CN109163701B - Method and system for measuring landform boundary, and computer-readable storage medium - Google Patents
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Abstract
The invention provides a measurement method, a measurement system and a computer-readable storage medium. The measuring method comprises the following steps: measuring a terrain factor; calculating a terrain factor standard deviation to compare the correlation of each terrain factor to obtain a correlation coefficient, and obtaining a covariance matrix from the correlation coefficient, wherein the covariance matrix comprises a variance value; calculating an optimal index according to the standard deviation and the correlation coefficient; screening the terrain factors according to the optimal indexes; selecting a plurality of initial clustering centers to perform unsupervised classified screening on the terrain factors according to the variance values and the mean values of the plurality of sub-images; and performing boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary later. The measuring method provided by the invention can improve the accuracy of landform monitoring, and further improve the measuring effect of the landform boundary.
Description
Technical Field
The invention relates to the technical field of landform science, in particular to a measuring method, a measuring system and a computer readable storage medium for a landform boundary.
Background
The division of the geomorphic type and the determination of the geomorphic boundary are the fundamental work of geomorphologic research. The Digital Elevation Model (DEM) provides a large amount of basic data for landform type division, and digital landform analysis and automatic landform type division become research hotspots. In the prior art, the DEM design realizes a method and a process for extracting and identifying a boundary of a large-area landform type.
The landform type extraction or the automatic identification and drawing of a landform boundary computer are carried out by utilizing the landform characteristic difference or a mathematical method, for example, the DEM utilizes the slope characteristic to carry out the extraction between loess plateau ditches. And extracting the ditch line by gradient variability or section curvature and adopting a mathematical morphology method. In the prior art, the adopted terrain factors are single mainly aiming at the terrain type division and the terrain boundary extraction of smaller scales. However, the landform is complicated and varied, especially for large-scale analysis, and a single landform factor cannot meet the requirement of boundary identification of the landform.
Disclosure of Invention
In view of the above problems, the present invention provides a method, a system and a computer-readable storage medium for measuring a landscape boundary, which can improve the accuracy of landscape monitoring.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for measuring a relief boundary, wherein an image of the relief boundary is generated by a computer program based on a digital elevation model, the image being composed of a plurality of sub-images, the method comprising:
measuring a plurality of terrain factors;
calculating the standard deviation of each terrain factor to compare the correlation of each terrain factor to obtain a correlation coefficient, and obtaining a covariance matrix from the correlation coefficient, wherein the covariance matrix comprises a variance value; calculating an optimal index according to the standard deviation and the correlation coefficient; screening the plurality of terrain factors according to the optimal index;
selecting a plurality of clustering centers according to the variance values and the mean values of the plurality of sub-images to unsupervised classify the screened terrain factors;
and performing boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary later.
As an optional implementation, after measuring a plurality of terrain factors and before screening the plurality of terrain factors, the measuring method comprises:
normalizing the plurality of measured terrain factors; wherein the normalization process comprises:
the method comprises the steps of subtracting the minimum pixel value of an original image from the pixel value of the original image, dividing the minimum pixel value of the original image by the maximum pixel value of the original image, and multiplying the minimum pixel value of the original image by the dimension maximum value to obtain the pixel value of a normalized image.
As an alternative embodiment, the plurality of terrain factors includes elevation, ground slope, slope variability, cumulative ground curvature, ground roughness, ground depth of cut, ground waviness, and elevation coefficient of variation.
As an alternative embodiment, in screening the plurality of terrain factors according to the optimal index, the measuring method includes:
calculating a mean value of each terrain factor, wherein the covariance matrix comprises covariance values;
setting the standard deviation to be inversely proportional to the information content of the image of the landform boundary;
the square of the variance value is obtained by subtracting the average value of the image elements of the image from the image element value of the image, and then the average value of the square value is calculated; the plurality of sub-images comprises at least a first image and a second image, the correlation being a covariance value between the first image and the second image, then divided by a standard deviation product between the first image and the second image;
the plurality of terrain factors at least comprise a first terrain factor and a second terrain factor, the optimal index is a standard deviation between the first terrain factor and the second terrain factor, then the standard deviation is divided by a correlation coefficient between the first terrain factor and the second terrain factor, and the numerical value of the optimal index is set to be in direct proportion to the information content of the image of the terrain boundary.
