate system with lake surface elevation as the horizontal coordinate and echo energy as the vertical coordinate is constructed based on the MATLAB graphical interface tool.

[0067] The echo waveform corresponding to each lake and the multi-source footprint characteristic value are displayed in the MATLAB graphical interface tool.

[0068] In one or more embodiments, the processing method for filtering the lake satellite radar altimetry data further includes:

[0069] Based on the MATLAB graphical interface tool, the geographical position information corresponding to each echo is determined by linking the satellite map tool.

[0070] In the embodiments of the present application, the MATLAB tool is used to call the "kmlwrite" function to realize linkage with Google Earth, and the geographical position of the sample lake is directly displayed to assist in judging whether the radar footprint corresponding to the lake falls completely into the water or not.

[0071] In one or more embodiments, after the machine learning model is trained using the random sample data of the labeled type, an automatic classification model of the radar satellite observation data is obtained, and the method further includes:

[0072] The classification model is used to classify the radar satellite observation data to be classified, and the corresponding radar echo type is obtained.

[0073] According to the target lake water level inversion accuracy requirement, the radar echo data of the target type is selected.

[0074] The trained classification model provided in the embodiments of the present application can classify the radar echo data to be classified. Different quality types of radar echo data can be obtained flexibly and conveniently according to needs.

[0075] Based on the above embodiments, in an application embodiment, as shown in Figure 5 The processing method for filtering the lake satellite radar altimetry data further includes:

[0076] Step 1: Prepare satellite altimetry data and lake boundary vector, randomly select radar footprint data from different lakes. Download global data of radar altimeter in a certain observation period and global lake vector data, obtain all footprint position information of altimetry data, obtain radar footprint data falling into the lake, and randomly select a certain number of footprint data, each radar footprint data corresponding to different lakes.

[0077] Step 2: Obtain time, position, tracker attribute and other multi-source footprint characteristic information of footprint data. For the selected sample data, as shown in Figure 5 , extract part of time position information, tracker attribute characteristics, model fitting calculation characteristics, echo inherent characteristics from the original satellite data, and calculate the shape characteristics, height deviation and other information of the echo.

[0078] Step 3: Design footprint sampling software to assist the automatic labeling of echo type. The sampling software integrates data import, sample type selection, data display, data export and other functions. In addition, the software can be linked with Google Earth software to display the position information of the echo. According to the position relationship between the echo and the lake, it can assist in judging whether the echo waveform is polluted or not.

[0079] Step 4: Manually label echo type. For the echo data to be labeled, it needs to be decomposed to obtain the number of subwaves of the echo, the optimal subwave and other multi-source footprint characteristic information. According to the echo shape, multi-source footprint characteristics, geographical position and other information, the echo type is judged. The echo is divided into four categories: 1. relatively ideal target type echo; 2. echo with un-polluted rising edge; 3. echo with slightly polluted rising edge; 4. echo with serious pollution or tracking window error.

[0080] Further, the satellite altimetry data can be traditional large footprint radar altimeter data, such as T / P series satellite data, ERS series satellite data, Cryosat-2 data, Sentinel-3 satellite data and other altimetry data with echo waveform and quality control information.

[0081] Specifically, the processing method of the above lake satellite radar altimetry data screening further includes the following steps:

[0082] Taking the altimetry data of S ARAL satellite as an example, the orbit height of S ARAL satellite is 800 km, covering the area of 81.5° north and south latitude, and S ARAL carries AltiKa altimeter, which is the first Ka-band altimeter in the world. The ground track of S ARAL satellite is fixed, the orbit interval at the equator is 80 km, the pulse limited footprint diameter is about 1.5 km, the radar ground footprint interval is 170 m, and the threshold time interval is 2 ns.

[0083] Step one: Prepare the altimetry data and lake boundary data. Download the data from the Satellite Oceanography Center, which uses Sensor Geophysical Data Record (SGDR). Obtain the global lake boundary vector from the global lake boundary database HydroLAKES, which has a global area of more than 10 km 2 . According to the position information of the altimetry data, obtain the ground footprint track of the altimetry data, and then use the lake boundary vector to obtain the footprint falling within the lake boundary, and then randomly select 1000 lakes and randomly select a footprint point for each lake.

[0084] Step two: Obtain the time, position, altimeter, and data state of the sampling footprint data, and other multi-source footprint feature information. For the above 1000 sample data, extract the field information as shown in Figure 2 from the SGDR data.

