CN116883617A - Method and system for constructing moon permanent shadow zone DEM based on correction height measurement data - Google Patents
Method and system for constructing moon permanent shadow zone DEM based on correction height measurement data Download PDFInfo
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Abstract
The invention provides a method and a system for constructing a moon permanent shadow zone DEM based on correction height measurement data. For the problem of screening and correcting the tracks with larger track direction deviation in the track height measurement data set, the screening and correcting of the tracks with larger track direction deviation are respectively realized by adopting a cross point inconsistent value and a reference topography method. And correcting errors of geographic positions of the height measurement data by adopting a self-adaptive iteration mode for the problem of correcting small A/C/R deviation of all the height measurement data. For the problem of filtering abnormal values existing in the height measurement data, a slope trending method is adopted to highlight the abnormal values and remove the abnormal values. The precision and the quality of the constructed DEM product can be obviously improved, and the DEM product has important scientific research value and market value.
Description
Technical Field
The invention belongs to the field of deep space exploration and planetary science, and relates to knowledge and technology in multiple aspects such as remote sensing technology, lunar exploration technology, digital Elevation Model (DEM) construction and precision improvement, lunar topography analysis, space data processing and the like.
Background
The moon area has great scientific value and research significance due to the fact that mineral resources are rich, water ice possibly exists, the geographic position is unique, the complete impact and evolution history of the solar system is saved, and the like. The moon permanent shadow region (Permanent Shadow Region, PSR) is located at the bottom of some deep impact pits in the polar region, and is in a non-illumination state throughout the year due to the lower solar altitude and the shielding of pit walls, so that the moon permanent shadow region is a core target region for polar region in-place detection. The permanent shadow areas of the moon poles are developed for in-situ detection, so that the trail of the interaction of comet, asteroid and solar wind can be captured, and the method plays a key role in recognizing the volatile behavior of the moon and other non-atmospheric celestial bodies of the solar system; meanwhile, a large amount of water ice and volatile matters exist in a permanent shadow area through satellite impact, but scientific problems such as occurrence states, material components and abundance are clarified, and the most effective solution is still to develop in-place and in-situ detection.
The lunar digital elevation model (Digital Elevation Model, DEM) is used as a data base for landing zone selection evaluation, cruise navigation and landing zone positioning, and is a precondition and guarantee for successful implementation of lunar region detection tasks. High-precision DEM depends on high-quality image data and height measurement data, but in a moon permanent shadow area, the solar altitude angle is very low (1-4 degrees), and the optical remote sensing observation effect is not good. In some small-scale impact pits, a small amount of secondary illumination is received through surrounding terrains, backscattering of the earth and weak illumination of stars, and a learner can recover optical images of some small impact pits by using a deep learning algorithm, but the method can not be applied to modeling of large permanent shadow areas and DEM.
At present, a new lunar exploration hot trend is coming, and a high-quality DEM is indispensable to the selection of landing areas, the roaming task of lunar vehicles and the like. How to preprocess and correct the height measurement data of the multi-source satellite and finally construct the high-precision DEM of the moon permanent shadow area is always a difficult problem.
Disclosure of Invention
The invention aims to solve the noise problem of the high-precision DEM of the moon permanent shadow area constructed internationally at present, and provides a method for constructing the high-precision DEM of the moon permanent shadow area through multi-source satellite height measurement data correction. The method is to construct a high-quality DEM with continuous terrain surface and no false terrain by adopting a mode of correcting and filtering denoising points aiming at multi-source height measurement data. The method aims at utilizing laser altimetry data acquired by a satellite detector, improving the precision of a constructed digital elevation model through preprocessing and correcting the data, better describing and analyzing the topographic features of the lunar surface, and providing important scientific data support for future lunar exploration tasks.
The technical scheme provided by the invention is a method for constructing a moon permanent shadow zone DEM based on correction height measurement data, which comprises the following steps:
step 1, preprocessing lunar satellite height measurement data;
step 2, screening and correcting the tracks with the deviation of the height measurement data set along the track direction larger than a certain threshold value;
step 3, performing self-adaptive iterative correction on the vertical trace, the along trace and the radial deviation of all the height measurement data;
and 4, filtering the abnormal value existing in the height measurement data, and finally, obtaining the high-precision and high-quality moon permanent shadow area DEM after interpolation meshing.