As an alternative embodiment, in the unsupervised classification of the screened terrain factors, the plurality of cluster centers includes at least a first cluster center, a second cluster center, a third cluster center and a fourth cluster center, and the measuring method includes:
and calculating and counting the distance between the pixel of each image and the first clustering center, then calculating a new mean value of the images, and enabling the third clustering center to be identical to the fourth clustering center and reach a convergence threshold value in a circulating iteration mode by using the second clustering center.
As an optional implementation, in processing the landform boundary after performing boundary cleaning on the unsupervised classified terrain factors, the measurement method includes:
selecting a first image value of an image of a landform boundary to cover a second image value, wherein the first image value is larger than the second image value;
selecting a first area of an image of a landform boundary to cover a second area, wherein the first area is smaller than the second area;
and selecting a majority image value in a half parameter or a mode parameter of a third area of the image of the landform boundary to cover a minority image value.
In a second aspect, the present invention provides a system for surveying a relief boundary, an image of the relief boundary being generated by a computer program based on a digital elevation model, the image being composed of a plurality of sub-images, the system comprising:
a measurement unit for measuring a plurality of terrain factors;
the screening unit is used for calculating the standard deviation of each terrain factor to compare the correlation of each terrain factor to obtain a correlation coefficient and obtain a covariance matrix from the correlation coefficient, wherein the covariance matrix comprises a variance value; calculating an optimal index according to the standard deviation and the correlation coefficient; the screening unit screens the plurality of terrain factors according to the optimal index;
the classification unit is used for selecting a plurality of clustering centers according to the variance values and the mean values of the plurality of sub-images to unsupervised classify the screened terrain factors;
and the processing unit is used for carrying out boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary in a later period.
As an optional embodiment, the measurement system comprises:
the calculation unit is used for carrying out normalization processing on the plurality of measured terrain factors; wherein the normalization process comprises:
the method comprises the steps of subtracting the minimum pixel value of an original image from the pixel value of the original image, dividing the minimum pixel value of the original image by the maximum pixel value of the original image, and multiplying the minimum pixel value of the original image by the dimension maximum value to obtain the pixel value of a normalized image.
As an optional implementation, the screening unit calculates a mean value of each terrain factor, wherein the covariance matrix includes a covariance value; the screening unit sets the standard deviation to be inversely proportional to the information content of the image of the landform boundary;
the square of the variance value is obtained by subtracting the average value of the image elements of the image from the image element value of the image, and then the average value of the square value is calculated; the plurality of sub-images comprises at least a first image and a second image, the correlation being a covariance value between the first image and the second image, then divided by a standard deviation product between the first image and the second image;
the plurality of terrain factors at least comprise a first terrain factor and a second terrain factor, the optimal index is a standard deviation between the first terrain factor and the second terrain factor, then the standard deviation is divided by a correlation coefficient between the first terrain factor and the second terrain factor, and the numerical value of the optimal index is set to be in direct proportion to the information content of the image of the terrain boundary.
In a third aspect, the present invention provides a computer-readable storage medium having a memory storing a computer program for use in the method for measuring a topographical boundary as described above.
According to the measuring method, the measuring system and the computer readable storage medium provided by the invention, the landform boundary can be accurately calculated, so that the detection efficiency is improved. The accuracy is processed by the digital elevation model to make a topographical boundary, and a plurality of topographical causes are measured by the measurement unit. The calculation unit unifies the dimensions of the terrain factors and performs normalization processing. And calculating to obtain the optimal terrain factor combination by the screening unit according to the terrain factors with higher screening correlation. The optimal terrain factor combination is fused by the classification unit through loop iteration. And the processing unit is used for cleaning the boundary through post-processing to remove burrs and eliminate the hollow holes. Therefore, the accuracy of measuring the landform boundary can be improved by implementing the technical scheme of the invention, and simultaneously, a plurality of landform factors are considered and the optimal landform factor combination is fused through screening loop iteration, so that the accuracy of measuring the landform boundary is improved.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope of the present invention.
