Target Imaging Time Calculation Method Based on Satellite Sliding Focusing Mode

Abstract

This invention discloses a method for calculating target imaging time based on satellite sliding spotting mode, relating to the field of signal processing technology. The method includes: acquiring a ground target area; rasterizing the ground target area; encoding each unit raster to obtain a raster code; acquiring target area parameter information corresponding to each unit raster; constructing a mapping relationship between the raster code and the target area parameter information; converting the target area parameter information into the geocentric latitude of the target area based on the mapping relationship; acquiring the satellite's imaging viewing angle or pitch attitude angle based on the geocentric latitude and raster code; extracting the six roots of the satellite's orbit and performing orbit extrapolation calculations to obtain the time T1 from the satellite's orbit extrapolation to the imaging viewing angle; and optimizing and denoising the time T1 to obtain the final target imaging time. This application can quickly obtain the access time window for remote sensing satellites to image ground target areas.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of signal processing, and particularly relates to a target imaging time calculation method based on a satellite sliding spotlight mode. BACKGROUND

[0002] With the continuous development of remote sensing satellite technology, remote sensing satellite technology plays an increasingly important role in application fields such as disaster monitoring, resource investigation, surveying and mapping, agricultural yield estimation, and environmental protection.

[0003] Among them, the access time window in which the satellite can take images of the target area on the ground, that is, the start time to the end time in which the satellite can take images of the target area on the ground, is an important parameter in the field of remote sensing satellite imaging technology, which is used to represent which time period the satellite can image the target area on the ground. However, in the prior art, when observing the target area, there are problems such as low acquisition efficiency, low data utilization efficiency, and poor imaging processing speed.

[0004] Therefore, it is urgent to improve the defects in the prior art. SUMMARY

[0005] In order to solve the above problems in the prior art, the application provides a target imaging time calculation method based on a satellite sliding spotlight mode. The technical problem to be solved by the application is solved by the following technical scheme:

[0006] In a first aspect, the application provides a target imaging time calculation method based on a satellite sliding spotlight mode, comprising:

[0007] acquiring a target area on the ground, rasterizing the target area on the ground, performing code assignment processing on a unit grid to obtain a grid code;

[0008] acquiring target area parameter information corresponding to the unit grid, and constructing a mapping relationship between the grid code and the target area parameter information; wherein the target area parameter information includes a position vector, a velocity vector, a distance from an imaging point to the center of the earth, and a distance from the satellite to the center of the earth.

[0009] Based on the mapping relationship between the grid code and the target area parameter information, the target area parameter information is converted into a target area center latitude;

[0010] Based on the target area center latitude and the grid code, an imaging down-view angle or a pitch attitude angle of the satellite is obtained; six numbers of a satellite orbit are extracted, and orbit extrapolation calculation is performed to obtain a time T1 at which the satellite orbit is extrapolated to the imaging down-view angle.

[0011] The time T1 is optimized and denoised to obtain a final target imaging time.

[0012] Optionally, the process of assigning codes to unit graticles and obtaining graticle codes includes:

[0013] Build a standard coding library and store it in a backend database or cloud storage space;

[0014] Extract the content contained in the meaning format corresponding to the unit raster, and compare the content with the standard encoding library. If the comparison is successful, directly assign the specific encoding from the standard encoding library.

[0015] Optionally, the construction of the standard assignment library includes:

[0016] Obtain the original encoding library model;

[0017] Obtain preference information, and based on the original coding library model and preference information, obtain one or more layers included in the original coding library;

[0018] A standard coding library is generated based on the layers and the original coding library model.

[0019] Optionally, the process of extracting the six roots of the satellite's orbit and performing orbit extrapolation calculations includes:

[0020] Based on the six elements of the satellite's orbit, orbit extrapolation calculations are performed using a preset model; the preset model is either TwoBody, J2, J4, or HPOP.

[0021] Optionally, the six fundamental parameters of the satellite's orbit include the semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly.

