CN114882191A - Digital elevation model generation method, electronic equipment and computer readable storage device - Google Patents
Digital elevation model generation method, electronic equipment and computer readable storage device Download PDFInfo
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Abstract
The application discloses a method for generating a digital elevation model. The method comprises the following steps: obtaining an original digital surface model; carrying out smooth filtering processing on the original digital surface model to obtain a filtered digital surface model; obtaining a cover height model based on the original digital surface model and the filtered digital surface model; performing height screening processing on each data point in the cover height model to obtain ground candidate points; screening the derivative of the ground candidate point through a preset derivative threshold value to obtain a ground point; and obtaining a digital elevation model based on the ground points. The application also discloses an electronic device and a computer readable storage device. By means of the method, the digital elevation model can be accurately obtained through the digital surface model.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a method for generating a digital elevation model, an electronic device, and a computer-readable storage device.
Background
A Digital Elevation Model (DEM) refers to a data set of plane coordinates and elevations of regular grid points in a certain range, and mainly describes spatial distribution of a regional landform form, which is a virtual representation of the landform. The DEM contains only elevation information of the terrain and no other surface information. Because the DEM describes the ground elevation information, the DEM can be widely applied to the fields of national economy and human natural science such as surveying and mapping, hydrology, meteorology, geology, military, engineering construction and the like. A Digital Surface Model (DSM) is a ground elevation Model including the heights of Surface buildings, trees, bridges, roads, and the like. The DSM further contains elevation information for earth surface information other than terrain on the basis of the DEM. The information of the real ground surface is represented by the fluctuation condition of the real ground surface, particularly the information of buildings, bridges, roads and the like, and the information is widely used in the aspects of urban treatment and development planning. However, generally, only the digital surface model can be obtained by scanning the terrain, but the digital elevation model cannot be directly obtained, and how to accurately obtain the digital elevation model through the digital surface model becomes a problem to be solved by those skilled in the art.
Disclosure of Invention
The method mainly aims to provide a method for generating a digital elevation model, and can solve the technical problem of accurately obtaining the digital elevation model through a digital surface model.
In order to solve the above technical problem, the first technical solution adopted by the present application is: a method for generating a digital elevation model is provided. The method comprises the following steps: obtaining an original digital surface model; carrying out smooth filtering processing on the original digital surface model to obtain a filtered digital surface model; obtaining a cover height model based on the original digital surface model and the filtered digital surface model; carrying out height screening processing on each data point in the cover height model to obtain ground candidate points; screening the derivative of the ground candidate point through a preset derivative threshold value to obtain a ground point; and obtaining a digital elevation model based on the ground points.
In order to solve the above technical problem, the second technical solution adopted by the present application is: an electronic device is provided. The electronic device comprises a memory for storing program data that can be executed by a processor for implementing the method as described in the first aspect and a processor.
In order to solve the above technical problem, the third technical solution adopted by the present application is: a computer-readable storage device is provided. The computer readable storage means stores program data that can be executed by a processor to implement the method as described in the first aspect.
The beneficial effect of this application is: the method comprises the steps of obtaining a filtering digital surface model by carrying out smooth filtering on an original digital surface model, and further obtaining a covering object height model capable of expressing the height of a covering object above the earth surface by calculating the original digital surface model and the filtering digital surface model. And screening the heights of the data points in the covering height model to obtain ground candidate points which can be used as ground data points, further screening derivatives of the ground candidate points to remove data points which are judged to be non-ground from the ground candidate points, and finally leaving the ground candidate points as the ground points. And finally, obtaining a digital elevation model based on the ground points. Through the acquisition of the covering object height model, the further screening and derivative judgment processing of the data points in the covering object height model, the ground candidate points are divided into the ground points and the non-ground points, so that the obtained ground points are more accurate, and the digital elevation model obtained based on the ground points is more accurate.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of a method for generating a digital elevation model according to the present application;
FIG. 2 is a schematic flow chart diagram illustrating a second embodiment of a method for generating a digital elevation model according to the present application;
FIG. 3 is a schematic flow chart diagram illustrating a third embodiment of a method for generating an elevation model according to the present application;
FIG. 4 is a schematic flow chart diagram illustrating a fourth embodiment of a method for generating a digital elevation model according to the present application;
FIG. 5 is a schematic structural diagram of an embodiment of an electronic device of the present application;
FIG. 6 is a schematic diagram of an embodiment of a computer-readable storage device;
fig. 7 is a schematic structural diagram of an embodiment of an electronic device according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first", "second", etc. in this application are used to distinguish between different objects and not to describe a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Before describing the technical solutions of the present application, some concepts will be briefly introduced.