FIG. 1 is a schematic flow chart of a method for measuring a topographic boundary according to embodiment 1 of the present invention;
FIG. 2 is a flow chart of a method for measuring a topographic boundary according to embodiment 2 of the present invention;
fig. 3 is a block diagram of a measurement system for providing a topographical boundary according to embodiment 3 of the present invention.
Detailed Description
Reference will now be made in detail to the embodiments of the present invention, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
To solve the problems in the prior art, the present invention provides a method, a system and a computer readable storage medium for measuring a landform boundary; processing accuracy according to the digital elevation model to manufacture a landform boundary, and calculating to obtain an optimal landform factor combination according to screening of landform factors with high correlation; fusing the optimal terrain factor combination through loop iteration; boundary cleaning is performed by post-processing to remove burrs and eliminate voids. Therefore, the technical scheme of the invention can improve the accuracy of measuring the landform boundary, simultaneously considers a plurality of terrain factors and fuses the optimal terrain factor combination through screening loop iteration, thereby improving the real-time performance and the accuracy of environment measurement. Also, the techniques may be implemented in associated software or hardware, as described below by way of example.
Example 1
Referring to fig. 1, fig. 1 is a flow chart illustrating a method for measuring a topographic border (hereinafter referred to as "measuring method") according to embodiment 1 of the present invention, wherein an image of the topographic border is generated by a computer program based on a Digital Elevation Model (DEM), and the image is composed of a plurality of sub-images. As shown in fig. 1, the measurement method includes the steps of:
and S101, measuring a plurality of terrain factors.
In this embodiment, the plurality of terrain factors may include elevation, ground slope, slope variability, cumulative ground curvature, ground roughness, ground depth of cut, ground waviness, and elevation coefficient of variation. Further, elevation represents the distance from a point on the ground to the absolute base in the direction of the plumb line. The ground slope represents the included angle formed by the height difference connecting line and the horizontal distance between two different points on the ground. The ground slope represents the rate of change of the ground in differential space. The cumulative curvature of the ground represents the degree of change in distortion at a point on the ground. The ground roughness represents the ground roughness and has characteristic parameters with length dimension, the range between the characteristic parameters with length dimension is fixed, the characteristic parameters with length dimension are known and can be checked, the detailed description is omitted in the embodiment of the invention, and the acquisition of the ground roughness in the embodiment of the invention can be acquired from a network or a memory built in a measurement system on a near-earth satellite. The surface cut depth, which represents the difference between the average elevation of a neighborhood of a point on the surface and the minimum elevation within the neighborhood, may indicate an eroded cut of the surface. Relief represents the difference between the highest elevation and the lowest elevation in a particular area. The elevation coefficient of variation represents the degree of variation of a point on the ground along the direction of the plumb line.
S103, screening a plurality of terrain factors according to the optimal indexes. Calculating the standard deviation of each terrain factor to compare the correlation of each terrain factor to obtain a correlation coefficient, and obtaining a covariance matrix from the correlation coefficient, wherein the covariance matrix comprises a variance value; and calculating an optimal index according to the standard deviation and the correlation coefficient.
In this embodiment, the mean is a rough value of each terrain factor, and the standard deviation is a discrete value of each terrain factor. Wherein the standard deviation is inversely proportional to the amount of information in the image of the relief boundary. For example, the standard deviation may be used to indicate the amount of information of the image of the feature boundary, and when the standard deviation is greater than the first threshold, the gray scale of the image representing the feature boundary is more dispersed and the gray scale contrast is greater, i.e., more information of the image is obtained. Conversely, when the standard deviation is lower than the second threshold, the image representing the feature boundary has a single gray level and a small gray level contrast, resulting in a smaller amount of information of the image. The first threshold is greater than the second threshold. Firstly, calculating the standard deviation of elevation, ground gradient, gradient variability, ground accumulated curvature, ground roughness, ground cutting depth, ground relief and elevation variation coefficient, and then sequentially representing the multi-degree arrangement of the information content of the image according to the standard deviation height arrangement of all terrain factors.