[0085] The SGDR data uses the network universal data format (NetCDF), and uses the MATLAB "ncread" function to read out the time (field "time_40hz") and position information (fields "lat_40hz" and "lon_40hz"), altimeter and data state (such as the field "alt_state_flag_acq_mode_40hz", which represents the acquisition mode of the altimeter data), model fitting calculation features (such as the field "width_leading_edge_40hz", which represents the rising edge width estimated in the ocean retrace mode), echo inherent characteristics (such as the field "agc_40hz", which represents the automatic gain control coefficient of the echo energy), and calculate the echo part shape features (such as the maximum, minimum, average, etc. listed in Figure 3 ), elevation deviation (preset tracking gate, i.e. the deviation between the elevation of the 52nd sampling tracking point and the lake surface elevation), and other information.

[0086] Step three: Design a footprint sampling software for echo type labeling. Use MATLAB to design a sampling software (such as Figure 7 ), design a graphical user interface (GUI), which realizes the functions of sampling data import and reading, echo data window display, multi-source footprint feature display, echo type manual judgment selection, and sampling data export. These functions are mainly based on button response ("pushbutton_callback"), file interaction operation ("uigetfile"), edit box response ("edit_callback"), single selection box response ("uibuttongroup_SelectionChangedFcn"), etc. The design and operation steps of the software are as follows:

[0087] 1. Import the original satellite footprint sample file through the "Data Selection" button. By clicking the button, trigger the button response, call the file interaction function, pop up the file selection window, specify the path of the file to be imported, and import the file.

[0088] 2. Realize echo display and multi-source footprint feature information display through the "View Data and Type Selection" button. By clicking the button, read the echo data and multi-source footprint feature value. Display the complete echo in the echo display window, convert the horizontal coordinate into lake elevation, and the vertical coordinate into echo energy value. In addition, mark two lines on the echo for reference, the horizontal line corresponds to the reference energy value, and the vertical line corresponds to the preset tracking gate position. In addition, display the time, automatic gain coefficient (AGC), echo backscatter coefficient (sig0), elevation deviation, etc. (from the above step two) in the window.

[0089] 3. Click the "View Location" button to view the footprint location information. Trigger the button response, call the "kmlwrite" function to realize the linkage with Google Earth, and directly display the geographical location of the sample, which is used to assist in judging whether the lake footprint falls completely into the water or not.

[0090] 4. Manually mark the echo type through the "button group". Integrate the echo shape, multi-source footprint feature information, echo footprint location environment information, etc. to select the type of echo.

[0091] 5. Click "View Data and Type Selection" again to start reading the next footprint data, and cycle to the last footprint data.

[0092] 6. Click the "Export Data" button to export all the footprint data with type labels.

[0093] Step four: Determine the type of echo. For sampled echo data, decompose the echo to obtain the number of subwaves, optimal subwave position, and other multi-source footprint feature information. Display these information in the sampling window to assist in determining the type of echo. Combine the echo shape and multi-source auxiliary information to determine the type of echo. The multi-source auxiliary information includes multi-source footprint features and echo geographical location information, including time, automatic gain control coefficient AGC, backscatter coefficient sig0, subwave information, and preset tracking gate and lake elevation deviation. The type of echo is divided into four types (such as Figure 8As shown in the figure): 1. The echo is more ideal, such echoes are often located in the center of the lake, the lake area is larger or there are fewer surrounding strong reflection objects, the shape of such echoes is relatively regular, and there is usually only one sub-wave; 2. The rising edge of the echo is not polluted, such echoes are similar to the first type of echo, but are polluted to a certain extent, but the polluted part is often located at the falling edge or other positions, and has no effect on the rising edge, the rising edge of such echoes is very complete, which has little interference on the inversion of the lake water level; 3. The rising edge of the echo is slightly polluted, such echoes are slightly worse than the second type of echo, the echo is polluted to a certain extent, but the lake surface reflection sub-wave can be easily distinguished, but the rising edge of the lake echo is polluted to a certain extent, which has a certain interference on the inversion of the lake water level. 4. Severely polluted or tracking window error echo. For severely polluted echoes, there are usually multiple sub-waves, the automatic gain control coefficient AGC value is low, and the backscattering coefficient sig0 value is low, it is difficult to find the lake surface echo from the echo. For the echo of the tracking window error, the deviation of the preset tracking gate and the lake surface elevation can be used to judge, the elevation deviation of such echo is often several tens of meters or hundreds of meters, which is often caused by the window error of the tracker. Such type of data is often useless data.

