CN109163701A - Measurement method, measuring system and the computer readable storage medium on landforms boundary - Google Patents
Measurement method, measuring system and the computer readable storage medium on landforms boundary Download PDFInfo
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Abstract
The present invention provides a kind of measurement method, measuring system and computer readable storage medium.Measurement method includes: the factor of surveying the topography；It calculates terrain factor standard deviation and related coefficient is obtained with the correlation of more each terrain factor, and covariance matrix is obtained by related coefficient, wherein covariance matrix includes variance yields；Optimum index is calculated according to standard deviation and related coefficient；Terrain factor is screened according to optimum index；The terrain factor that multiple initial cluster centers screen come unsupervised classification is chosen according to the mean value of variance yields and multiple subgraphs；Boundary is carried out to the terrain factor of the unsupervised classification to clear up with landforms boundary described in post-processing.Measurement method provided by the invention can propose the accuracy of landforms monitoring, and then improve the measurement effect on landforms boundary.
Description
Technical field
The present invention relates to geomorphology's technical fields, in particular to measurement method, the measuring system on a kind of landforms boundary
And computer readable storage medium.
Background technique
The division of geomorphic type and the determination on landforms boundary are the element tasks of geomorphology's research.Digital elevation model
(DEM, digital elevation module) provides a large amount of basic data, digital land value model analysis for relief type zone
Automatic with geomorphic type is divided into research hotspot.In the prior art, DEM design is realized for big regional landforms type
Extract the method and process with Boundary Recognition.
Using features of terrain difference or mathematical method is used, geomorphic type extraction is carried out or landforms feature modeling machine is known automatically
It does not draw, for example, DEM carries out the extraction between the ditch of loess plateau using gradient feature.By gradient variability or profile curvature,
It is extracted along ditch using Mathematical Morphology Method.Relief type zone, landforms side in the prior art, mainly for smaller scale
Boundary extracts, and the terrain factor of use is more single.However, landforms are complicated and changeable, the especially morphological analyses of large scale, it is single
One terrain factor has been unable to meet the requirement of landforms Boundary Recognition.
Summary of the invention
In view of the above problems, the present invention provides a kind of measurement methods on landforms boundary, measuring system and computer-readable
Storage medium can be improved the accuracy of landforms monitoring.
To achieve the goals above, the present invention adopts the following technical scheme that:
In a first aspect, the present invention provides a kind of measurement methods on landforms boundary, calculating is passed through based on digital elevation model
The image on landforms boundary described in machine program making, described image are made of multiple subgraphs, and the measurement method includes:
Measure multiple terrain factors；
It calculates the standard deviation of each terrain factor and related coefficient is obtained with the correlation of more each terrain factor, and by the phase
Relationship number obtains covariance matrix, wherein the covariance matrix includes variance yields；According to the standard deviation and the phase relation
Number calculates optimum index；The multiple terrain factor is screened according to the optimum index；
Multiple cluster centres are chosen according to the mean value of the variance yields and the multiple subgraph come described in unsupervised classification
The terrain factor of screening；
Boundary is carried out to the terrain factor of the unsupervised classification to clear up with landforms boundary described in post-processing.
As an alternative embodiment, after measuring multiple terrain factors, and the multiple landform of screening because
Before son, the measurement method includes:
Multiple terrain factors of the measurement are normalized；Wherein, the normalized includes:
The minimum pixel value that original image is first subtracted by the pixel value of original image, then divided by original image
Maximum pixel value subtracts the minimum pixel value of original image, then obtains the pixel of normalized image multiplied by dimension maximum value
Value.
As an alternative embodiment, the multiple terrain factor include elevation, ground line gradient, gradient variability,
Face adds up curvature, surface roughness, ground depth of cut, surface relief degree and the elevation coefficient of variation.
As an alternative embodiment, screened in the multiple terrain factor according to the optimum index, it is described
Measurement method includes:
Calculate the mean value of each terrain factor, wherein the covariance matrix includes covariance value；
The information content that the image on the standard deviation and landforms boundary is arranged is inversely proportional；
Wherein, variance yields square for image pixel value subtracted image pixel average value after be squared value, then count
Calculate the average value of the square value；Multiple subgraphs include at least the first image and the second image, and the correlation is described the
Covariance value between one image and second image, then divided by the standard deviation between the first image and second image
Product；
Wherein, multiple terrain factors include at least the first terrain factor and the second terrain factor, and the optimum index is institute
The standard deviation between the first terrain factor and second terrain factor is stated, then divided by first terrain factor and described second
Related coefficient between terrain factor, be arranged the optimum index numerical value be proportional to the landforms boundary image information content.