In this embodiment, a mean value for each terrain factor is calculated, wherein the covariance matrix comprises covariance values. The standard deviation is set inversely proportional to the amount of information in the image of the relief boundary. The square of the variance value is obtained by subtracting the average value of the image elements of the image from the image element value of the image, and then the average value of the square value is calculated; the plurality of sub-images includes at least a first image and a second image, and the correlation is a covariance value between the first image and the second image, and is then divided by a standard deviation product between the first image and the second image. The correlation is between-1 and 1, and the present invention is not limited by this numerical range. In detail, because there is redundancy among the calculated elevation, the ground gradient, the gradient variability, the ground cumulative curvature, the ground roughness, the ground cutting depth, the ground waviness and the elevation variation coefficient, it is necessary to calculate the correlation coefficient among the terrain factors, and the redundancy among the terrain factors can be obtained through a correlation coefficient matrix formed by the correlation coefficients. For example, the remaining elements in the covariance matrix are the covariance between all input grid pairs. The covariance between image i and image j can be determined using the following equation:
in the formula, Z is a pixel value, i and j are stacked images, mu is an image average value, N is the number of pixels, and k represents a specific pixel. The covariance between the first image and the second image is the intersection of the respective row and column. Since the covariance between the first image and the second image is the same as the covariance between the second image and the first image, the covariance matrix values are all dependent only on the unit of value, and thus the covariance matrix values do not have the notion of vectors. The correlation matrix displays a correlation coefficient value, and the relationship between the two data sets can be seen through the correlation coefficient value, namely the data sets can be judged to be high-correlation or low-correlation through the correlation coefficient value. For a set of raster images, the correlation matrix indicates that the pixel values in a raster image are of high or low correlation with the pixel values of another image. The correlation between the images may be used to measure the dependency between the first image and the second image. The correlation is a ratio of a covariance value between the first image and the second image to a product of a standard deviation between the first image and the second image, i.e., a covariance value between the first image and the second image divided by a product of a standard deviation between the first image and the second image. The correlation is a ratio, with no units. The formula for the correlation is as follows:
for example, a positive correlation indicates that the variation relationship between the first image and the second image is the same, and when the pixel value of the first image is decreased, the pixel value of the second image is also decreased relatively; when the pel value of the first image increases, the pel value of the second image also increases relatively. The negative correlation indicates that the variation relationship between the first image and the second image is different, and the pixel value of the second image is relatively increased when the pixel value of the first image is decreased; as the pel value of the first image increases, the pel value of the second image relatively decreases. A correlation of zero indicates that there is no positive or negative correlation between the first image and the second image.
In this embodiment, the optimal index is a standard deviation between the first terrain factor and the second terrain factor, and then divided by a correlation coefficient between the first terrain factor and the second terrain factor, and a value of the optimal index is proportional to an information amount of the image. The best index may also be referred to as OIF (optimal index factor). The optimal index is calculated as follows:
Siis the standard deviation of the ith terrain factor, RijIs the correlation coefficient between the two terrain factors i and j. The optimal exponent is inversely proportional to the sum of the correlation coefficients of each terrain factor image, and the optimal exponent is proportional to the standard deviation between terrain factors. The larger the optimal index is, the smaller the redundancy between index factors is and the larger the information amount of the image is. Conversely, the smaller the optimal index, the greater the redundancy between the index factors and the smaller the amount of information in the image.
And S105, selecting a plurality of clustering centers to perform unsupervised classified screening on the terrain factors according to the variance values and the mean values of the plurality of sub-images, wherein the plurality of clustering centers at least comprise a first clustering center, a second clustering center, a third clustering center and a fourth clustering center.
In this embodiment, the distance between each pixel and the first clustering center is calculated and counted, then a new mean value of the image is calculated to serve as the second clustering center, and the third clustering center is made to be the same as the fourth clustering center in a loop iteration manner and reaches the convergence threshold. The optimal combined terrain factors are subjected to unsupervised classification based on Iterative Self Organizing Data Analysis Technology (ISODATA). The following equation is used for selecting n initial clustering centers according to the mean value M and the variance sigma of the image:
and classifying the calculation result into the closest category by calculating the distance between the pixel and the first clustering center. And then recalculating the new mean value of each category to serve as a second clustering center, continuing to perform loop iteration until the loop time reaches the maximum iteration time, and comparing the clustering results of the last two times (namely the third clustering center and the fourth clustering center) to keep unchanged to reach a loop convergence threshold value.