[0022] Optionally, the satellite's imaging viewpoint or pitch attitude angle is obtained based on the satellite's actual imaging range.

[0023] Optionally, it also includes:

[0024] The interval between the trajectories of adjacent sub-satellite points at the equator is expressed as follows:

[0025] a = T0(ST);

[0026] Where a is the time interval between adjacent sub-satellite orbits at the equator, T0 is the satellite orbital period, S is the Earth's rotation speed, and T is the average rate of orbital nodal precession.

[0027] The imaging period T is obtained by summing the orbital periods of multiple satellites. n And the imaging period is segmented.

[0028] Optionally, it also includes: time T1 and imaging period T n Optimize noise reduction;

[0029] Based on time T1 and imaging period T n Construct a three-dimensional voxel grid;

[0030] Within each voxel grid, statistical analysis is performed on time points to obtain the center time point;

[0031] The center time point within each voxel grid is sampled and processed to obtain the accurate target imaging time.

[0032] The beneficial effects of this invention are:

[0033] This invention provides a method for calculating target imaging time based on satellite sliding spotting mode. First, the ground target area is acquired, rasterized, and each unit raster is coded. This coding process improves system smoothness, saves resources and memory, speeds up satellite and server processing, and facilitates subsequent data retrieval and transmission. Second, a mapping relationship is established between the raster code and target area parameter information, which facilitates target area transcoding, reduces the overall system load, and improves system smoothness. Third, based on the mapping relationship between the raster code and target area parameter information, the target area parameter information is converted into the geocentric latitude of the target area. Finally, based on... Based on the geocentric latitude and raster code of the target area, the imaging down-view angle or pitch attitude angle of the satellite is obtained; the six roots of the satellite's orbit are extracted and extrapolated to obtain the time from the extrapolation of the satellite's orbit to the imaging down-view angle; finally, the imaging down-view angle time is optimized and noise-reduced to obtain the final target imaging time. In this way, the access time window for remote sensing satellites to image ground target areas can be quickly obtained, thereby improving the efficiency of obtaining the access time window for remote sensing satellites to image ground target areas. It also enables remote sensing satellite imaging technology to have a rapid response capability to emergencies, and provides rapid and stable support for the preparation of satellite imaging planning and imaging shooting plans.

[0034] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0035] Figure 1 This is a flowchart of a target imaging time calculation method based on satellite sliding spotting mode provided in an embodiment of the present invention. Detailed Implementation

[0036] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0037] The sliding spotlight mode is one of the operating modes of spaceborne synthetic aperture radar (SAR). By controlling the antenna beam scanning speed, the sliding speed of the antenna beam footprint on the ground is reduced, increasing the synthetic aperture time of the imaging point and achieving high-resolution imaging. The azimuth imaging range of the sliding spotlight mode is larger than that of the traditional spotlight mode, but the azimuth resolution is lower. The sliding spotlight mode can achieve a trade-off between resolution and azimuth imaging range by controlling the beam scanning speed. In the sliding spotlight mode, because the antenna illumination area moves on the ground, the target within the imaging area is weighted by the complete antenna pattern, which can eliminate azimuth fluctuations in system performance indicators, allowing the sliding spotlight to obtain a uniform azimuth image like the strip mode.

[0038] Please see Figure 1 , Figure 1 This is a flowchart of a target imaging time calculation method based on satellite sliding spotting mode provided by an embodiment of the present invention. The method for calculating target imaging time based on satellite sliding spotting mode provided in this application includes:

[0039] S101. Obtain the ground target area, rasterize the ground target area, and assign codes to the unit raster to obtain the raster code;

[0040] S102. Obtain the target area parameter information corresponding to the unit grid, and construct the mapping relationship between the grid code and the target area parameter information; wherein, the target area parameter information includes the position vector, velocity vector, distance from the imaging point to the Earth's center, and distance from the satellite to the Earth's center of the target area;

[0041] S103. Based on the mapping relationship between raster code and target area parameter information, convert the target area parameter information into the geocentric latitude of the target area;

[0042] S104. Based on the geocentric latitude and raster code of the target area, obtain the satellite's imaging viewpoint or pitch attitude angle; extract the six roots of the satellite's orbit and perform orbit extrapolation calculation to obtain the time T1 from the extrapolation of the satellite's orbit to the imaging viewpoint.