Further, the preprocessing in step 1 includes:
(11) Data format conversion: converting the altimetric data stored in the PDS from ASCII or binary format to decimal data format for subsequent processing and analysis;
(12) And (3) data filtering: the filtering algorithm comprises average filtering, median filtering and Gaussian filtering;
(13) Coordinate conversion: projecting longitude and latitude coordinates to a Cartesian coordinate system;
(14) Region data extraction: extracting height measurement data in the range according to the geographical coordinate range of the permanent shadow region, wherein the height measurement data comprises x, y and z coordinates and the acquisition time of the height measurement data point;
(15) And performing block segmentation on the permanently hatched area.
Further, the relation formula between the longitude and latitude coordinates and the cartesian coordinate system in the step (13) is as follows:
（1）
（2）
(northern hemisphere) (3)
(southern hemisphere) (4)
Wherein X and Y are the coordinates of the X and Y axes in a Cartesian coordinate system, LON and LAT are the longitude and latitude coordinates, and the radius R of the moon is 1737400 meters.
Further, the specific implementation manner of the step 2 is as follows;
(21) Track intersection elevation discrepancy calculation
Judging whether the undersea point tracks of the two tracks are crossed or not, if yes, obtaining two point positions of the undersea points of the two tracks closest to the crossing point position, and calculating the crossing point elevation discrepancy value through interpolation;
(22) Track with larger deviation along track direction
Respectively calculating elevation discrepancy values of each track and all other tracks with intersections, taking absolute average values of the elevation discrepancy values as quality parameters of the track, calculating each track data to obtain a quality parameter, setting a quality parameter threshold value, and screening the tracks with the quality parameters larger than the quality parameter threshold value;
(23) Track position correction with greater deviation in track direction
After removing the track data with larger position deviation, a new DEM can be constructed after interpolation meshing of residual height measurement data, the DEM is used as a reference surface, the geographic information of the positions of all data points of the error track is reconstructed through interpolation processing and is used as a reference value, the reference value is compared with the offset condition of all the data points on the original track to obtain the total offset, and the correction of the track data with larger position deviation along the track direction is realized through direct translation along the track.
Further, in the step (21), a fast rejection and straddling algorithm is adopted to judge whether the undersea point tracks of the two tracks are crossed.
Further, the specific implementation manner of the step 3 is as follows;
(31) Taking all laser detection data in the area as a reference surface, and respectively moving all track data along the track and the track sagging directions in a certain step length or converting the track data into movement along the x/y direction;
(32) After each piece of track data is translated, each data point on the track is taken as a center to obtain a data point on a reference surface in a certain searching radius, the data point on the reference surface in the searching radius is interpolated to obtain elevation information of a central position, and then the elevation information is differenced from the data point on the original track to obtain an elevation non-character value;
(33) Taking the standard value of the elevation non-conforming value of the whole track data and the data on the reference plane as a threshold value, discarding the data points with the elevation non-conforming value being larger than a double threshold value as abnormal points, calculating root mean square error as the error value of the track data after the movement of the track data, and determining the position where the error value is minimum as the plane position after the correction of the track data;
(34) After all tracks are corrected, updating a reference surface by using corrected track data, and iteratively performing (31) - (33) to realize correction, wherein the average absolute error value obtained by subtracting the step length from the vertical trace/along trace offset value of all track data in the area before and after each correction is used as a reference value for convergence or not until the reference value of each track position is converged;
(35) And after the plane position correction is completed, correcting the track data in the radial direction by using the same method.
Further, in step (34), when the reference value of the trace/edge after the iteration is close to 0, the iteration is considered to be converged.
Further, in the step 4, the abnormal value is removed by a filtering method based on gradient, specifically: firstly, calculating a DEM gradient value, respectively calculating a difference absolute value between the gradient value and a median of the gradient value in a certain window range with the grid point as a center for each grid, and setting a threshold value for the difference absolute value to screen abnormal values.