As an alternative embodiment, in the terrain factor of the screening described in unsupervised classification, multiple cluster centres
Including at least the first cluster centre, the second cluster centre, third cluster centre and the 4th cluster centre, the measurement method packet
It includes:
Calculate and count the pixel of each image and the distance of first cluster centre, then calculate image new mean value with
Second cluster centre, by loop iteration mode so that the third cluster centre is identical to the 4th cluster centre simultaneously
Reach convergence threshold values.
It clears up as an alternative embodiment, carrying out boundary in the terrain factor to the unsupervised classification with the later period
It handles in the landforms boundary, the measurement method includes:
The first image value for choosing the image on landforms boundary covers the second image value, wherein the first image value is big
In second image value；
The first area for choosing the image on landforms boundary covers second area, wherein the first area is less than described
Second area；
The half parameter or most image values in mode parameter for choosing the third region of the image on the landforms boundary are covered
Cover a small number of image values.
Second aspect, the present invention provides a kind of measuring systems on landforms boundary, pass through calculating based on digital elevation model
The image on landforms boundary described in machine program making, described image are made of multiple subgraphs, and the measuring system includes:
Measuring unit, for measuring multiple terrain factors；
Screening unit obtains phase relation for calculating the standard deviation of each terrain factor with the correlation of more each terrain factor
Number, and covariance matrix is obtained by the related coefficient, wherein the covariance matrix includes variance yields；According to the standard
The poor and described related coefficient calculates optimum index；Screening unit screens the multiple terrain factor according to the optimum index；
Taxon, for choosing multiple cluster centres according to the mean value of the variance yields and the multiple subgraph come non-
The terrain factor of screening described in supervised classification；
Processing unit carries out boundary for the terrain factor to the unsupervised classification and clears up with landforms described in post-processing
Boundary.
As an alternative embodiment, the measuring system includes:
Computing unit, for multiple terrain factors of the measurement to be normalized；Wherein, at the normalization
Reason includes:
The minimum pixel value that original image is first subtracted by the pixel value of original image, then divided by original image
Maximum pixel value subtracts the minimum pixel value of original image, then obtains the pixel of normalized image multiplied by dimension maximum value
Value.
As an alternative embodiment, screening unit calculates the mean value of each terrain factor, wherein the covariance square
Battle array includes covariance value；The information content that the image on the standard deviation and landforms boundary is arranged in screening unit is inversely proportional；
Wherein, variance yields square for image pixel value subtracted image pixel average value after be squared value, then count
Calculate the average value of the square value；Multiple subgraphs include at least the first image and the second image, and the correlation is described the
Covariance value between one image and second image, then divided by the standard deviation between the first image and second image
Product；
Wherein, multiple terrain factors include at least the first terrain factor and the second terrain factor, and the optimum index is institute
The standard deviation between the first terrain factor and second terrain factor is stated, then divided by first terrain factor and described second
Related coefficient between terrain factor, be arranged the optimum index numerical value be proportional to the landforms boundary image information content.
The third aspect, the present invention provides a kind of computer readable storage mediums, have memory, are stored with above-mentioned ground
Computer program used in the measurement method on looks boundary.
Measurement method, measuring system and the computer readable storage medium provided according to the present invention, can be precisely calculated
Landforms boundary is obtained, to improve detection efficiency.Accuracy is handled by digital elevation model to make landforms boundary, by surveying
Amount unit come measure multiple landform because.Unified the dimension of each terrain factor by computing unit and is normalized.By screening
Best landform combinations of factors is calculated according to the screening higher terrain factor of correlation in unit.Circulation is passed through by taxon
Iteration merges best landform combinations of factors.It is cleared up come boundary by post-processing to remove Burr removal and eliminate empty by processing unit
Hole.As it can be seen that implementing technical solution of the present invention can be improved the accuracy on measurement landforms boundary, while considering multiple terrain factors
And best landform combinations of factors is merged by screening loop iteration, to improve the accuracy on measurement landforms boundary.
To enable the above objects, features and advantages of the present invention to be clearer and more comprehensible, preferred embodiment is cited below particularly, and cooperate
Appended attached drawing, is described in detail below.