[0043] S105. Perform noise reduction optimization on the time T1 to obtain the final target imaging time.

[0044] For details, please continue to see Figure 1As shown in this embodiment, a method for calculating target imaging time based on satellite sliding beam mode is provided. First, the ground target area is acquired and rasterized. The ground target area can be a regular or irregular area. When it is a regular area, rasterization is simpler. When it is an irregular area, it is converted into a regular raster area for processing. Rasterization can more accurately express the parameter information of the target area. The information of the center point of each unit grid after rasterization is the parameter information of the target area. In addition, considering that the sliding beam imaging area is a very small area relative to the Earth's surface, the center point position can be used as the median value of the unit grid. Specifically, a square kilometer grid of rectangular coordinate system is established based on the CGCS2000 national geodetic coordinate system. The ground target area is rasterized, and the unit grid is coded. Through coding, the system smoothness can be improved, resources and memory can be saved, the processing speed of satellite and server can be accelerated, and data retrieval can be facilitated later. The system performs several steps: First, it transmits data. Second, it establishes a mapping relationship between raster codes and target area parameter information, which facilitates the transcoding of the target area, reduces the load on the entire system, and improves system smoothness. Third, based on the mapping relationship between raster codes and target area parameter information, it converts the target area parameter information into the geocentric latitude of the target area. Fourth, based on the geocentric latitude of the target area and the raster code, it obtains the satellite's imaging viewing angle or pitch attitude angle. It extracts the six roots of the satellite's orbit and performs orbit extrapolation calculations to obtain the time T1 from the extrapolation of the satellite's orbit to the imaging viewing angle. Finally, it optimizes and reduces noise on time T1 to obtain the final target imaging time. In this way, it can quickly obtain the access time window for remote sensing satellites to image ground target areas, thereby improving the efficiency of obtaining the access time window for remote sensing satellite imaging of ground target areas. It also enables remote sensing satellite imaging technology to have a rapid response capability to emergencies, providing rapid and stable support for the preparation of satellite imaging planning and imaging shooting plans.

[0045] It should be noted that the process of transforming an irregular ground target area into a regular ground target area includes:

[0046] The Latmax and Latmin boundaries of irregular ground target areas are obtained, and these areas are converted into multiple unit grids to facilitate the extraction of target area parameters and to transform irregular ground target areas into regular rectangular areas. The Latmax and Latmin boundaries of the rectangular areas are obtained using a GIS zoning statistical tabulation tool, with the grid code as the statistical field, and the latitude and longitude averages of each grid are calculated. Then, the average slope, population density, light density, and PM2.5 fields in the grid layer attribute table are standardized to ensure that the values ​​of each indicator are within the range of 0 to 1, thereby eliminating the influence of inconsistent dimensions of imaging influencing factors.

[0047] In one optional embodiment of this application, the process of assigning a code to a unit grid to obtain a grid code includes:

[0048] Build a standard coding library and store it in a backend database or cloud storage space;

[0049] Extract the content contained in the meaning format corresponding to the unit raster, and compare the content with the standard encoding library. If the comparison is successful, directly assign the specific encoding from the standard encoding library.

[0050] Specifically, in this embodiment, different latitudes and longitudes and terrains (including rivers, lakes, and mountains) within a unit grid are classified. For example, rivers, lakes, and mountains are represented as A, B, and C, respectively. At the same time, the latitudes and longitudes and terrains within a unit grid have different meanings in the code string. A standard coding library is constructed. Since the standard coding library occupies a large amount of memory, it is stored in a background database or cloud storage space. When coding a unit grid in the future, it is only necessary to search and compare. If the comparison is successful, the code is directly assigned.