The invention also provides a system for constructing the moon permanent shadow zone DEM based on correction height measurement data, which comprises the following modules:
the preprocessing module is used for preprocessing lunar satellite height measurement data;
the screening module is used for screening and correcting the tracks with the deviation of the height measurement data set along the track direction larger than a certain threshold value;
the iteration correction module is used for carrying out self-adaptive iteration correction on the vertical trace, the along trace and the radial deviation of all the height measurement data;
the DEM construction module is used for filtering abnormal values existing in the height measurement data, and finally obtaining the high-precision and high-quality moon permanent shadow area DEM after interpolation meshing.
The invention has the beneficial effects that: the high-precision DEM of the constructed moon permanent shadow area has a large number of false terrains due to the existence of the A/C/R deviation and the abnormal value of the original height measurement data, which greatly influences the subsequent moon scientific detection work. The invention corrects the original data so that the geographical position information of the high-precision points is more accurate, thereby realizing the construction of the high-precision DEM and the removal of noise. In this way, the method not only has no loss of measured data, but also repairs and strengthens the description of partial small-sized topography and landform. The precision and the quality of the constructed DEM product can be greatly improved, and the DEM product has important scientific research value and market value.
Drawings
FIG. 1 is a flowchart of a lunar satellite altimetry data processing in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of a cross-rejection and cross-over experiment in which the cases (a) and (b) can pass the fast rejection test and the case (c) cannot pass the fast rejection test.
Fig. 3 is a frequency histogram obtained by processing an embodiment of the present invention, where (a) and (b) are frequency histograms of the height non-conforming values of the intersection points calculated from the original data and the corrected data, respectively.
Detailed Description
The technical scheme of the invention is specifically described below with reference to the accompanying drawings and examples.
Due to uncertainty in satellite orbit determination and laser altimeter orientation, there is often a sag, deviation in the track and radial (a/C/R) directions in the acquired orbit altimeter data set, which is also the root cause of the large amount of noise present on the constructed high-precision DEM. In general, the A/C/R directional deviation of the track height measurement data is small, and the track height measurement data is shown as tiny noise on a constructed DEM. However, in the long-time flight process, the satellite can cause larger deviation of data position information due to factors such as orbital maneuver or heat blanket effect. Among them, orbital maneuvers may cause the spacecraft to produce thrust and moment imbalances in the along-track direction, the affected orbits often show large deviations along the track, and appear as very noticeable coarse noise on the constructed DEM, which in conventional processing must be treated as coarse and rejected. However, in high-altitude areas with low density and rich topographic features, direct noise removal results in loss of valuable raw observed data. Meanwhile, due to the existence of some abnormal values, false topography such as tiny and abrupt holes or protrusions is usually displayed on the constructed DEM, which seriously affects the quality of the constructed DEM. How to find out the track with larger deviation along the track direction in the multi-source track height measurement data set and correct, correct the small A/C/R deviation of all height measurement data and filter the abnormal value existing in the height measurement data is three key problems to be solved in constructing high-quality DEM.
In order to achieve the above object, the technical solution of the present invention provides corresponding solutions to the three key problems.
For the problem of screening and correcting the tracks with larger deviation along the track direction in the track height measurement data set, the invention aims to respectively screen and correct the tracks with larger deviation along the track direction by adopting a cross point inconsistent value and a reference topography method. And respectively calculating the elevation discrepancy value of each track and the track with the intersection point, and taking the absolute value of the average value of the elevation discrepancy values as the quality parameter of the track. And screening the tracks with larger deviation along the track direction by setting a quality parameter threshold value. And removing some piece of screened track data, constructing geographical information of the tracks by using the nearby track data through an interpolation method, and correcting the data by comparing the offset between the known track and the interpolated track.