Detailed description of the invention
In order to illustrate the technical solution of the embodiments of the present invention more clearly, below will be to needed in the embodiment attached
Figure is briefly described, it should be understood that the following drawings illustrates only certain embodiments of the present invention, therefore is not construed as pair
The restriction of the scope of the invention.
Fig. 1 is the flow diagram for the measurement method that the embodiment of the present invention 1 provides landforms boundary；
Fig. 2 is the flow diagram for the measurement method that the embodiment of the present invention 2 provides landforms boundary；
Fig. 3 is the block schematic diagram for the measuring system that the embodiment of the present invention 3 provides landforms boundary.
Specific embodiment
The embodiment of the present invention is described below in detail, in which the same or similar labels are throughly indicated same or like
Element or element with the same or similar functions.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include one or more feature.In the description of the present invention, the meaning of " plurality " is two or more, remove
It is non-separately to have clearly specific restriction.
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
Below in conjunction with attached drawing in the embodiment of the present invention, technical solution in the embodiment of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Usually exist
The component of the embodiment of the present invention described and illustrated in attached drawing can be arranged and be designed with a variety of different configurations herein.Cause
This, is not intended to limit claimed invention to the detailed description of the embodiment of the present invention provided in the accompanying drawings below
Range, but it is merely representative of selected embodiment of the invention.Based on the embodiment of the present invention, those skilled in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
For the problems of the prior art, the present invention provides measurement method, measuring system and the meters on a kind of landforms boundary
Calculation machine readable storage medium storing program for executing；Accuracy is handled according to digital elevation model to make landforms boundary, and according to screening correlation
Best landform combinations of factors is calculated in higher terrain factor；Best landform combinations of factors is merged by loop iteration；It is logical
It crosses post-processing and carrys out boundary cleaning to remove Burr removal and eliminate cavity.As it can be seen that implementing technical solution of the present invention can be improved survey
The accuracy on landforms boundary is measured, while considering multiple terrain factors and best terrain factor is merged by screening loop iteration
Combination, to improve the real-time and accuracy of environment measurement.Also, the technology can be real using relevant software or hardware
It is existing, it is described below by embodiment.
Embodiment 1
Referring to Fig. 1, Fig. 1 is that the embodiment of the present invention 1 provides measurement method (hereinafter referred to as " the measurement side on landforms boundary
Method ") flow diagram, computer program is passed through based on digital elevation model (DEM, digital elevationmodule)
The image on landforms boundary is made, image is made of multiple subgraphs.As shown in Figure 1, measurement method the following steps are included:
S101, the multiple terrain factors of measurement.
In the present embodiment, multiple terrain factors may include elevation, ground line gradient, gradient variability, ground add up curvature,
Surface roughness, ground depth of cut, surface relief degree and the elevation coefficient of variation.Furthermore, it is understood that elevation indicates ground point edge
Distance of the plumb line direction to absolute datum.Ground line gradient indicates difference in height line and its horizontal distance between the difference ground Liang Ge
Formed angle.Ground line gradient indicates ground in the change rate in differential space.Ground, which adds up curvature, indicates the torsion of ground certain point
Bent variation degree.Surface roughness indicates ground degree of roughness and the characteristic parameter with length dimension, and range is between with length
It is fixed for measuring the size of the characteristic parameter of guiding principle, is the known characteristic parameter with length dimension looked into, to this present invention
It does not repeat in embodiment, and the acquisition of surface roughness can be obtained from network in the embodiment of the present invention, or
It is to be obtained from the memory built in the measuring system on near-earth satellite.Earth's surface depth of cut represents ground neighborhood of a point range
Dispersed elevation and the minimum elevation in the contiguous range difference, earth's surface depth of cut can show earth's surface and be etched cutting
The case where.Topographic relief amplitude indicates the difference of highest point height above sea level and minimum point height above sea level in specific region.Elevation
The coefficient of variation indicates ground point along the variation degree in plumb line direction.
S103, multiple terrain factors are screened according to optimum index.The standard deviation of each terrain factor is calculated with more each landform
The correlation of the factor obtains related coefficient, and obtains covariance matrix by related coefficient, wherein covariance matrix includes variance
Value；Optimum index is calculated according to standard deviation and the related coefficient.