[0051] In an optional embodiment of this application, the construction of the standard coding library includes:

[0052] Obtain the original encoding library model;

[0053] Obtain preference information, and based on the original coding library model and preference information, obtain one or more layers included in the original coding library;

[0054] A standard coding library is generated based on the layers and the original coding library model.

[0055] Specifically, without encoding, one or more strings of data representing geographical areas may be transmitted, which would consume too many resources and memory, increasing the load on satellites and servers. In contrast, the encoding method used in this embodiment facilitates data retrieval and transmission later.

[0056] In one optional embodiment of this application, the process of extracting the six roots of the satellite's orbit and performing orbit extrapolation calculations includes:

[0057] Based on the six elements of the satellite's orbit, orbit extrapolation calculations are performed using a preset model; the preset model is either TwoBody, J2, J4, or HPOP.

[0058] In one optional embodiment of this application, the six fundamental parameters of the satellite's orbit include the semi-major axis, eccentricity, orbital inclination, right ascension of the ascending node, argument of perigee, and true anomaly.

[0059] In one alternative embodiment of this application, the satellite's imaging viewing angle or pitch attitude angle is obtained based on the satellite's actual imaging range.

[0060] It should be noted that the imaging downward viewing angle or pitch attitude angle range can be preset, and a fixed range or fixed value can be set. For example, the downward viewing angle or pitch attitude angle range is -45° to +45°.

[0061] In one optional embodiment of this application, it further includes:

[0062] The interval between the trajectories of adjacent sub-satellite points at the equator is expressed as follows:

[0063] a = T0(ST);

[0064] Where a is the time interval between adjacent sub-satellite orbits at the equator, T0 is the satellite orbital period, S is the Earth's rotation speed, and T is the average rate of orbital nodal precession.

[0065] The imaging period T is obtained by summing up the orbital periods of multiple satellites. n The imaging period is then segmented.

[0066] In an optional embodiment of this application, it further includes: adjusting the time T1 and the imaging period T. n Optimize noise reduction;

[0067] Based on time T1 and imaging period T n Construct a three-dimensional voxel grid;

[0068] Within each voxel grid, statistical analysis is performed on time points to obtain the center time point;

[0069] The center time point within each voxel grid is sampled and processed to obtain the accurate target imaging time.

[0070] Specifically, in this embodiment, the imaging period T n The time T1 is optimized for noise reduction to reduce the number of data time points during processing. Voxel rasterization downsampling is used to reduce the imaging period T by leveraging the grid. n And the amount of data at the three-dimensional time point T1, through the input imaging period T n A three-dimensional voxel grid is created at time T1 (which can be imagined as a collection of tiny three-dimensional cubes). Then, within each voxel grid (i.e., a three-dimensional cube, typically with a voxel size of 100), other time points within the voxel grid are displayed using approximate time points for all time points within the voxel. Thus, all time points within a voxel grid can be represented by a centroid time point. After sampling and processing across all voxel grids, the filtered imaging period T is obtained. nTime T1, i.e., the imaging period T n Time T1 dilution and denoising are performed to obtain the accurate target imaging time. It should be noted that the denoising idea is as follows: perform a statistical analysis on the neighborhood of each periodic time point, calculate its distribution to all neighboring points, and for each point, calculate its average distance to all neighboring points (assuming that the result is a Gaussian distribution, the periodic distribution of which is determined by the mean and standard deviation). Points whose average distance is outside the standard range can be defined as outliers and removed from the data.

[0071] In this embodiment, the period interval is set to 500-800, the distance threshold is 500, the RANSAC algorithm is used to remove ground point clouds, the maximum number of iterations is set to 10000, and the farthest distance threshold is defined as 80.