[0094] Finally, the sampling data as shown in the figure is derived. Figure 9 Using the labeled sample data, the machine learning model training is carried out, and the trained machine learning model can be used for automatic classification and screening of echoes.

[0095] The embodiment of the present application also has the following beneficial effects:

[0096] 1. The embodiment of the present application randomly samples the lake surface echo sample in the global scope, and the echo characteristics obtained are more suitable for global lake footprint screening.

[0097] 2. The embodiment of the present application acquires more footprint characteristics, and determines the echo category with the help of the echo position information of Google Earth, which is more conducive to accurate determination of the lake type;

[0098] 3. The radar footprint sampling software designed by the embodiment of the present application not only has simple operation, but also can comprehensively utilize multi-source information to assist in echo type determination, greatly improving the efficiency and accuracy of radar footprint sampling.

[0099] 4. The embodiment of the present application determines the echo type by designing the echo type determination rule, which is more conducive to determining echoes of different pollution degrees, and can improve the utilization rate of polluted echoes.

[0100] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all described as a series of action combinations, but those skilled in the art should know that the present application is not limited by the action sequence described, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily necessary for the present application.

[0101] According to another aspect of the embodiments of the present application, a lake satellite radar altimetry data screening processing device for implementing the lake satellite radar altimetry data screening processing method is also provided. As shown in the figure, the device comprises: Figure 10

[0102] The first acquisition unit 902 is configured to acquire random sample data from global lake surface satellite radar altimetry data, wherein the required basic data of the random sample data includes global satellite altimetry data and lake boundary data.

[0103] The processing unit 904 is configured to process the random sample data based on a preset graphical interface tool, and display the echo waveform and multi-source auxiliary information corresponding to the random sample data, wherein the multi-source auxiliary information includes multi-source footprint characteristic values and geographic location information.

[0104] The classification marking unit 906 is configured to mark the random sample data according to the echo waveform and multi-source auxiliary information.

[0105] The training unit 908 is configured to train a machine learning model using the random sample data of the marked type to obtain an automatic classification model of lake satellite radar altimetry data.

[0106] The method of processing the random sample data based on a preset graphical interface tool, displaying the echo waveform and multi-source auxiliary information corresponding to the random sample data, marking the random sample data according to the echo waveform and multi-source auxiliary information, and training a machine learning model using the random sample data of the marked type to obtain an automatic classification model of lake satellite radar altimetry data, in the above method, the training sample data is processed, displayed, and marked by classification using a preset graphical interface tool, and then a machine learning model is trained, which improves the sampling and classification efficiency of radar echo data, and further solves the technical problem of low sampling and classification efficiency of lake satellite radar altimetry data.

[0107] In one or more embodiments, the classification marking unit 906 comprises:

[0108] ​The dividing module is configured to label the random sample data according to the echo waveform and multi-source auxiliary information.

[0109] In one or more embodiments, the dividing module comprises:

[0110] The dividing sub-unit is configured to divide the random sample data into multiple radar echo types according to the echo shape, multi-source footprint features (time, automatic gain control coefficient AGC, backscattering coefficient sig0, wavelet information, deviation of preset tracking gate and lake surface elevation, etc.) and footprint geographical location information.

[0111] In one or more embodiments, the processing unit 904 comprises:

[0112] The constructing module is configured to construct a two-dimensional coordinate system with the lake surface elevation as the horizontal coordinate and the echo energy as the vertical coordinate based on a MATLAB graphical interface tool.

[0113] The display module is configured to display the echo waveform corresponding to each lake and the multi-source footprint feature values in the MATLAB graphical interface tool.

[0114] In one or more embodiments, the processing device for filtering the lake satellite radar altimetry data further comprises:

[0115] The determining unit is configured to determine the geographical location information corresponding to each echo based on the MATLAB graphical interface tool and a satellite map tool.

[0116] In one or more embodiments, the processing device for filtering the lake satellite radar altimetry data further comprises:

[0117] The classifying unit is configured to classify the radar satellite observation data to be classified using the trained classification model to obtain the corresponding radar echo type.

[0118] The filtering unit is configured to filter out the radar echo data of the target type according to the target lake water level inversion accuracy requirement.

[0119] According to another aspect of the embodiments of the present application, an electronic device for implementing the above-mentioned processing method for filtering the lake satellite radar altimetry data is also provided, which can be a terminal device or a server as shown in the drawings. Figure 10 As shown in the drawings, the electronic device comprises a memory 1002 and a processor 1004, the memory 1002 stores a computer program, and the processor 1004 is configured to execute the steps in any one of the method embodiments by the computer program. Figure 10

[0120] ​Optionally, in the embodiment, the electronic device can be located in at least one of the plurality of network devices of the computer network.