In the present embodiment, mean value is the approximate value of each terrain factor, and standard deviation is the discrete value of each terrain factor.Its
In, the information content of the image on standard deviation and landforms boundary is inversely proportional.For example, standard deviation can be used to indicate that landforms boundary
The number of the information content of image, when standard deviation be greater than the first threshold values when, represent the image on landforms boundary gray scale it is more dispersed and
Gray scale contrast is larger, the information content of you can get it more image.Opposite, when standard deviation is lower than the second threshold values, represent landforms
The gray scale of the image on boundary is more single and gray scale contrast is more small, obtains the information content of less image.First threshold values is greater than the
Two threshold values.Firstly, calculate elevation, ground line gradient, gradient variability, ground add up curvature, surface roughness, ground depth of cut,
The standard deviation of surface relief degree and the elevation coefficient of variation is then arranged according to the standard deviation height of each terrain factor, is sequentially represented
The number of the information content of image arranges.
In the present embodiment, the mean value of each terrain factor is calculated, wherein covariance matrix includes covariance value.Setting mark
The information content of the image on quasi- difference and landforms boundary is inversely proportional.Variance yields square for the pixel of the pixel value subtracted image of image it is flat
It is squared value after mean value, then calculates the average value of square value；Multiple subgraphs include at least the first image and the second image, phase
Covariance value of the closing property between the first image and the second image, then multiplies divided by the standard deviation between the first image and the second image
Product.For correlation between -1~1, the present invention is not limitation with this digital scope.Specifically, due to calculate elevation, ground line gradient,
Gradient variability, ground add up to exist between curvature, surface roughness, ground depth of cut, surface relief degree and the elevation coefficient of variation
There is redundancy, it is therefore desirable to calculate the related coefficient between above-mentioned terrain factor, and correlation matrix is formed by by related coefficient
Redundancy between you can get it each terrain factor.For example, the remaining member in covariance matrix is between all input rasters pair
Covariance.The available lower formula of covariance between image i and image j determines:
In formula, Z is pixel value, and i, j are to stack image, and μ is image averaging value, and N is the quantity of pixel, and k indicates specific
Pixel.Covariance between first image and the second image is the intersection point of corresponding row and column.Due between the first image and the second image
Covariance and the second image and the first image between covariance be it is identical, covariance matrix value is all solely dependent upon the list of value
Position, so covariance matrix value and the concept there is no vector.Correlation matrix shows correlation coefficient value, passes through correlation coefficient value
Can be seen that the relationship between two datasets, i.e., by correlation coefficient value may determine that data set between belong to high related or low correlation.
For one group of grating image, correlation matrix indicate pixel value and the pixel value of another image in grating image belong to height it is related or
Low correlation.Correlation between image can be used for measuring the dependence between the first image and the second image.Correlation is first
The ratio of standard deviation product between covariance value and the first image and the second image between image and the second image, i.e. the first image and
Covariance value between second image is divided by standard deviation product between the first image and the second image.Correlation is a ratio, is not had
There is unit.The calculation formula of correlation is as follows:
For example, positive correlation show the variation relation between the first image and the second image be it is identical, when the first image
Pixel value when reducing, the pixel value of the second image is also opposite to be reduced；When the pixel value of the first image increases, the second image
Pixel value also relative increase.Negative correlation show the variation relation between the first image and the second image be it is different, when the first image
Pixel value reduce when, the pixel value relative increase of the second image；When the pixel value of the first image increases, the picture of the second image
Member value is opposite to be reduced.When correlation is zero, indicate that positive correlation or negative correlation are not present between the first image and the second image.
In the present embodiment, standard deviation of the optimum index between the first terrain factor and the second terrain factor, then divided by
The numerical value of related coefficient between first terrain factor and the second terrain factor, optimum index is proportional to the information content of image.Most preferably
Index is referred to as OIF (optimum index factor).The calculation formula of optimum index is as follows:
SiIt is the standard deviation of i-th of terrain factor, RijIt is the related coefficient between two terrain factors of i, j.Optimum index and
The sum of the related coefficient of each landform factor graph picture is inversely proportional, and the standard deviation between optimum index and terrain factor is directly proportional.Most preferably
Index is bigger, illustrates that redundancy between exponential factor is smaller and the information content of image is bigger.Opposite, optimum index is smaller, explanation
Redundancy is bigger between exponential factor and the information content of image is smaller.
S105, the ground that multiple cluster centres screen come unsupervised classification is chosen according to the mean value of variance yields and multiple subgraphs
The shape factor, multiple cluster centres include at least in the first cluster centre, the second cluster centre, third cluster centre and the 4th cluster
The heart.