For the problem of correcting the small A/C/R deviation of all altimetric data, the invention aims to correct the error of the geographic position of the altimetric data by adopting a self-adaptive iteration mode. Since the a/C/R deviation of data generally has amplitude variation, the same data is not equal in offset values in two areas distant from each other, and therefore, it is necessary to divide the data into a plurality of areas 20000 m×20000 m for correction. For the track data in each area, taking all laser height measurement data in the area as a reference, and respectively moving a certain selected track data along the track and the track direction with a smaller step length (the movement along the x/y direction in a Cartesian coordinate system can be converted). After the track is translated, data points within a certain searching radius are obtained by taking each data point on the track as a center. And interpolating the data points of other tracks in the search radius to obtain the elevation information of the central position and making a difference with the data points on the original track. After the point-by-point difference statistics, the data points with the non-conforming value larger than twice standard deviation are discarded as abnormal points, and the non-conforming value of the rest data points is used for calculating the root mean square error as the error value of the track data after the movement. And performing multiple movements according to a certain step length, and determining the position where the error value is minimum as the corrected plane position of the piece of track data. Since the reference data is also obtained from the data points to be corrected, the above process is also required to be iteratively corrected until the adjustment values of the respective track plane positions converge. After the plane position is corrected accurately, the method is also used for correcting the track data in the radial direction.
For the problem of filtering abnormal values existing in the height measurement data, the invention aims to highlight the abnormal values and remove the abnormal values by adopting a slope trending method. The change in slope values is more sensitive to the detection of outliers on the DEM, especially declivity slope values. The main method comprises the following steps: and calculating gradient values of the DEM with a certain resolution in the target area, and obtaining absolute values of gradient values of each grid and median differences of gradient values in a certain window range taking the grid point as a central point. The calculation result is set to a threshold value (such as about 0.1% of the points in the removed area) and the abnormal value is screened.
As shown in fig. 1, the method for constructing the permanent shadow area DEM of the moon by correcting the laser altimetry data provided by the embodiment of the invention comprises the following specific implementation steps:
1. lunar satellite altimetry data preprocessing
The lunar satellite laser altimetry data is high-precision three-dimensional data obtained by using a satellite laser altimetry technology, and can provide information such as elevation, position, topographic features and the like of the lunar surface. The laser altimeter scans the moon surface through the laser beam, and measures the time for the laser beam to return after being reflected by the surface, so that the distance between the laser beam and the moon surface is calculated. The measurement accuracy and spatial resolution of different satellite laser altimeters are different, and generally can reach horizontal resolution of several meters to tens of meters, and vertical resolution of several meters to tens of meters. The data has important significance for exploring geological structures, topography, resource distribution and the like of the moon, and provides scientific support and data foundation for future lunar exploration tasks. The multi-source altimetric data preprocessing process generally includes:
(1) Data format conversion: the altimetric data stored in the form of PDS (Planetary Data System, planetary probe data processing and archiving system) is converted from ASCII or binary format to a common data format, such as decimal, for subsequent processing and analysis.
(2) And (3) data filtering: because of noise in laser altimetry data, filtering is required to reduce errors and improve accuracy. Typical filtering algorithms used are average filtering, median filtering, gaussian filtering, etc.
(3) Coordinate conversion: because the permanent shadow area of the lunar surface is mostly near the polar region, the longitude and latitude coordinates are adopted to have larger grid error in the subsequent interpolation, the longitude and latitude coordinates are required to be projected under a Cartesian coordinate system, and the relation formula of the longitude and latitude coordinates is as follows:
（1）
（2）
(northern hemisphere) (3)
(southern hemisphere) (4)
Wherein X and Y are the coordinates of the X and Y axes in a Cartesian coordinate system, LON and LAT are the longitude and latitude coordinates, and the radius R of the moon is 1737400 meters.
(4) Region data extraction: and extracting height measurement data (including x, y and z coordinates and acquisition time of the height measurement data points) in the range according to the geographical coordinate range of the permanent shadow region.
(5) Block segmentation: since the a/C/R deviation of the track data generally has amplitude variation, the offset values of the same track data in two areas farther apart are not equal. Therefore, the area to be built (i.e. the permanently hatched area of the moon surface) with a large area needs to be divided into blocks, and the following correction steps are performed respectively, so that the final DEM to be built has better quality (the block size is usually 20000 m×20000 m).