And S107, performing boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary in the later period. The landform entity obtained through unsupervised classification has burrs, low pixel contrast, holes and the like to a certain degree on the image. The extraction of the landform entity and the landform boundary is completed by removing broken spots, increasing the resolution, eliminating burrs, eliminating cavities and the like. And then using boundary cleaning and main filtering to carry out smoothing treatment on the region edge.
The boundary cleaning is mainly used for cleaning irregular edges among areas. The digital elevation model cleans the boundary over a large range using expansion and contraction methods. Firstly, the higher priority area covers the adjacent lower priority area from all directions, and one image element is taken as the coverage size. Wherein a high image value may be overlaid with a low image value, and a high image value may be overlaid with a high image value.
In this embodiment, a first image value of the image of the landform boundary is selected to be overlaid to a second image value, and the first image value is greater than the second image value. In other words, regions with larger image values may be selected to be overlaid with higher priority to regions with smaller image values. The schematic table is as follows:
in this embodiment, a first area of the image of the landform boundary is selected to cover a second area, and the first area is smaller than the second area. In other words, a first region of smaller area may be selected to be higher priority coverage to a second region of larger area. The schematic table is as follows:
in this embodiment, most image values in the half-number parameter or the mode parameter of the third area of the image of the landform boundary are selected to cover a few image values. In particular, the primary filtering may replace a pel based on a number of values in the neighborhood of the pel. The primary filtering can only be replaced when two conditions (half-number or mode) need to be met. First, the number of equal-valued neighboring picture elements must be as large as the mode value, or at least half of the picture elements must have the same value. Among the mode parameters, three quarters or four sixths or five eighths of the connected pixels must have the same value. In the half parameter, two-quarters or three-sixths or four-eighths of the connected pixels are required to have the same value. Second, the pixels within a region must be adjacent to the center of the specified filter (e.g., three quarters of the pixels must be the same). The purpose of the half or mode parameter is to minimize the degree of corruption of the spatial pattern of picture elements within a region. If the condition of the half number parameter or the mode parameter is not met, the replacement will not be carried out, and the value of the pixel will be kept unchanged. The main filtering is applied to the input grid, using the last four pixels as filters, and requiring a mode parameter to change the value of the corresponding pixel. And changing the image elements surrounded by three or more adjacent image elements with the same value. The schematic table is as follows:
example 2
Referring to fig. 2, fig. 2 is a flowchart illustrating a method for measuring a topographic boundary according to embodiment 2 of the present invention. As shown in fig. 2, the measurement method includes the steps of:
s201, measuring a plurality of terrain factors.
S202, normalization processing is carried out on the measured multiple terrain factors.
S203, screening a plurality of terrain factors.
S205, unsupervised classification of a plurality of terrain factors.
And S207, post-processing.
For the related descriptions of S201, S203, S205, and S207, refer to the related description of embodiment 1, which is not repeated herein.
In S202, after measuring the plurality of terrain factors and before screening the plurality of terrain factors, the measuring method includes:
unifying the dimension of each terrain factor and carrying out normalization processing; the normalization processing is to subtract the minimum pixel value of the original image from the pixel value of the original image, divide the minimum pixel value of the original image by the maximum pixel value of the original image and then multiply the minimum pixel value of the original image by the dimension maximum value to obtain the pixel value of the normalized image.
In this embodiment, in order to avoid that at least one of the elevation, the ground slope, the slope variability, the ground cumulative curvature, the ground roughness, the ground cutting depth, the ground waviness, and the elevation variation coefficient exceeds a threshold, the 8 terrain factors are normalized. And (4) adopting the contrast stretching of the minimum pixel value minus the maximum pixel value of the image to keep the correlation degree between the original terrain factor information. The value of the output topographic factor image is calculated by:
BVoutis the value of the original topographic factor image, minkAnd maxkRespectively representing the minimum and maximum pixel values, quant, in the original imagekFor the maximum dimension, the most 8-bit data type is taken hereLarge value 255 and minimum value 0.
Example 3
Referring to fig. 3, fig. 3 is a block diagram of a measurement system (hereinafter referred to as "measurement system") for providing a topographic border according to embodiment 3 of the present invention. An image of the relief boundary is made by a computer program based on the digital elevation model. As shown in fig. 3, the measurement system 300 includes:
a measuring unit 301 for measuring a plurality of terrain factors.