[0121] Optionally, in the embodiment, the processor can be configured to perform the following steps by means of a computer program:

[0122] S1. obtaining random sample data from satellite radar altimetry data of global lake surfaces; wherein the required basic data of the random sample data comprises global satellite altimetry data and lake boundary data;

[0123] S2. processing the random sample data based on a preset graphical interface tool, and displaying echo waveform corresponding to the random sample data and multi-source auxiliary information; wherein the multi-source auxiliary information comprises multi-source footprint features and geographic location information;

[0124] S3. classifying the random sample data according to the echo waveform and the multi-source auxiliary information;

[0125] S4. training a machine learning model using the random sample data of the labeled type to obtain an automatic classification model of lake satellite radar altimetry data.

[0126] Optionally, those skilled in the art can understand that, Figure 10 The structure shown is only schematic, and the electronic device can also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, or the like. Figure 10 It does not limit the structure of the electronic device. For example, the electronic device can further include more or less components (such as a network interface, etc.) than Figure 10 shown, or have a different configuration from ​ shown.

[0127] The memory 1002 can be used to store software programs and modules, such as the program instructions / modules corresponding to the processing method and apparatus for lake satellite radar altimetry data filtering in this embodiment of the invention. The processor 1004 executes various functional applications and data processing by running the software programs and modules stored in the memory 1002, thereby realizing the aforementioned processing method for lake satellite radar altimetry data filtering. The memory 1002 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 1002 may further include memory remotely located relative to the processor 1004, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, the memory 1002 may be used, but is not limited to, to store information such as radar echo sampling data classification models. As an example, such as ​ As shown, the memory 1002 may include, but is not limited to, the first acquisition unit 902, processing unit 904, classification and labeling unit 906, and training unit 908 in the above-mentioned lake satellite radar altimetry data filtering processing device. Furthermore, it may also include, but is not limited to, other module units in the above-mentioned lake satellite radar altimetry data filtering processing device, which will not be described in detail in this example.

[0128] Optionally, the transmission device 1006 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 1006 includes a Network Interface Controller (NIC), which can be connected to other network devices and routers via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 1006 is a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0129] In addition, the aforementioned electronic device also includes: a display 1008 for displaying the classification type of the radar echo sampling data; and a connection bus 1010 for connecting the various module components in the aforementioned electronic device.

[0130] In other embodiments, the terminal device or the server described above can be a node in a distributed system, where the distributed system can be a blockchain system, which can be a distributed system formed by the plurality of nodes connected through network communication. Among them, the nodes can form a peer-to-peer (P2P) network, and any form of computing device, such as a server, a terminal, and the like, can become a node in the blockchain system by joining the peer-to-peer network.

[0131] According to an aspect of the present application, a computer program product or computer program is provided, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the processing method for filtering satellite radar altimetry data of a lake described above, where the computer program is configured to execute the steps in any of the method embodiments described above when running.

[0132] Optionally, in the present embodiment, the computer readable storage medium described above can be configured to store a computer program for executing the following steps:

[0133] S1, obtaining random sample data from satellite radar altimetry data of a global lake surface; wherein the required basic data of the random sample data includes global satellite altimetry data and lake boundary data;

[0134] S2, processing the random sample data based on a pre-set graphical interface tool, and displaying echo waveform and multi-source auxiliary information corresponding to the random sample data; wherein the multi-source auxiliary information includes multi-source footprint characteristic values and geographic location information;

[0135] S3, classifying the random sample data according to the echo waveform and the multi-source auxiliary information;

[0136] S4, training a machine learning model using the random sample data of the labeled type to obtain an automatic classification model of satellite radar altimetry data of a lake.

[0137] Optionally, in the present embodiment, a person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, which can be stored in a computer readable storage medium, and the storage medium can include a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0138] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0139] The integrated units in the above-mentioned embodiments, if realized in the form of software functional units and sold or used as independent products, can be stored in the above-mentioned computer-readable storage medium. Based on such understanding, the technical solutions of the present application or the whole or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing one or more computer devices (which can be personal computers, servers or network devices, etc.) to execute all or part of the steps of the embodiments of the present application.