2. Track screening and correction with larger deviation in track direction in height measurement data set
Due to deviations in the track maneuver or the pointing direction of the instrument that occur during certain periods of time, the laser altimeter may produce very large deviations in the track direction, which may appear as very noticeable coarse noise on the constructed DEM, which is typically treated as coarse and rejected in conventional processing. However, in high-density areas with low density and rich terrain features, direct noise elimination results in loss of precious original observed data, and the terrain features of the areas cannot be displayed correctly through interpolation processing, so that the accuracy and reliability of the DEM are affected. After the preprocessing of the height measurement data is completed, the method for screening the track with larger deviation along the track direction by the height measurement data set comprises the following correction steps:
(1) Track intersection elevation discrepancy calculation
In the altimetric data processing, track positioning accuracy is generally improved by adding intersection data, so that the quality of the constructed DEM is improved. Similarly, the intersection elevation discrepancy value existing in different track data can also be used as the judgment basis of the track precision. The intersection is defined as the position of the point under the satellite where the two orbits intersect during the periodic motion of the detector. The three-dimensional geographic information measured at the position at different moments under ideal conditions is the same, but the acquired data has deviation of the three-dimensional position due to the influence of various errors, such as satellite attitude control precision, inaccurate instrument calibration and the like.
The calculation of the intersection elevation discrepancy value needs to judge whether the undersea point tracks of the two tracks are crossed or not, and if yes, the two point positions of the undersea points of the two tracks, which are nearest to the intersection point position, are also needed to be obtained, and the intersection elevation discrepancy value is calculated through interpolation. For the existence of crossing points of the undersea point tracks of the two tracks, a fast rejection and straddling experimental algorithm is generally adopted. The demonstration of the rapid rejection and straddling experiment algorithm is shown in figure 2, and whether the intersection point exists between the line segment formed by the points A1 and A2 and the line segment formed by the points B1 and B2 only needs to be judged if the rapid rejection and straddling experiment algorithm can pass. And connecting A1 and A2 with B1 and B2, respectively using the two lines as diagonal lines to generate two rectangular areas R1 and R2, and judging whether the overlapping areas exist in the R1 and R2 or not is called a rapid rejection algorithm. The two cases (a) and (b) shown in fig. 2 can pass the fast rejection experiment, and (c) can not pass, but the two line segments shown in (b) are obviously disjoint. Determining whether A1, A2 crosses a straight line formed by B1, B2 (i.e., A1, A2 is distributed on both sides of the line connecting B1, B2), while B1, B2 crosses a straight line formed by A1, A2 is referred to as a straddling experiment.
To determine the position of the crossing point between a lifting rail and a lowering rail, since the rails are composed of data points with smaller position spacing, an enumeration method is generally adopted, i.e. two adjacent points on the lifting rail are respectively circulated with two adjacent points on the lowering rail to perform fast rejection and straddling experiments until two points on the lifting rail closest to the crossing point are determined. And obtaining the position information corresponding to each intersection point by using inverse distance weighting, wherein the elevation difference value of the two is the elevation non-conforming value of the intersection point.
(2) Track with larger deviation along track direction
And respectively calculating elevation discrepancy values of each track and all other tracks with crossing points, and taking the absolute average value of the elevation discrepancy values as the quality parameter of the track. Therefore, each track data can be calculated to obtain a quality parameter, and the quality parameter value is also large because the track with larger deviation along the track direction is inevitably different from the crossing points of other normal tracks, and the track with larger quality parameter (the quality parameter is larger than the quality parameter threshold) can be screened by setting the threshold.