In the field of mapping, the use of digital models is quite widespread. Some Digital models that are commonly used are, for example, a Digital Line Map (DLG), a Digital Raster Map (DRG), a Digital Orthophotomap (DOM), a Digital Terrain Model (DTM), a Digital Surface Model (DSM), and a Digital Elevation Model (DEM), among others.
The digital scribe map DLG is a vector data set of map elements that substantially coincide with existing scribes, and stores spatial relationships between the elements and related attribute information. The visual effect is consistent with that of a topographic map with the same scale but the color is richer.
The digital raster map DRG is a raster data set which is formed according to the existing paper, film and other topographic maps and is consistent with the topographic maps in content, geometric precision and color after scanning, geometric correction and color correction. The product is a product for transition from a simulation product to a digital product, and can be used as a background reference image and relevant reference and analysis of other spatial information. The method can be used for data acquisition, evaluation and updating of a digital line map, and can also be integrated with data such as a digital orthophoto map and a digital elevation model to derive new information and manufacture a new map.
The digital orthophoto map DOM is a digital orthophoto set generated by performing digital differential correction and mosaic on an aviation (or aerospace) photo and cutting according to a certain image range. It is an image with both map geometric accuracy and imagery features. The method has the advantages of high precision, rich information, intuition, vividness, quickness in acquisition and the like.
The digital ground model DTM is a data set of plane coordinates (x, y) and other property attributes of elements. Which describes the spatial distribution of the topography of the area in three dimensions from a differential perspective. DTM is a digital representation of topographical surface morphology attribute information, which is a digital description with spatial location features and topographical attribute features. And x and y represent plane coordinates of the point, and when other property attribute data are elevations, namely z-axis data are elevations, the digital elevation model DEM is formed. Other property attribute data may also be information such as grade, temperature, etc.
The digital surface model DSM refers to a ground elevation model that includes the height of surface buildings, bridges, trees, etc. The digital elevation model only contains ground elevation information. In general, objects on the ground surface, such as buildings and vegetation, can be removed by simply smoothing the digital surface model to obtain a digital elevation model. However, a lot of errors are introduced by the simple filtering method, the earth surface building vegetation and the like cannot be well removed, and the obtained digital elevation model is not very accurate. Therefore, the present application proposes the following technical solution to process the digital surface model to obtain a more accurate digital elevation model.
Referring to fig. 1, fig. 1 is a schematic flow chart of a first embodiment of the data storage method of the present application. Which comprises the following steps:
s11: an original digital surface model is obtained.
The raw digital surface model is the digital surface model that has not been processed. The digital surface model is mainly obtained by satellite remote sensing and aerial photography. With the development of the spatial information acquisition technology, more flexible means are derived to acquire the required area, such as the laser radar technology. The acquired raw digital surface model may be obtained by self-scanning, or may be existing data directly acquired from other platforms or databases.
S12: and carrying out smooth filtering processing on the original digital surface model to obtain a filtered digital surface model.
Smoothing filtering is a low frequency enhanced spatial domain filtering technique. Its effect is firstly fuzzy and secondly noise elimination. Smoothing in the spatial domain typically uses averaging to average data from neighboring data points. The size of the field is therefore directly related to the effect of smoothing. The larger the domain, the better the smoothing effect, but the too large domain, the smoothing may cause excessive loss of edge information, so that the output result becomes too fuzzy. The main smoothing filtering methods include domain smoothing filtering and median smoothing filtering. And gaussian filtering belongs to one of linear smoothing filters. Gaussian filtering is linear and is suitable for removing gaussian noise, while median filtering is nonlinear and is suitable for removing impulse noise.