In the present embodiment, the distance of each pixel and the first cluster centre is calculated and counted, then calculates the new mean value of image
Using as the second cluster centre, by loop iteration mode so that third cluster centre is identical to the 4th cluster centre and reaches receipts
Hold back threshold values.Optimal combination terrain factor is carried out based on iteration self-organizing data analysis technique by unsupervised classification
The unsupervised classification of (ISODATA, iterative self organizing data analysis).Following formula is foundation
The mean value M and variances sigma of image choose n initial cluster center:
By calculating pixel at a distance from the first cluster centre, calculated result is included into immediate classification.Then again
The new mean value for recalculating each classification continues cycling through iteration as the second cluster centre, changes until cycle-index reaches maximum
Generation number, by finally (i.e. third cluster centre and the 4th cluster centre) cluster result reaches circulation compared to remaining unchanged twice
Convergence threshold.
S107, boundary cleaning is carried out to the terrain factor of unsupervised classification with post-processing landforms boundary.By non-supervisory
Classify the landforms entity obtained, has on image a degree of flash, pixel contrast low, empty etc..By remove broken spot,
Increase resolution, eliminate the operations such as flash, elimination cavity, completes the extraction on landforms entity and landforms boundary.It is clear to reuse boundary
Reason and main filtering are smoothed edges of regions.
Boundary cleaning is primarily used to irregular edge between cleaning area.What digital elevation model use was extended and was shunk
Method clears up boundary in biggish range.Firstly, priority upper zone covered from all directions neighbouring priority compared with
Low area is covering size with a pixel.Wherein it is possible to low image value is covered by hi-vision value, it can also be by low image value
Cover hi-vision value.
In the present embodiment, the first image value for choosing the image on landforms boundary covers the second image value, the first image value
Greater than the second image value.In other words, can choose the biggish region of image value is that cover image value smaller for higher priority
Several regions.Schematic table is as follows:
In the present embodiment, the first area for choosing the image on landforms boundary covers second area, and first area is less than institute
State second area.In other words, can choose the lesser first area of area is that higher priority covers area biggish the
Two regions.Schematic table is as follows:
In the present embodiment, the half parameter in the third region of the image on landforms boundary or most figures in mode parameter are chosen
Picture value covers a small number of image values.Specifically, main filtering can replace pixel according to numerous numerical value in pixel neighborhood.It needs
When meeting two conditions (half parameter or mode parameter), main filtering could be replaced.Firstly, identical value is adjacent to pixel
Quantity must more to become mode value, or the pixel of at least half must have identical value.In mode parameter, four/
Three or 4/6ths or 5/8ths pixel that has connected must value having the same.In half parameter, then need four/
Two or 3/6ths or 4/8ths have connected pixel value having the same.Secondly, pixel in region must with it is specified
The center of filter is adjacent (for example, 3/4ths pixel must be identical).The purpose of half parameter or mode parameter is by region
The extent of the destruction of the spatial model of interior pixel is preferably minimized.It, will not if being unsatisfactory for half parameter or mode Parameter Conditions
It is replaced, the value of pixel will also remain unchanged.Main filtering application uses four nearest pixels as filtering in input raster
Device, and require mode parameter that could change the value of corresponding pixel.Change is by the pixel that three or three values above are identical and close on
The pixel of encirclement.Schematic table is as follows:
Embodiment 2
Referring to Fig. 2, Fig. 2 is the flow diagram for the measurement method that the embodiment of the present invention 2 provides landforms boundary.Such as figure
Shown in 2, measurement method the following steps are included:
S201, the multiple terrain factors of measurement.
S202, multiple terrain factors of measurement are normalized.
S203, the multiple terrain factors of screening.
The multiple terrain factors of S205, unsupervised classification.
S207, post-processing.
Wherein, the related description of S201, S203, S205 and S207 please refer to the related description of embodiment 1, to this present invention
It does not repeat.
In this S202, after measuring multiple terrain factors, and before the multiple terrain factors of screening, measurement method includes:
The dimension of unified each terrain factor is simultaneously normalized；Wherein, normalized is first by original image
Pixel value subtract the minimum pixel value of original image, then subtract original figure divided by the maximum pixel value of original image
The minimum pixel value of picture, then obtains the pixel value of normalized image multiplied by dimension maximum value.