[0072] Based on the probability density function formula of the average distance over the neighborhood time period:

[0073]

[0074] Set the maximum search threshold for each point to N, and the standard deviation multiple to n. Points that exceed the average distance by n times are defined as outliers and will be removed.

[0075] Among them, X i Let f(X) be the coordinates of the i-th cloud point, σ be the number of outliers, and μ be a constant, where μ can range from 0 to 0.6. i ) represents the probability density value of the average distance over a time period in the neighborhood; for example, when μ is 0.6 and σ is 40, it calculates the average distance between each point in the point cloud and its 40 neighboring points; when this value is greater than 0.6 times the standard deviation, the point is identified as an outlier, otherwise the point is deleted.

[0076] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for calculating target imaging time in a sliding spotlight mode based on a satellite, characterized in that, The method comprises the following steps: acquiring a ground target area, rasterizing the ground target area, and performing code assignment on a unit grid to obtain a grid code; the method comprises the following steps: constructing a standard code assignment library and storing the standard code assignment library in a background database or a cloud storage space; extracting content contained in a meaning format corresponding to the unit grid and comparing the content with the standard code assignment library; if the comparison is successful, a specific code in the standard code assignment library is directly assigned to the unit grid; acquiring target area parameter information corresponding to the unit grid and constructing a mapping relationship between the grid code and the target area parameter information; the target area parameter information comprises a position vector, a velocity vector, a distance from an imaging point to the center of the Earth, and a distance from a satellite to the center of the Earth; based on the mapping relationship between the grid code and the target area parameter information, the target area parameter information is converted into target area central latitude; Based on the target area geocentric latitude and the grid code, an imaging down-viewing angle or a pitch attitude angle of a satellite is acquired; six elements of a satellite orbit are extracted, and orbit extrapolation calculation is performed to obtain a time when the satellite orbit is extrapolated to the imaging down-viewing angle ​ acquire satellite orbit periods, accumulate a plurality of the satellite orbit periods, and acquire an imaging period ; optimizing the noise reduction processing on the time and the imaging cycle to obtain a final target imaging time; wherein the optimizing noise reduction processing comprises: based on the time and the imaging cycle , a three-dimensional voxel grid is constructed; in each voxel grid, a time point is statistically analyzed to obtain a central time point; sampling the central time point in each voxel grid, for each central time point, the average distance between the central time point and adjacent central time points is calculated; if the average distance is outside a standard range, the central time point is defined as an outlier and is deleted to obtain an accurate target imaging time.

2. The method according to claim 1, wherein, The construction of the standard code assignment library comprises the following steps: an original code assignment library model is acquired; preference information is acquired, and based on the original code assignment library model and the preference information, one layer or multiple layers included in the original code assignment library are obtained; based on the layer and the original code assignment library model, the standard code assignment library is generated.

3. The method for calculating target imaging time based on satellite sliding focus mode according to claim 1, characterized in that, The process of acquiring six elements of a satellite orbit and performing orbit extrapolation calculation comprises the following steps: based on the six elements of the satellite orbit, a preset model is used to perform orbit extrapolation calculation; the preset model is a TwoBody, J2, J4 or HPOP model.

4. The method according to claim 3, wherein, The six elements of the satellite orbit comprise an orbital semi-major axis, an eccentricity, an orbital inclination, an ascending node right ascension, a perigee argument and a true anomaly.

5. The method according to claim 1, wherein, The imaging downward angle or the pitch attitude angle of the satellite is acquired based on the actual imaging range of the satellite.

6. The method according to claim 1, wherein, The method further comprises the following steps: an interval of a time-adjacent ground track at the equator is acquired, and the expression is as follows: ； wherein, is the interval of time between two successive subs at the equator, is the period of the satellite orbit, is the rotation speed of the Earth, is the average rate of precession of the orbital nodal line; The plurality of satellite orbit periods are accumulated to obtain an imaging period and the imaging period is segmented.

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