In an embodiment of the present application, the filtered digital surface model is obtained by processing the original digital surface model through two-dimensional gaussian filtering. Two-dimensional gaussian filtering is used to remove noise in the digital surface model and detail information of the surface covering. When filtering is carried out, the obtained filtering digital surface model represents the terrain gradient information of the area corresponding to the original digital surface model to a certain extent according to the actual situation by combining the specific sigma value and the Gaussian kernel size.
The two-dimensional gaussian distribution is as follows:
where the parameter values may be σ =25m, x = y =100 m.
S13: a covering height model is obtained based on the original digital surface model and the filtered digital surface model.
The smooth filtering removes the details of the covering on the original digital surface model, and the height of the data point corresponding to the covering is calculated by the height of the adjacent data point. A filtered digital surface model is a model that represents terrain information to some extent. The original digital surface model is a model including terrain and ground surface covering height information, so that the covering height model of the covering height of the corresponding area can be obtained by calculating and processing the original digital surface model and the filtered digital surface model obtained after filtering.
In one embodiment, the height of the corresponding data point of the filtered digital surface model is subtracted from the height of each data point of the original digital surface model to obtain the height of the corresponding data point of the cover height model. The height of the original digital surface data points minus the height of the terrain is representative of the height of the cover above the surface. And the height of the covering is corresponding to the corresponding data point, so that a covering height model based on the corresponding area of the original digital surface model is obtained.
S14: and (4) performing height screening processing on each data point in the cover height model to obtain ground candidate points.
For height values in the covering height model, it can be determined that the ground points are all data points with lower height values. The data points with lower height values are mixed with some data points of the covering object which are not processed by the smoothing filtering, and the corresponding height values are lower when the height of the covering object is calculated because the smoothing filtering is not processed. Therefore, the data points with lower height values are screened out as candidate ground points. The ground candidate points are used for subsequent further screening processing.
S15: and screening the derivative of the ground candidate point through a preset derivative threshold value to obtain the ground point.
The derivative is a local property, and the derivative of a function at a point can describe the rate of change of the function at that point. For some points with excessive gradients, isolated high-level points, or outliers, noise points, the derivative value is very large relative to the derivative values of other data points. Because the slope of the ground is generally changed gently and slightly, the derivative of the ground data point is not large.
In one embodiment, after the ground candidate points are obtained, the second derivative of the ground candidate points is calculated. And screening and judging the obtained second derivative, and judging the ground candidate points of which the second derivative is less than or equal to a preset derivative threshold value as ground points. Because the derivative values of the edge points with overlarge gradient, the isolated elevation high and low points, the outliers and the like are overlarge, a derivative threshold value is set for the edge points, the edge points with overlarge gradient, the isolated elevation high and low points, the outliers and the outliers, the candidate points are judged to be the abnormal points if the derivative values are exceeded, and the ground points are judged if the derivative values are not exceeded.
Ground candidate points are screened by obtaining the height of the covering object, and the final ground point is further judged by a derivative, so that ground point data which is more accurate and more accords with the actual terrain condition is obtained.
S16: and obtaining a digital elevation model based on the ground points.
After the ground points are obtained, since some non-ground data points are screened and excluded in advance, the data points in the model need to be complemented to obtain a complete digital elevation model.
In one embodiment, the heights of the ground points are used to calculate the heights of other data points needed for the digital elevation model by linear interpolation. The linear interpolation is to approximate the original function by using a straight line passing through two points, and the interpolation error of the linear interpolation on an interpolation node is zero. The linear interpolation calculation is quick, simple and convenient. The linear interpolation includes one-dimensional linear interpolation, bilinear interpolation, trilinear interpolation and the like. The interpolation method for completing the data points includes a nearest neighbor interpolation method, a parabolic interpolation method, a radial basis function method, a minimum curvature method and the like.
Referring to fig. 2, fig. 2 is a schematic flow chart of a data storage method according to a second embodiment of the present application. The method is a further extension of the screening process for cover height. Which comprises the following steps:
s21: setting a height threshold interval.
A height threshold interval is set for the height of the data points in the cover height model. Because the ground points are data points with lower height in the covering object height model, a height threshold interval is set in a low height range, and the ground points are primarily screened.
S22: and taking the data points with the height in the height threshold interval as candidate ground points.
