CN106530398A - Terrain visibility analysis-oriented visibility graph network construction method - Google Patents
Terrain visibility analysis-oriented visibility graph network construction method Download PDFInfo
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Abstract
The invention provides a terrain visibility analysis-oriented visibility graph network construction method. The method includes the following steps: (1) scanning DEM grid data, analyzing whether null data exist, and if yes, performing interpolation calculation, and supplementing data; (2) selecting a DEM grid unit neighbor mode of a constructed graph network, mode types including a 4-grid unit neighbor mode, a 8-grid unit neighbor mode and 16-grid unit neighbor mode; (3) calculating attribute values of vertices in the visibility graph network: based on a visible range analysis algorithm, calculating the number of visible grid points of a grid unit corresponding to each vertex in the network or the area of a visible range; (4) according to the selected neighbor mode type, calculating the weights of sides in the visibility graph network; and (5) saving visibility graph network data. The terrain visibility analysis-oriented visibility graph network construction method provided by the invention can be completely applied to graph network-based optimization modeling and solving of different fields of terrain visibility analysis of large-scale mass data, and processing efficiency can be improved.
Description
Technical field
The invention belongs to the visualization analysis technical field of digital Terrain Analysis, relates to the use of the DEM of Rule acquisition
Realization of the data conversion into the Visual Graph network conversion method based on graph theory, so be based on Visual Graph network model can
Analyze depending on property and lay the first stone with application.
Background technology
Digital Terrain Analysis (Digital Terrain Analysis, abbreviation DTA) are in digital elevation model
Terrain properties are carried out on the basis of (Digital Elevation Model, vehicle economy M) calculates the digital information with feature extraction
Treatment technology.Visualization analysis are the important terrain analysis factors of digital Terrain Analysis, including visibility analysis and visible range point
Analysis.Recallable amounts refer to from single or multiple geographical position the terrestrial range that can be seen also known as landform flux-vector splitting
Or with the visible level between other geographical position, be the indispensable part in digital Terrain Analysis.Landform is visual
Domain analysiss have important meaning in many association areas, have become landscape Analysis and assessment, building plans, military affairs,
The important research means in the field such as spatial cognition and decision-making, archaeology.
DEM models are a kind of field models, including regular grid (Regular Square Grid, abbreviation RSG) model and not
Regular triangle net (Triangulated Irregular Network, abbreviation TIN) model.Due to visualization analysis and its should
It is analyzed with DEM is all based on, these applications are taken visual feature into account and set up various Optimized models, and set up in DEM models
On Optimized model either in terms of modeling, or be all more complicated in terms of model solution, and towards magnanimity DEM number
According to the solving speed of application algorithm cannot effectively improve, it is even if adopting parallel computing, but involved in these applications
Data have dependency characteristic, and cannot improve the efficiency for calculating by parallelization means.
On the one hand, the reflection of DEM models be landform altitude data, and in the landform that is beyond expression point of observation and impact point it
Between relation, need by application parsed.If by DEM model conversions into a kind of figure network model based on graph theory being
The various geographic application modelings for taking visual feature into account bring great convenience, and set up new for analysis of Terrain Visibility and application
Theoretical basiss and instrument.On the other hand, with the appearance of various novel sensors and e measurement technology, dem data is in series
Increase, be a very difficult thing so as to cause large-scale data to be carried out processing under stand-alone environment.Therefore, based on figure
The big diagram data of network model can improve the efficiency of data processing using parallel computing.
On the digital terrain surface represented based on gridded DEM data, each grid unit can be regarded as a node,
Relation (such as distance, depth displacement etc.) between grid unit can be described as the side with weights, so as to by regular grid list
It is a virtual graph network model with 2 dimensional plane features that the digital elevation model region with 2.5 dimensions of unit's composition is abstract.
Different regular grid units has 4 grid unit patterns, 8 grid unit patterns, 16 grid unit patterns substantially adjacent to pattern.Choosing
Select precision and solution efficiency requirement of the different adjacent modes depending on problem, and data volume and computation complexity constraint.
The content of the invention
The present invention is directed to the problems referred to above, takes visualization analysis into account, it is proposed that a kind of field model for representing DEM terrain datas
It is converted into the construction method of pictorial Visual Graph network model.