(3) Track position correction with greater deviation in track direction
The steps carry out screening of tracks with larger position deviation along the track direction, and after the track data with larger position deviation are removed, a new DEM can be constructed by utilizing residual height measurement data interpolation grid. The large noise evident on the DEM has disappeared, but part of the measured data is missing. The DEM is used as a reference surface, and the geographic information of the positions of all data points of the error track is reconstructed through interpolation processing and is used as a reference value. And comparing the offset conditions of the reference value and each data point on the original track to obtain the total offset, and correcting the track data with larger deviation along the track direction by simple direct translation along the track. Experiments show that after the correction method of the invention corrects the error orbit of the LOLA original height measurement data, the large-scale noise existing on the surface of the DEM can be obviously eliminated.
3. Correction of A/C/R deviations for all altimetric data
Due to uncertainty in satellite orbit determination and attitude, there is often sag, track-and-radial (a/C/R) bias in the acquired altimetric data set, which is also the root cause of the large amount of noise present on the constructed high-precision DEM. The A/C/R direction deviation of the track height measurement data is smaller, and the track height measurement data is shown as finer noise on the constructed DEM. The invention aims to correct uncertainty of the geographical position of original laser data by adopting a self-adaptive iteration method, and comprises the following specific steps:
(1) All the laser detection data in the area are used as reference surfaces, and the movement along the track and the track hanging direction (the movement along the x/y direction can be converted) is respectively carried out on all the track data by a small step length (for example, 2.5 meters and the adjustment is needed according to the density of the laser height measurement data). The choice of step size is related to the accuracy of the data, the higher the accuracy the smaller the data step size is desirable.
(2) After each piece of track data is translated, each data point on the track is taken as a center to obtain the data point on the reference surface in a certain searching radius. And interpolating the data points on the reference surface in the searching radius to obtain the elevation information of the central position, and performing difference between the central position and the data points on the original track to obtain the elevation discrepancy value.
(3) Taking the standard value of the elevation non-conforming value of the whole track data and the data on the reference plane as a threshold value, discarding the data points with the elevation non-conforming value being larger than a double threshold value as abnormal points, and calculating root mean square error as the error value of the track data after the movement. And determining the position where the error value is minimum as the plane position after the correction of the track data.
(4) After all track corrections are completed, since the reference surface is also derived from the data points to be corrected. Therefore, the reference surface is updated by using the corrected track data and the process is iteratively corrected. Taking the average absolute error value obtained by subtracting the step length from the A/C offset values of all track data in the area before and after each correction as a reference for convergence or not until the reference value of each track position is converged.
(5) And after the plane position correction is completed, correcting the track data in the radial direction by using the same method.
By the steps, adaptive A/C/R correction is carried out on the track data, and more accurate geographic positions are obtained after repeated iterative correction. When the reference value of A/C is near 0 after the iteration, the iteration is considered to have converged. Finally, the uncertainty of the geographical position of the track data is corrected, the DEM is constructed after interpolation meshing, and noise (pseudo terrain) can be eliminated.
4. Filtering outliers present in altimetric data
DEM constructed using adaptive iteration corrected altimetric data still has a few noise points, and these noise points caused by outliers typically appear as tiny and abrupt voids or protrusions on the DEM as false terrains, and these abnormal noise points can be removed by a slope-based filtering method. The change in slope values is more sensitive to the detection of abnormal values of DEM, especially trended slope values. And calculating the gradient value of the DEM, and respectively calculating the absolute value of the difference between the gradient value of the DEM and the median of the gradient values in a certain window range with the grid point as the center for each grid. An outlier screening was performed by setting a threshold (about 0.1% of points in the removed area) to the calculation result (absolute difference value), and the noise on the DEM due to the outlier disappeared.
The invention can be summarized as follows: and a high-precision and high-quality DEM method for the moon permanent shadow area is further constructed by correcting satellite height measurement data. Breaks through the bottleneck of fine topography mapping, and is helpful to improve the effectiveness and safety of the in-place detection of the lunar region. Firstly, preprocessing satellite height measurement data, screening orbits with larger deviation along the track direction in the height measurement data set, correcting, carrying out self-adaptive iterative correction on A/C/R deviation of all height measurement data, filtering abnormal values after adjustment is finished, and finally constructing a high-precision and high-quality moon permanent shadow zone DEM after interpolation gridding.