In the present embodiment, in order to avoid elevation, ground line gradient, gradient variability, ground add up curvature, surface roughness,
The numerical value of face depth of cut, surface relief degree and the elevation coefficient of variation wherein at least one exceeds threshold values, to above-mentioned 8 terrain factors
It is normalized.Using the maximum pixel value contrast stretching of minimum pixel value subtracted image, former terrain factor letter is kept
Degree of correlation between breath.The value for exporting landform factor graph picture calculates gained by following formula:
BVoutFor the value of initial land form factor graph picture, minkAnd maxkRespectively represent minimum in original image, maximum pixel
Value, quantkFor dimension maximum value, the maximum value 255 of 8bit data type, minimum value 0 are taken here.
Embodiment 3
Referring to Fig. 3, Fig. 3 is that the embodiment of the present invention 3 provides measuring system (hereinafter referred to as " the measurement system on landforms boundary
System ") block schematic diagram.The image on landforms boundary is made by computer program based on digital elevation model.As shown in figure 3,
Measuring system 300 includes:
Measuring unit 301, for measuring multiple terrain factors.
Screening unit 302 obtains phase for calculating the standard deviation of each terrain factor with the correlation of more each terrain factor
Relationship number, and covariance matrix is obtained by related coefficient, wherein covariance matrix includes variance yields；According to standard deviation and correlation
Coefficient calculates optimum index；Multiple terrain factors are screened according to optimum index.
Taxon 303, for choosing multiple cluster centres according to the mean value of variance yields and multiple subgraphs come non-supervisory
The terrain factor of category filter.
Processing unit 304 carries out boundary for the terrain factor to unsupervised classification and clears up with post-processing landforms boundary.
Computing unit 305, for multiple terrain factors of measurement to be normalized；Wherein, normalized packet
It includes:
The minimum pixel value that original image is first subtracted by the pixel value of original image, then divided by original image
Maximum pixel value subtracts the minimum pixel value of original image, then obtains the pixel of normalized image multiplied by dimension maximum value
Value.
Screening unit 302 calculates the mean value of each terrain factor, wherein covariance matrix includes covariance value；Screening unit
The information content of the image on 302 setting standard deviations and landforms boundary is inversely proportional；
Wherein, variance yields square for image pixel value subtracted image pixel average value after be squared value, then count
Calculate the average value of square value；Multiple subgraphs include at least the first image and the second image, and correlation is the first image and second
Covariance value between image, then divided by the standard deviation product between the first image and the second image；
Wherein, multiple terrain factors include at least the first terrain factor and the second terrain factor, and optimum index is the first
Standard deviation between the shape factor and the second terrain factor, then divided by the phase relation between the first terrain factor and the second terrain factor
Number, be arranged optimum index numerical value be proportional to landforms boundary image information content.
As it can be seen that measuring system described in Fig. 3, can be accurately calculated landforms boundary, to improve detection effect
Rate.On the other hand, accuracy is handled according to digital elevation model to make landforms boundary, is measured multiplely by measuring unit
Shape because.Unified the dimension of each terrain factor by computing unit and is normalized.By screening unit according to screening correlation
Best landform combinations of factors is calculated in higher terrain factor.Best landform is merged by loop iteration by taxon
Combinations of factors.Boundary is cleared up by post-processing by processing unit to remove Burr removal and eliminate cavity.As it can be seen that implementing the present invention
Technical solution can be improved the accuracy on measurement landforms boundary, while considering that elevation, ground line gradient, gradient variability, ground are tired
It counts the terrain factor of curvature, surface roughness, ground depth of cut, surface relief degree and elevation coefficient of variation etc. and passes through
Loop iteration is screened to merge best landform combinations of factors, to improve the accuracy on measurement landforms boundary.
Memory may include storing program area and storage data area, wherein storing program area can storage program area, at least
Application program needed for one function (such as sound-playing function, image player function etc.) etc.；Storage data area can store root
Created data (such as audio data, phone directory etc.) etc. are used according to mobile terminal.In addition, memory may include high speed
Random access memory, can also include nonvolatile memory, a for example, at least disk memory, flush memory device or
Other volatile solid-state parts.
The present embodiment additionally provides a kind of computer readable storage medium, has memory, is stored with above-mentioned landforms
Computer program used in the measurement method on boundary.