[0140] In the above-mentioned embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0141] In the several embodiments provided in the present application, it should be understood that the disclosed client can be implemented by other means. Among them, the above-mentioned device embodiment is only schematic, for example, the division of units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be through some interface, indirect coupling or communication connection between units or modules, which can be electrical or other forms.

[0142] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment of the present application according to actual needs.

[0143] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be realized in the form of hardware or software functional unit.

[0144] The above is only the preferred embodiment of the present application, and it should be noted that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which improvements and refinements should be considered as the protection scope of the present application.

Claims

1. A method for filtering satellite radar altimetry data of lakes, characterized in that, include: Random sample data is obtained from satellite radar altimetry data of lake surfaces worldwide. This random sample data is obtained as follows: multiple lakes are randomly selected from a global lake boundary database, and a radar footprint sample is randomly selected from each lake. The random sample data is processed using a pre-defined graphical interface tool, and the echo waveforms and multi-source auxiliary information corresponding to the random sample data are displayed. This includes: extracting the echo waveforms and multi-source auxiliary information from the random sample data, and calculating the shape characteristics and elevation deviation of the echoes; constructing a two-dimensional coordinate system with lake surface elevation as the abscissa and echo energy as the ordinate using a MATLAB graphical interface tool; displaying the echo waveforms and multi-source footprint feature values ​​corresponding to each lake in the MATLAB graphical interface tool; wherein the multi-source auxiliary information includes multi-source footprint feature values ​​and geographical location information; the multi-source footprint feature values ​​include time information, tracker attribute features, model fitting calculation features, and inherent echo features. Based on the MATLAB graphical interface tool linked with the satellite map tool, the geographical location information corresponding to each echo is determined in order to determine whether the radar footprint corresponding to the lake has completely fallen into the water. The random sample data is categorized based on the echo waveform and multi-source auxiliary information, including: classifying the random sample data into multiple radar echo types based on the echo waveform and multi-source auxiliary information. The radar echo types include: echoes of the first type whose echo shape conforms to the target rules and contains only one wavelet; echoes of the second type whose rising edge is uncontaminated; echoes of the third type whose rising edge is partially contaminated; and echoes of the fourth type whose data is unavailable. By training a machine learning model using labeled random sample data, an automatic classification model for lake satellite radar altimetry data is obtained.

2. The method according to claim 1, characterized in that, After training a machine learning model using labeled random sample data to obtain an automatic classification model for lake satellite radar altimetry data, the method further includes: The trained classification model is used to classify the radar satellite observation data to be classified, and the corresponding radar echo type is obtained. Based on the accuracy requirements of the target lake's water level inversion, radar echo data of the target type are selected.

3. A processing device for filtering satellite radar altimetry data of lakes, characterized in that, include: The first acquisition unit is used to acquire random sample data from satellite radar altimetry data of global lake surfaces; wherein, the random sample data is acquired in the following manner: multiple lakes are randomly selected from the global lake boundary database, and a radar footprint sample is randomly selected from each lake; The processing unit is used to process the random sample data based on a preset graphical interface tool, and display the echo waveform and multi-source auxiliary information corresponding to the random sample data. This includes: extracting the echo waveform and multi-source auxiliary information from the random sample data, and calculating the shape characteristics and elevation deviation of the echo; constructing a two-dimensional coordinate system based on the MATLAB graphical interface tool with lake surface elevation as the abscissa and echo energy as the ordinate; displaying the echo waveform and multi-source footprint feature values ​​corresponding to each lake in the MATLAB graphical interface tool; wherein the multi-source auxiliary information includes multi-source footprint feature values ​​and geographical location information; the multi-source footprint feature values ​​include time information, tracker attribute features, model fitting calculation features, and inherent echo features; Based on the MATLAB graphical interface tool linked with the satellite map tool, the geographical location information corresponding to each echo is determined in order to determine whether the radar footprint corresponding to the lake has completely fallen into the water. The classification and labeling unit is used to classify the random sample data according to the echo waveform and multi-source auxiliary information, including: classifying the random sample data into multiple radar echo types according to the echo waveform and multi-source auxiliary information, wherein the radar echo types include: a first type of echo whose echo shape conforms to the target rule and contains only one subwavelength; a second type of echo whose rising edge is uncontaminated; a third type of echo whose rising edge is partially contaminated; and a fourth type of echo whose data is unusable. The training unit is used to train a machine learning model using labeled random sample data to obtain an automatic classification model for lake satellite radar altimetry data.

4. An electronic device, comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 2 through the computer program.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when executed, performs the method described in any one of claims 1 to 2.

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