A screening unit 302, configured to calculate a standard deviation of each terrain factor to compare correlations of each terrain factor to obtain a correlation coefficient, and obtain a covariance matrix from the correlation coefficient, where the covariance matrix includes a variance value; calculating an optimal index according to the standard deviation and the correlation coefficient; and screening a plurality of terrain factors according to the optimal index.
And the classification unit 303 is configured to select a plurality of clustering centers according to the variance values and the mean values of the plurality of sub-images to perform unsupervised classification and screening on the terrain factors.
And the processing unit 304 is used for performing boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary in a later period.
A calculation unit 305 for performing normalization processing on the plurality of measured terrain factors; wherein the normalization process comprises:
the method comprises the steps of subtracting the minimum pixel value of an original image from the pixel value of the original image, dividing the minimum pixel value of the original image by the maximum pixel value of the original image, and multiplying the minimum pixel value of the original image by the dimension maximum value to obtain the pixel value of a normalized image.
The screening unit 302 calculates a mean value of each terrain factor, wherein the covariance matrix includes a covariance value; the screening unit 302 sets the standard deviation to be inversely proportional to the information content of the image of the landform boundary;
the square of the variance value is obtained by subtracting the average value of the image elements of the image from the image element value of the image, and then the average value of the square value is calculated; the plurality of sub-images including at least a first image and a second image, the correlation being a covariance value between the first image and the second image, divided by a standard deviation product between the first image and the second image;
the plurality of terrain factors at least comprise a first terrain factor and a second terrain factor, the optimal index is the standard deviation between the first terrain factor and the second terrain factor, then the standard deviation is divided by the correlation coefficient between the first terrain factor and the second terrain factor, and the numerical value of the optimal index is set to be in direct proportion to the information content of the image of the landform boundary.
It can be seen that the measurement system described in fig. 3 can accurately calculate the topographic boundary, thereby improving the detection efficiency. On the other hand, accuracy is processed according to the digital elevation model to make a relief boundary, and a plurality of terrain causes are measured by the measurement unit. The calculation unit unifies the dimensions of the terrain factors and performs normalization processing. And calculating to obtain the optimal terrain factor combination by the screening unit according to the terrain factors with higher screening correlation. The optimal terrain factor combination is fused by the classification unit through loop iteration. The boundaries are cleaned by post-processing by a processing unit to remove burrs and eliminate voids. Therefore, the accuracy of measuring the landform boundary can be improved by implementing the technical scheme of the invention, and meanwhile, the optimum landform factor combination is fused by considering the landform factors such as height, ground gradient, gradient variability, ground cumulative curvature, ground roughness, ground cutting depth, ground waviness and elevation variation coefficient through screening loop iteration, so that the accuracy of measuring the landform boundary is improved.
The memory may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the mobile terminal, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device.
The present embodiment also provides a computer-readable storage medium having a memory storing a computer program used by the above-described method for measuring a topographic boundary.
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative and, for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part of the technical solution that contributes to the prior art in essence can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a smart phone, a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method according to the embodiments of the present invention. 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 for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method for surveying a relief boundary, an image of the relief boundary being generated by a computer program based on a digital elevation model, the image being composed of a plurality of sub-images, the method comprising:
measuring a plurality of terrain factors;
calculating the standard deviation of each terrain factor to compare the correlation of each terrain factor to obtain a correlation coefficient, and obtaining a covariance matrix from the correlation coefficient, wherein the covariance matrix comprises a variance value; calculating an optimal index according to the standard deviation and the correlation coefficient; screening the plurality of terrain factors according to the optimal index;
selecting a plurality of clustering centers according to the variance values and the mean values of the plurality of sub-images to unsupervised classify the screened terrain factors;
carrying out boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary later;
the screening the plurality of terrain factors according to the optimal index comprises:
calculating a mean value of each terrain factor, wherein the covariance matrix comprises covariance values;
setting the standard deviation to be inversely proportional to the information content of the image of the landform boundary;
the square of the variance value is obtained by subtracting the average value of the image elements of the image from the image element value of the image, and then the average value of the square value is calculated; the plurality of sub-images comprises at least a first image and a second image, the correlation being a covariance value between the first image and the second image, then divided by a standard deviation product between the first image and the second image;
the plurality of terrain factors at least comprise a first terrain factor and a second terrain factor, the optimal index is a standard deviation between the first terrain factor and the second terrain factor, then the standard deviation is divided by a correlation coefficient between the first terrain factor and the second terrain factor, and the numerical value of the optimal index is set to be in direct proportion to the information content of the image of the terrain boundary.