The data points with the height in the threshold interval are used as candidate points on the ground, and the points exceeding the threshold interval are the points with covering and some noise points, etc., which are used as non-ground points to be screened out.
When the embodiment is used for screening, screening can also be performed by setting a sliding window.
Referring to fig. 3, fig. 3 is a schematic flow chart of a data storage method according to a third embodiment of the present application. The method is a further extension of the screening process for cover height. Which comprises the following steps:
s31: a sliding window is preset.
And designing a sliding window according to parameters such as the plane size and the area of the ground surface object. The sliding window is used to segment the model. The data of the model is divided into different parts for processing, so that the data processing result is more accurate, and result errors caused by losing certain details due to too large data volume are avoided.
S32: the overlay height model is traversed using a sliding window.
And (4) performing independent processing on data points in each window by utilizing a preset cover model obtained by traversing the sliding window.
S33: and selecting a preset proportion of data points with the lowest height from the area covered by the current position of the sliding window.
When the window slides to a certain area, the cover height of the data points in the area is obtained, and the lowest point of a certain proportion is selected. The proportion can be set according to actual conditions and can be any proportion between five percent and thirty-three percent.
S34: the median height of the selected data points is calculated.
The heights of the coverings of the selected data points with the lowest proportion are arranged in a size sequence, and the median height of the height values of the data points is selected.
S35: and determining a height threshold interval by taking the median height as a center.
And determining a height threshold interval by using the obtained median height. The size of the threshold interval is determined by the height of the object to be filtered on the general surface. If the height of the object to be filtered is about ten meters, the median is taken as the center, and the height interval range of twenty meters above and below is taken as the threshold interval for determining the ground candidate point.
S36: and taking the data points in the height threshold interval as candidate ground points.
The data points with the height in the threshold interval are used as candidate points on the ground, and the points exceeding the threshold interval are the points with covering and some noise points, etc., which are used as non-ground points to be screened out.
Compared with the conventional pixel-by-pixel or point-by-point sliding window, the whole calculation efficiency is greatly improved by setting the sliding window and the corresponding step length according to the actual situation.
Referring to fig. 4, fig. 4 is a schematic flow chart of a fourth embodiment of the data storage method of the present application. The method is a further extension of the above embodiment. Which comprises the following steps:
s41: the overlay height model is traversed multiple times through the sliding window.
And traversing the cover height model for multiple times by using the sliding window, wherein at least one of a starting point, a sliding step length and a window size of the sliding window in each traversal is different. Therefore, in order to ensure that the data points covered in the window are different during each traversal, the final obtained data result has higher accuracy by using different range divisions to carry out multiple calculations.
In one embodiment, four traversals are performed with different starting points. Assuming that the preset length and width of the sliding window is F, the step size of the sliding is also set to F, but the starting position points of each traversal are (F/2 ), (F/2, F) and (F, F), respectively. The height of each data point is calculated four times, and the data in the corresponding sliding window is different in each calculation.
In one embodiment, the traversal is performed with different window sizes. When the length and the width of the window are consistent, the length and the width of the window are taken as sliding step lengths, different window sizes are set in each traversal, and corresponding contained data after the sliding of the different window sizes are different.
In one embodiment, the traversal is performed in sliding steps. Assuming that the length and width of the preset sliding window is F, and F/2 is used as the sliding step, after one traversal, each data point in the cover height model is calculated four times. The relationship between the window size and the step size set over the traversal corresponds to the number of times each data point in the model can be calculated.
S42: and after the multiple traversals are completed, excluding the data points which are determined as the ground candidate points and have the times smaller than or equal to a preset time threshold value.
And after multiple traversals, namely after each data point is calculated for multiple times, excluding the data points which are determined as the ground candidate points and the times of which are less than a preset time threshold value. If the relationship between the size of the sliding window and the sliding step length is set, multiple calculations of each data point can be completed through one traversal.
In one embodiment, after each window sliding, the data points are divided into candidate ground points and non-ground points. After one traversal, each data point in the model is divided into two types, namely a ground candidate point and a non-ground point, and the data points are divided into two types again by the traversal again, and so on. And setting a preset frequency threshold according to the frequency of the data point calculation so as to divide all the data points and determine a final ground point. If each data point is calculated four times, the data points which are judged as ground candidate points twice or less are finally judged as non-ground points, and the non-ground points are excluded. And finally judging which ground point is the data point which is judged as the ground candidate point for more than two times, and reserving the data point.