The technical solution used in the present invention is as follows：
A kind of visual figure network establishing method towards analysis of Terrain Visibility, comprises the following steps：
Step 1, dem data initialization：DEM Grid squares are scanned, analyse whether there are empty data, if existing,
Carry out interpolation calculation, supplementary data；
Step 2, selects to build the DEM grid unit adjacent modes of figure network, and mode type includes 4 grid units adjacent to mould
Formula, 8 grid unit adjacent modes and 16 grid unit adjacent modes；
Step 3, calculates the property value on summit in Visual Graph network：With reference to recallable amounts algorithm, in calculating network each
The visual grid of the grid unit of vertex correspondence is counted out or visible range size；
Step 4, according to the adjacent modes type that step 2 is selected, calculates the weights on side in Visual Graph network：
(1) for 4 grid unit adjacent modes, grid unit is considered as along 4 direction connections of coordinate axess, its weights meter
Calculating formula is：
Wherein, d is square grid unit sampling interval, the i.e. length of side；Ei、EjIt is source grid points and target mesh point respectively
Height value；
(2) for 8 grid unit adjacent modes, 4 sides of the grid unit along 4 direction connections of coordinate axess and diagonally
To connection, its weight computing formula is：
Wherein, d is square grid unit sampling interval, the i.e. length of side；Ei、EjIt is source grid points and target mesh point respectively
Height value；
(3) for 16 grid unit adjacent modes, grid unit is along coordinate axess 4 direction connections, 4 sides diagonally
To connection, and it is connected with 8 directions of angular bisector of diagonal angle along coordinate axess, its weight computing formula is：
Wherein, d is square grid unit sampling interval, the i.e. length of side；Ei、EjIt is source grid points and target mesh point respectively
Height value；
Step 5, preserves Visual Graph network data.
The invention provides a kind of dem data model based on graph theory is to the Visual Graph network model expressed with diagram data
Conversion method, compared with prior art, the present invention technical characterstic and have the beneficial effect that：
1st, the figure network method towards the visual conformal analysis of landform proposed by the present invention, advises with reference to the DEM of digital Terrain Analysis
The field model that sound of laughing network data is represented, based on figure network model in graph theory Fundamentals of Mathematics, is built, be analysis of Terrain Visibility with
It is basic using setting up.
2nd, in the figure network based on recallable amounts proposed by the present invention attribute value calculating method and side weight computing side
Method, can build complete figure network model, for taking Optimization Modeling and the analysis of the visuality application of visual feature into account, there is provided
New approach.
3rd, present invention is fully applicable to the different field of the analysis of Terrain Visibility of extensive mass data based on figure
In terms of the Optimization Modeling of network and solution, for example, advised based on the tourism path planning of recallable amounts, hazardous materials transportation path
Draw, army marches hidden path planning, it is also possible to be applied to landscape Analysis and assessment, military, spatial cognition and decision-making, archaeology etc.
The application scenarios such as the research meanses based on the recallable amounts in field, improve treatment effeciency.
Description of the drawings
Fig. 1 is the figure network struction flow chart in the embodiment of the present invention；
Fig. 2 is the DEM grid adjacent modes figures towards landform recallable amounts in the embodiment of the present invention, wherein, is a) 4
Grid unit pattern；B) it is 8 grid unit patterns；C) it is 16 grid unit patterns；
Fig. 3 is the weight computing schematic diagram of 4 grid adjacent unit ideograph network edges in the embodiment of the present invention；
Fig. 4 is the weight computing schematic diagram of 8 grid adjacent unit ideograph network edges in the embodiment of the present invention；
Weight computing schematic diagrams of the Fig. 5 for 16 grid adjacent unit ideograph network edges in the embodiment of the present invention, wherein,
A) be Fig. 2 c) in 9,12,13,16 grid points directions；B) be Fig. 2 c) in 10,11,14,15 grid points directions.
Specific embodiment
The present invention is illustrated below in conjunction with accompanying drawing.It may be noted that described embodiment is only deemed as the mesh for illustrating
, rather than the restriction to inventing.
The embodiment provides a kind of figure network establishing method modeled with application towards visualization analysis, its mesh
Be that, in order to the Optimization Modeling problem of analysis of Terrain Visibility application to be divided into two steps to complete, one is first by digital elevation
The data conversion of the field model of model is then based on the Optimization Modeling and model of graph model into the diagram data expressed with graph model
Solve.Advantage of this is that the efficiency that can simplify the complexity and model solution of modeling.
Such as in the path planning problem based on visualization analysis, digital elevation model is converted into into figure network mould first
Type, then carrying out path planning in plane graph network just can be using carrying out Shortest Path Searching about the algorithm in graph theory.