So far, according to the above flow, the high-precision DEM of the moon permanent shadow area meeting the lunar exploration engineering requirement is finally obtained. In specific implementation, the automatic flow operation can be realized by adopting a computer technology.
The invention also provides a system for constructing the moon permanent shadow zone DEM based on correction height measurement data, which comprises the following modules:
the preprocessing module is used for preprocessing lunar satellite height measurement data;
the screening module is used for screening and correcting the tracks with the deviation of the height measurement data set along the track direction larger than a certain threshold value;
the iteration correction module is used for carrying out self-adaptive iteration correction on the vertical trace, the along trace and the radial deviation of all the height measurement data;
the DEM construction module is used for filtering abnormal values existing in the height measurement data, and finally obtaining the high-precision and high-quality moon permanent shadow area DEM after interpolation meshing.
The specific implementation manner of each module is the same as that of each step, and the invention is not written.
According to the specific steps implemented above, based on LOLA altimetry data, a lunar north permanent shadow area Hermite A impact pit is constructed as an example (range in Cartesian coordinate system: 58km to 38km, 50km to 28 km). After the data is preprocessed in the first step, 823267 effective altitudes of the area are obtained. After screening and correcting the tracks with larger track deviation along the second step, screening and correcting one track (LOLARDR_ 110590332) with larger track deviation in the area. And correcting the A/C/R deviation of all the height measurement data through the third step. After six iterations, the correction values for the deviations of all track data in the a/C direction in this region converge. And correcting the track data in the region in the R direction, and further filtering the abnormal value to finally obtain the high-quality and noiseless DEM after the region is corrected. Compared with the DEM before correction, the surface noise is completely eliminated, and partial topography and topography are better described.
In altimetric data processing, track data accuracy is generally improved by adding intersection data, so that the quality of the constructed DEM is improved. Similarly, the elevation discrepancy value of the crossing points of different track data can also be used as the judgment basis of the track data precision. In the Hermite a region, the elevation discrepancy values of the intersections of the orbit data before and after correction were calculated, and 1063494 and 1063464 intersections were obtained, respectively, and the frequency histogram was shown in fig. 3. Fig. 3 (a) and (b) are frequency histograms of the height non-conforming values of the intersection calculated from the original data and the corrected data, respectively. The error of the whole track data is further reduced because the corrected cross point non-conforming value is concentrated to be close to zero by the whole normal distribution change. The average values of absolute values of the non-coincident values of the crossing points before and after correction are 26.8 meters and 17.4 meters respectively, which shows that the improvement effect is obvious after correction by using the invention.
In the foregoing, the protection scope of the present invention is not limited to the preferred embodiments of the present invention, and any simple changes or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed in the present invention fall within the protection scope of the present invention.
Claims (9)
1. The method for constructing the moon permanent shadow zone DEM based on the correction height measurement data is characterized by comprising the following steps of:
step 1, preprocessing lunar satellite height measurement data;
step 2, screening and correcting the tracks with the deviation of the height measurement data set along the track direction larger than a certain threshold value;
step 3, performing self-adaptive iterative correction on the vertical trace, the along trace and the radial deviation of all the height measurement data;
and 4, filtering the abnormal value existing in the height measurement data, and finally, obtaining the high-precision and high-quality moon permanent shadow area DEM after interpolation meshing.
2. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as set forth in claim 1, wherein: the preprocessing in step 1 comprises the following steps:
(11) Data format conversion: converting the altimetric data stored in the PDS from ASCII or binary format to decimal data format for subsequent processing and analysis;
(12) And (3) data filtering: the filtering algorithm comprises average filtering, median filtering and Gaussian filtering;
(13) Coordinate conversion: projecting longitude and latitude coordinates to a Cartesian coordinate system;
(14) Region data extraction: extracting height measurement data in the range according to the geographical coordinate range of the permanent shadow region, wherein the height measurement data comprises x, y and z coordinates and the acquisition time of the height measurement data point;
(15) And performing block segmentation on the permanently hatched area.
3. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as claimed in claim 2, wherein: in the step (13), the relation formula of longitude and latitude coordinates and a Cartesian coordinate system is as follows:
（1）
（2）
northern hemisphere (3)
Southern hemisphere (4)
Wherein X and Y are the coordinates of the X and Y axes in a Cartesian coordinate system, LON and LAT are the longitude and latitude coordinates, and the radius R of the moon is 1737400 meters.
4. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as set forth in claim 1, wherein: the specific implementation mode of the step 2 is as follows;
(21) Track intersection elevation discrepancy value calculation: judging whether the undersea point tracks of the two tracks are crossed or not, if yes, obtaining two point positions of the undersea points of the two tracks closest to the crossing point position, and calculating the crossing point elevation discrepancy value through interpolation;
(22) Screening tracks with larger deviation along the track direction: respectively calculating elevation discrepancy values of each track and all other tracks with intersections, taking absolute average values of the elevation discrepancy values as quality parameters of the track, calculating each track data to obtain a quality parameter, setting a quality parameter threshold value, and screening the tracks with the quality parameters larger than the quality parameter threshold value;
(23) Track position correction with large deviation along track direction: after removing the track data with larger position deviation, a new DEM can be constructed after interpolation meshing of residual height measurement data, the DEM is used as a reference surface, the geographic information of the positions of all data points of the error track is reconstructed through interpolation processing and is used as a reference value, the reference value is compared with the offset condition of all the data points on the original track to obtain the total offset, and the correction of the track data with larger position deviation along the track direction is realized through direct translation along the track.
5. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as set forth in claim 4, wherein: in the step (21), a fast rejection and straddling algorithm is adopted to judge whether the undersea point tracks of the two tracks are crossed.
6. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as set forth in claim 1, wherein: the specific implementation mode of the step 3 is as follows;
(31) Taking all laser detection data in the area as a reference surface, and respectively moving all track data along the track and the track sagging directions in a certain step length or converting the track data into movement along the x/y direction;
(32) After each piece of track data is translated, each data point on the track is taken as a center to obtain a data point on a reference surface in a certain searching radius, the data point on the reference surface in the searching radius is interpolated to obtain elevation information of a central position, and then the elevation information is differenced from the data point on the original track to obtain an elevation non-character value;
(33) Taking the standard value of the elevation non-conforming value of the whole track data and the data on the reference plane as a threshold value, discarding the data points with the elevation non-conforming value being larger than a double threshold value as abnormal points, calculating root mean square error as the error value of the track data after the movement of the track data, and determining the position where the error value is minimum as the plane position after the correction of the track data;
(34) After all tracks are corrected, updating a reference surface by using corrected track data, and iteratively performing (31) - (33) to realize correction, wherein the average absolute error value obtained by subtracting the step length from the vertical trace/along trace offset value of all track data in the area before and after each correction is used as a reference value for convergence or not until the reference value of each track position is converged;
(35) And after the plane position correction is completed, correcting the track data in the radial direction by using the same method.
7. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as set forth in claim 6, wherein: in step (34), when the reference value of the trace/edge after the iteration approaches 0, the iteration is considered to have converged.
8. The method of constructing a moon permanent shadow DEM based on corrected altimetric data as set forth in claim 1, wherein: in the step 4, abnormal values are removed by a filtering method based on gradient, specifically: firstly, calculating a DEM gradient value, respectively calculating a difference absolute value between the gradient value and a median of the gradient value in a certain window range with the grid point as a center for each grid, and setting a threshold value for the difference absolute value to screen abnormal values.
9. The system for constructing the moon permanent shadow zone DEM based on the correction height measurement data is characterized by comprising the following modules:
the preprocessing module is used for preprocessing lunar satellite height measurement data;
the screening module is used for screening and correcting the tracks with the deviation of the height measurement data set along the track direction larger than a certain threshold value;
the iteration correction module is used for carrying out self-adaptive iteration correction on the vertical trace, the along trace and the radial deviation of all the height measurement data;
the DEM construction module is used for filtering abnormal values existing in the height measurement data, and finally obtaining the high-precision and high-quality moon permanent shadow area DEM after interpolation meshing.
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