The more times each data point in the cover height model is statistically determined, the more accurate the final ground point obtained finally, and the more accurate the obtained digital height model.
The following describes the technical solution of the present application by taking a specific example.
An original DSM is obtained, and a filtering digital surface model called DSMS is obtained through two-dimensional Gaussian filtering. The cover height model is further calculated by subtracting the height of the data points in the DSMS from the height of the original DSM data points and is called DSMO.
The second derivative of the DSMO is calculated. And calculating a second derivative delta DSMO through a Laplace operator, screening the delta DSMO by using a preset derivative threshold value, and judging that the second derivative value exceeds the threshold value as a non-ground point and using the value smaller than the threshold value as a ground candidate point.
And (3) presetting a sliding window, wherein the length and the width of the sliding window are F, the sliding step length is F, the starting points are (F/2 ), (F, F/2), (F/2, F) and (F, F) of the image at the upper left, traversing the whole image for four times, and ensuring that each point is counted for four times. During each pass, within each sliding window, a DSMO data point within the sliding window is acquired. A certain proportion (5% -33%) of their nadirs are obtained, and the median height H of these nadirs is calculated. And determining the height interval H +/-H according to a preset height threshold H. Data points with height values within the interval are regarded as candidate ground points, and data points outside the interval are regarded as non-ground points.
And finally, judging whether the data is the final data point according to the times of judging the data as the ground candidate according to the data point and the corresponding second derivative. The decision mode may be determined by means of vote counting. For example, for four traversals, where a data point is counted four times, the number of votes whose second derivative exceeds the derivative threshold is recorded as-2 and those that do not exceed is recorded as 1. And the ground candidate point is judged as 1 during each height screening process, and the non-ground point is judged as 0. And finally, the data points with the number of votes larger than 3 are judged as final ground points, and the rest are non-ground points.
In the above embodiment, the order of performing the screening of the derivative and the screening of the height interval is not limited, and the screening of the height interval may be followed by the screening of the second derivative to determine the final data point. However, when the height interval is screened, the sliding window of the height interval needs to count all data points so as to ensure the accuracy of the data.
In an implementation scenario, the method can be applied to urban traffic planning. For example, the digital surface model obtained in advance is a digital model of an undeveloped natural area or a developed urban area. And aiming at the digital surface model of the area, obtaining a digital elevation model which can only show the terrain information of the area after processing. Based on the terrain information of the digital elevation model, relevant planning of the area and relevant construction of the road can be further carried out, or urban construction can be carried out again according to the terrain information, so that urban layout and regional traffic are more reasonable, and the development of production and life is facilitated.
As shown in fig. 5, fig. 5 is a schematic structural diagram of an embodiment of an electronic device according to the present application.
The electronic device includes a processor 110, a memory 120.
The processor 110 controls the operation of the electronic device, and the processor 110 may also be referred to as a Central Processing Unit (CPU). The processor 110 may be an integrated circuit chip having the processing capability of signal sequences. The processor 110 may also be a general purpose processor, a digital signal sequence processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The processor 110 is configured to execute instructions to implement the methods provided by any of the embodiments and possible combinations of the aforementioned digital elevation model generation methods of the present application.
As shown in fig. 6, fig. 6 is a schematic structural diagram of an embodiment of a computer-readable storage device according to the present application.
One embodiment of the readable storage device includes a memory 210, the memory 210 storing program data that, when executed, implements the methods provided by any one and possibly combinations of the embodiments of the digital elevation model generation method of the present application.
The Memory 210 may include a medium that can store program instructions, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, or may also be a server that stores the program instructions, and the server may send the stored program instructions to other devices for operation, or may self-operate the stored program instructions.
As shown in fig. 7, fig. 7 is a schematic structural diagram of an embodiment of an electronic device according to the present application.
The electronic device comprises a control module 310, a data acquisition module 320 and a data processing module 330.