2. A method of measurement according to claim 1, wherein after measuring a plurality of terrain factors and before screening the plurality of terrain factors, the method of measurement comprises:
normalizing the plurality of measured terrain factors; wherein the normalization process comprises:
the method comprises the steps of subtracting the minimum pixel value of an original image from the pixel value of the original image, dividing the minimum pixel value of the original image by the maximum pixel value of the original image, and multiplying the minimum pixel value of the original image by the dimension maximum value to obtain the pixel value of a normalized image.
3. The method of claim 1, wherein the plurality of terrain factors includes elevation, ground slope, slope variability, cumulative ground curvature, ground roughness, ground depth of cut, ground waviness, and elevation coefficient of variation.
4. The measurement method according to claim 1, wherein in unsupervised classification of the screened terrain factors, the plurality of cluster centers includes at least a first cluster center, a second cluster center, a third cluster center, and a fourth cluster center, the measurement method comprising:
and calculating and counting the distance between the pixel of each image and the first clustering center, then calculating a new mean value of the images to serve as the second clustering center, and enabling the third clustering center to be identical to the fourth clustering center in a circulating iteration mode and to reach a convergence threshold value.
5. The measurement method according to claim 1, wherein in processing the relief boundary after boundary cleaning of the unsupervised classified terrain factors, the measurement method comprises:
selecting a first image value of an image of a landform boundary to cover a second image value, wherein the first image value is larger than the second image value;
selecting a first area of an image of a landform boundary to cover a second area, wherein the first area is smaller than the second area;
and selecting a majority image value in a half parameter or a mode parameter of a third area of the image of the landform boundary to cover a minority image value.
6. A survey system for a relief boundary, an image of the relief boundary being created by a computer program based on a digital elevation model, the image being composed of a plurality of sub-images, the survey system comprising:
a measurement unit for measuring a plurality of terrain factors;
the screening unit is used for calculating the standard deviation of each terrain factor to compare the correlation of each terrain factor to obtain a correlation coefficient and obtain a covariance matrix from the correlation coefficient, wherein the covariance matrix comprises a variance value; calculating an optimal index according to the standard deviation and the correlation coefficient; the screening unit screens the plurality of terrain factors according to the optimal index;
the classification unit is used for selecting a plurality of clustering centers according to the variance values and the mean values of the plurality of sub-images to unsupervised classify the screened terrain factors;
the processing unit is used for carrying out boundary cleaning on the unsupervised classified terrain factors and processing the landform boundary in a later period;
the screening unit is further configured to calculate a mean value of each terrain factor, wherein the covariance matrix includes a covariance value;
the screening unit is also used for setting the inverse proportion of the standard deviation and the information quantity of the image of the landform boundary;
the square of the variance value is obtained by subtracting the average value of the image elements of the image from the image element value of the image, and then the average value of the square value is calculated; the plurality of sub-images comprises at least a first image and a second image, the correlation being a covariance value between the first image and the second image, then divided by a standard deviation product between the first image and the second image;
the plurality of terrain factors at least comprise a first terrain factor and a second terrain factor, the optimal index is a standard deviation between the first terrain factor and the second terrain factor, then the standard deviation is divided by a correlation coefficient between the first terrain factor and the second terrain factor, and the numerical value of the optimal index is set to be in direct proportion to the information content of the image of the terrain boundary.
7. The measurement system of claim 6, further comprising:
the calculation unit is used for carrying out normalization processing on the plurality of measured terrain factors; wherein the normalization process comprises:
the method comprises the steps of subtracting the minimum pixel value of an original image from the pixel value of the original image, dividing the minimum pixel value of the original image by the maximum pixel value of the original image, and multiplying the minimum pixel value of the original image by the dimension maximum value to obtain the pixel value of a normalized image.
8. A computer-readable storage medium having a memory, characterized in that it stores a computer program for use in the method of measuring a relief boundary of any one of claims 1 to 5.
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