In several embodiments provided herein, it should be understood that disclosed system and method can also pass through
Other modes are realized.The apparatus embodiments described above are merely exemplary, for example, flow chart and structure in attached drawing
Figure shows the system frame in the cards of the device of multiple embodiments according to the present invention, method and computer program product
Structure, function and operation.In this regard, each box in flowchart or block diagram can represent a module, section or code
A part, a part of the module, section or code includes one or more for implementing the specified logical function
Executable instruction.It should also be noted that function marked in the box can also be to be different from the implementation as replacement
The sequence marked in attached drawing occurs.For example, two continuous boxes can actually be basically executed in parallel, they are sometimes
It can execute in the opposite order, this depends on the function involved.It is also noted that in structure chart and/or flow chart
The combination of each box and the box in structure chart and/or flow chart, can function or movement as defined in executing it is dedicated
Hardware based system realize, or can realize using a combination of dedicated hardware and computer instructions.
In addition, each functional module or unit in each embodiment of the present invention can integrate one independence of formation together
Part, be also possible to modules individualism, an independent part can also be integrated to form with two or more modules.
If function is realized and when sold or used as an independent product in the form of software function module, can store
In a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially in other words to existing
Having the part for the part or the technical solution that technology contributes can be embodied in the form of software products, the computer
Software product is stored in a storage medium, including some instructions are used so that a computer equipment (can be intelligent hand
Machine, personal computer, server or network equipment etc.) execute all or part of each embodiment the method for the present invention
Step.And storage medium above-mentioned include: USB flash disk, it is mobile hard disk, read-only memory (ROM, Read-Only Memory), random
Access various Jie that can store program code such as memory (RAM, Random Access Memory), magnetic or disk
Matter.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a kind of measurement method on landforms boundary makes the landforms boundary by computer program based on digital elevation model
Image, described image are made of multiple subgraphs, which is characterized in that the measurement method includes:
Measure multiple terrain factors；
It calculates the standard deviation of each terrain factor and related coefficient is obtained with the correlation of more each terrain factor, and by the phase relation
Number obtains covariance matrix, wherein the covariance matrix includes variance yields；According to the standard deviation and the related coefficient meter
Calculate optimum index；The multiple terrain factor is screened according to the optimum index；
Multiple cluster centres are chosen according to the mean value of the variance yields and the multiple subgraph come screening described in unsupervised classification
Terrain factor；
Boundary is carried out to the terrain factor of the unsupervised classification to clear up with landforms boundary described in post-processing.
2. measurement method according to claim 1, which is characterized in that after measuring multiple terrain factors, and screening
Before the multiple terrain factor, the measurement method includes:
Multiple terrain factors of the measurement are normalized；Wherein, the normalized includes:
The minimum pixel value that original image is first subtracted by the pixel value of original image, then divided by the maximum of original image
Pixel value subtracts the minimum pixel value of original image, then obtains the pixel value of normalized image multiplied by dimension maximum value.
3. measurement method according to claim 1, which is characterized in that the multiple terrain factor includes elevation, ground slope
Degree, gradient variability, ground add up curvature, surface roughness, ground depth of cut, surface relief degree and the elevation coefficient of variation.
4. measurement method according to claim 1, which is characterized in that screened the multiplely according to the optimum index
In the shape factor, the measurement method includes:
Calculate the mean value of each terrain factor, wherein the covariance matrix includes covariance value；
The information content that the image on the standard deviation and landforms boundary is arranged is inversely proportional；
Wherein, variance yields square for image pixel value subtracted image pixel average value after be squared value, then calculate institute
State the average value of square value；Multiple subgraphs include at least the first image and the second image, and the correlation is first figure
Covariance value between picture and second image then multiplies divided by the standard deviation between the first image and second image
Product；
Wherein, multiple terrain factors include at least the first terrain factor and the second terrain factor, and the optimum index is described the
Standard deviation between one terrain factor and second terrain factor, then divided by first terrain factor and second landform
Related coefficient between the factor, be arranged the optimum index numerical value be proportional to the landforms boundary image information content.
5. measurement method according to claim 1, which is characterized in that the terrain factor of the screening described in unsupervised classification
In, multiple cluster centres include at least the first cluster centre, the second cluster centre, third cluster centre and the 4th cluster centre,
The measurement method includes:
Calculate and count the pixel of each image and the distance of first cluster centre, then calculate image new mean value using as
Second cluster centre, by loop iteration mode so that the third cluster centre is identical to the 4th cluster centre simultaneously
Reach convergence threshold values.