The data acquisition module 320 is used to acquire a raw data surface model. The data processing module 330 is configured to set various parameters, such as a derivative threshold, a height threshold interval, and the like, and perform a data processing process in a series of steps, such as performing smoothing filtering on the original digital surface model to obtain a filtered digital surface model, obtaining a cover height model based on the original digital surface model and the filtered digital surface model, performing height screening on each data point in the cover height model to obtain a ground candidate point, screening the derivative of the ground candidate point by using a preset derivative threshold to obtain a ground point, and obtaining the digital height model based on the ground point. The control module 310 is connected to the data acquisition module 320 and the data processing module 330 to implement any one of the embodiments and possible combinations of the above-described digital elevation model generation methods.
In summary, the filtered digital surface model is obtained by smoothing and filtering the original digital surface model, and the cover height model capable of representing the height of the cover above the ground surface is further obtained by calculating through the original digital surface model and the filtered digital surface model. And screening the heights of the data points in the covering height model to obtain ground candidate points which can be used as ground data points, further screening derivatives of the ground candidate points to remove data points which are judged to be non-ground from the ground candidate points, and finally leaving the ground candidate points as the ground points. And finally, obtaining a digital elevation model based on the ground points. Through the acquisition of the covering object height model, the further screening and derivative judgment processing of the data points in the covering object height model, the ground candidate points are divided into the ground points and the non-ground points, so that the obtained ground points are more accurate, and the digital elevation model obtained based on the ground points is more accurate.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other manners. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated units in the other embodiments described above may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings, or which are directly or indirectly applied to other related technical fields, are intended to be included within the scope of the present application.
Claims (10)
1. A method of generating a digital elevation model, the method comprising:
obtaining an original digital surface model;
carrying out smooth filtering processing on the original digital surface model to obtain a filtered digital surface model;
obtaining a cover height model based on the original digital surface model and the filtered digital surface model;
carrying out height screening processing on each data point in the covering height model to obtain ground candidate points;
screening the derivative of the ground candidate point through a preset derivative threshold value to obtain a ground point;
and obtaining the digital elevation model based on the ground points.
2. The method of claim 1, wherein said filtering said original digital surface model to obtain a filtered digital surface model comprises:
and processing the original digital surface model through two-dimensional Gaussian filtering to obtain the filtered digital surface model.
3. The method of claim 2, wherein said deriving a coverage height model based on said original digital surface model and said filtered digital surface model comprises:
subtracting the height of the corresponding data point of the filtered digital surface model from the height of each data point of the original digital surface model to obtain the height of the corresponding data point of the cover height model.
4. The method of claim 1, wherein the height screening each data point in the coverage height model to obtain candidate ground points comprises:
setting a height threshold interval;
and taking the data points with the height within the height threshold interval as the ground candidate points.
5. The method of claim 1, wherein the height screening each data point in the coverage height model to obtain candidate ground points comprises:
presetting a sliding window;
traversing the overlay height model using the sliding window;
selecting a preset proportion of data points with the lowest height from an area covered by the current position of the sliding window;
calculating the median height of the selected data points;
determining a height threshold interval by taking the median height as a center;
and taking the data points in the height threshold interval as the ground candidate points.
6. The method of claim 5, further comprising:
traversing the cover height model for multiple times through the sliding window, wherein at least one of a starting point, a sliding step length and a window size of the sliding window traversed for each time is different;
excluding data points determined as the ground candidate points for which the number of times is less than or equal to a preset number threshold after the plurality of traversals are completed.
7. The method of claim 1, wherein the screening the derivatives of the ground candidate points for ground points by a preset derivative threshold comprises:
calculating a second derivative of the ground candidate points;
and determining the ground candidate points with the second derivative smaller than or equal to a preset derivative threshold value as ground points.
8. The method according to claim 1, wherein the deriving the digital elevation model based on the ground points comprises:
and calculating the heights of other data points required by the digital elevation model by linear interpolation by utilizing the heights of the ground points.
9. An electronic device comprising a memory and a processor, the memory for storing program data executable by the processor to implement the method of any one of claims 1-8.
10. A computer-readable storage means, in which program data are stored, which can be executed by a processor to implement the method according to any one of claims 1-8.
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