Militarily, most hidden path can be found by figure network.In tourism path planning, it is possible to use figure network is found most short
Route just looks at most sight spots.In hazardous materials transportation route planning, be exactly on the basis of figure network find one it is most short
And the minimum route of caused harm is revealed or exploded to dangerous materials.
In the siteselecting plannings such as forest fire alarm Watchtower, communication base station, military point of observation, can be in the base of figure network
The network constituted by one group of point of observation is found on plinth so that the Watchtower network or scope observed by military observation spot net
(area) is maximum, or the signal area covered by the base station network is maximum.
The method of the present invention exactly realize it is above-mentioned application in the first step, will 2.5 dimension digital elevation models be converted into 1
The plane graph network model of dimension, is to be optimized modeling using figure network model to set up basis.Also it is further to realize simultaneously simultaneously
The design of row computational algorithm provides new way, so as to solve the problems, such as the high-performance calculation of large-scale data.The conversion side of the present embodiment
Method, comprises the following steps：
1：Dem data is initialized.DEM Grid squares are scanned, analyse whether there are empty data, if existing, carried out
Interpolation calculation, supplements and improves sky data；
2：Select grid unit adjacent modes.There are 4 grid adjacent modes, 8 grid adjacent modes and 16 grid adjacent modes
It is selective；
3：Calculate the property value on each summit in figure network.With reference to recallable amounts algorithm, each vertex correspondence is calculated
The visual grid of grid unit is counted out or visible range size.The spy on each summit in vertex attribute expression figure network
Levy, can be by one group of property value description.If necessary to visible range size, then each summit can be calculated by recallable amounts algorithm
The visible range area of corresponding grid unit.If discrete point is represented, then can be that the target seen from the point is counted out.
4：Calculate the weights on side in figure network.According to figure network size and required precision, may be selected 4 grid adjacent modes, 8
Grid adjacent modes or 16 grid adjacent modes carry out the weight computing formula on side, and calculate weights size：
1) 4 grid unit adjacent modes are based on, referring to Fig. 2 a), it is considered as grid unit and connects along 4 directions of coordinate axess
Connect, i.e., grid center is to 1,2,3 and 4 grid points.The weight computing on the side of its figure network model is fairly simple, and its geometric representation is such as
Shown in Fig. 3.
Assume grid unit ViAnd VjHeight value be respectively EiAnd Ej, along a length of a of X-axis between grid, along a length of b of Y-axis, then
The weight computing formula on the side of figure network is as follows：
If grid is square, i.e. a=b=d, then formula (1) is changed into：
If being normalized, formula (2) is changed into：
2) 8 grid unit patterns are based on, referring to Fig. 2 b), in addition to 4 directions along coordinate axess (1,2,3 and 4 point), also
4 directions (5,6,7 and 8 points) diagonally.The weight calculation method on the side of its graph model network is as shown in Figure 4.Assume lattice
Net unit ViAnd VjHeight value be respectively EiAnd Ej, along a length of a of X-axis between grid, along a length of b of Y-axis, then the power on the side of figure network
It is worth and is：
If square grid, i.e. a=b=d, then formula (4) is changed into：
It is normalized, then formula (5) is changed into：
3) 16 adjacent to grid unit pattern, referring to Fig. 2 c), it include X-direction (2 and 4 points), Y direction (1 and 3 points),
(one group is 9,12,13 and 16, and another group is 10,11,14, and 15 for diagonal (5,6,7 and 8 points) and other 8 directions
Point).The weight calculation method of its figure network edge is：Assume grid unit ViAnd VjHeight value be respectively EiAnd Ej, edge between grid
The a length of a of X-axis, along a length of b of Y-axis, then the weights on the side of figure network are (referring to Fig. 5)：
If square grid, i.e. a=b=d, then formula (7) is changed into：
It is normalized, then formula (8) is changed into：
Figure network model is the figure method for expressing in a kind of graph theory, and the height value of topographic(al) point on regular grid is transformed into
The figure network represented with summit and side, provides new way for Optimization Modeling.The attribute of node include visual viewpoint number or
Cartographic represenation of area, the weights on side can be calculated according to switching strategy according to height value.In order to consider visual covering problem, according to
Visualization analysis set up the coverage (representing with cartographic represenation of area or with the number of visible point) of viewpoint, and its value is used as in graph model
The property value of node.In the discrete case, can be modeled using complex network, set up typical viewpoint and observed point
The degree of visual network model, its point of observation or viewpoint can be used to represent visual coverage.