6. measurement method according to claim 1, which is characterized in that carried out in the terrain factor to the unsupervised classification
With in landforms boundary described in post-processing, the measurement method includes: for boundary cleaning
The first image value for choosing the image on landforms boundary covers the second image value, wherein the first image value is greater than institute
State the second image value；
The first area for choosing the image on landforms boundary covers second area, wherein the first area is less than described second
Region；
The half parameter or most image values in mode parameter for choosing the third region of the image on the landforms boundary cover
A small number of image values.
7. a kind of measuring system on landforms boundary makes the landforms boundary by computer program based on digital elevation model
Image, described image are made of multiple subgraphs, which is characterized in that the measuring system includes:
Measuring unit, for measuring multiple terrain factors；
Screening unit obtains related coefficient for calculating the standard deviation of each terrain factor with the correlation of more each terrain factor,
And covariance matrix is obtained by the related coefficient, wherein the covariance matrix includes variance yields；According to the standard deviation and
The related coefficient calculates optimum index；Screening unit screens the multiple terrain factor according to the optimum index；
Taxon, for choosing multiple cluster centres according to the mean value of the variance yields and the multiple subgraph come non-supervisory
Classify the terrain factor of the screening；
Processing unit carries out boundary for the terrain factor to the unsupervised classification and clears up with landforms side described in post-processing
Boundary.
8. measuring system according to claim 7, which is characterized in that the measuring system further include:
Computing unit, for multiple terrain factors of the measurement to be normalized；Wherein, the normalized packet
It includes:
The minimum pixel value that original image is first subtracted by the pixel value of original image, then divided by the maximum of original image
Pixel value subtracts the minimum pixel value of original image, then obtains the pixel value of normalized image multiplied by dimension maximum value.
9. measuring system according to claim 7, which is characterized in that screening unit calculates the mean value of each terrain factor,
In, the covariance matrix includes covariance value；The information content of the image on the standard deviation and landforms boundary is arranged in screening unit
It is inversely proportional；
Wherein, variance yields square for image pixel value subtracted image pixel average value after be squared value, then calculate institute
State the average value of square value；Multiple subgraphs include at least the first image and the second image, and the correlation is first figure
Covariance value between picture and second image then multiplies divided by the standard deviation between the first image and second image
Product；
Wherein, multiple terrain factors include at least the first terrain factor and the second terrain factor, and the optimum index is described the
Standard deviation between one terrain factor and second terrain factor, then divided by first terrain factor and second landform
Related coefficient between the factor, be arranged the optimum index numerical value be proportional to the landforms boundary image information content.
10. a kind of computer readable storage medium has memory, which is characterized in that it, which is stored in claim 1 to 6, appoints
Computer program used in the measurement method on landforms boundary described in one.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111288985A (en) * | 2020-03-04 | 2020-06-16 | 北京易控智驾科技有限公司 | Map determination method and device, equipment and automatic mine car driving method |
| CN111784789A (en) * | 2020-06-22 | 2020-10-16 | 上海米哈游天命科技有限公司 | Landform generation method and device, computer equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104180754A (en) * | 2014-07-28 | 2014-12-03 | 南京林业大学 | Inversion method for biophysical property of forest based on LiDAR comprehensive waveform model |
| CN105787457A (en) * | 2016-03-08 | 2016-07-20 | 浙江工商大学 | Evaluation method for improving vegetation classified remote sensing precision through integration of MODIS satellite and DEM |
-
2018
- 2018-08-02 CN CN201810873541.3A patent/CN109163701B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104180754A (en) * | 2014-07-28 | 2014-12-03 | 南京林业大学 | Inversion method for biophysical property of forest based on LiDAR comprehensive waveform model |
| CN105787457A (en) * | 2016-03-08 | 2016-07-20 | 浙江工商大学 | Evaluation method for improving vegetation classified remote sensing precision through integration of MODIS satellite and DEM |
Non-Patent Citations (1)
| Title |
|---|
| 万越: ""基于数字地形分析的排土场微地貌景观格局分析"", 《中国优秀硕士学位论文全文数据库 基础科学辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111288985A (en) * | 2020-03-04 | 2020-06-16 | 北京易控智驾科技有限公司 | Map determination method and device, equipment and automatic mine car driving method |
| CN111784789A (en) * | 2020-06-22 | 2020-10-16 | 上海米哈游天命科技有限公司 | Landform generation method and device, computer equipment and storage medium |
| CN111784789B (en) * | 2020-06-22 | 2024-03-08 | 上海米哈游天命科技有限公司 | Landform generating method and device, computer equipment and storage medium |
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