After calculating the weights on each side, then need to build the attribute of each summit V, (can be with including visible range size
Be expressed as size, visual impact point number etc.), degree size (directed networkses can be in-degree and go out angle value).It is adjacent for 4
Nearly grid pattern, the degree on each of which summit is 4.8 adjacent to grid pattern, and the degree on each of which summit is 8, and 16 adjacent to grid mould
Formula, the degree on each of which summit is 16.Obviously, the visual network of structure is regular network.
5：Storage figure network data.According to the result of calculation of the weights on each vertex attributes values and side in figure network, and deposit
Storage is in the data file.
In specific path planning, objective optimization model can be set up according to figure network, such as shortest path, minimum are visual
Path etc..In siteselecting planning, the model and its algorithm in maximum visual domain based on figure network etc. can be set up.
Claims (1)
1. a kind of visual figure network establishing method towards analysis of Terrain Visibility, it is characterised in that the method includes following
Step：
Step 1, dem data initialization：DEM Grid squares are scanned, analyse whether there are empty data, if existing, carried out
Interpolation calculation, supplementary data；
Step 2, select build figure network DEM grid unit adjacent modes, mode type include 4 grid unit adjacent modes, 8
Grid unit adjacent modes and 16 grid unit adjacent modes；
Step 3, calculates the property value on summit in Visual Graph network：With reference to recallable amounts algorithm, each summit in calculating network
The visual grid of corresponding grid unit is counted out or visible range size；
Step 4, according to the adjacent modes type that step 2 is selected, calculates the weights on side in Visual Graph network：
(1) for 4 grid unit adjacent modes, grid unit is considered as along 4 direction connections of coordinate axess, its weight computing is public
Formula is：
Wherein, d is square grid unit sampling interval, the i.e. length of side；Ei、EjIt is the height of source grid points and target mesh point respectively
Journey value；
(2) for 8 grid unit adjacent modes, 4 directions of the grid unit along 4 direction connections of coordinate axess and diagonally connect
Connect, its weight computing formula is：
Wherein, d is square grid unit sampling interval, the i.e. length of side；Ei、EjIt is the height of source grid points and target mesh point respectively
Journey value；
(3) for 16 grid unit adjacent modes, grid unit connects along 4 direction connections of coordinate axess, 4 directions diagonally
Connect, and be connected with 8 directions of angular bisector of diagonal angle along coordinate axess, its weight computing formula is：
Wherein, d is square grid unit sampling interval, the i.e. length of side；Ei、EjIt is the height of source grid points and target mesh point respectively
Journey value；
Step 5, preserves Visual Graph network data.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107808059A (en) * | 2017-11-14 | 2018-03-16 | 南京师范大学 | A kind of landform paths planning method based on directed networkses |
| CN108446785A (en) * | 2018-01-31 | 2018-08-24 | 南京师范大学 | A kind of optimal visual overlay path planing method based on landform visible range |
| CN110097636A (en) * | 2019-01-31 | 2019-08-06 | 南京师范大学 | A kind of Site planning method based on recallable amounts |
| CN111247564A (en) * | 2019-03-12 | 2020-06-05 | 深圳市大疆创新科技有限公司 | Method for constructing digital earth surface model, processing equipment and system |
| CN111325791A (en) * | 2020-01-17 | 2020-06-23 | 中国人民解放军战略支援部队信息工程大学 | OSP space reference line through-view domain analysis method based on regular grid DEM |
| CN111504322A (en) * | 2020-04-21 | 2020-08-07 | 南京师范大学 | Scenic spot tour micro-route planning method based on visible field |
| CN112002012A (en) * | 2020-08-26 | 2020-11-27 | 中南大学 | Visibility analysis method for urban area |
| CN112184900A (en) * | 2019-07-04 | 2021-01-05 | 北京四维图新科技股份有限公司 | Method and device for determining elevation data and storage medium |
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| CN107808059A (en) * | 2017-11-14 | 2018-03-16 | 南京师范大学 | A kind of landform paths planning method based on directed networkses |
| CN108446785A (en) * | 2018-01-31 | 2018-08-24 | 南京师范大学 | A kind of optimal visual overlay path planing method based on landform visible range |
| CN110097636A (en) * | 2019-01-31 | 2019-08-06 | 南京师范大学 | A kind of Site planning method based on recallable amounts |
| WO2020181508A1 (en) * | 2019-03-12 | 2020-09-17 | 深圳市大疆创新科技有限公司 | Digital surface model construction method, and processing device and system |
| CN111247564A (en) * | 2019-03-12 | 2020-06-05 | 深圳市大疆创新科技有限公司 | Method for constructing digital earth surface model, processing equipment and system |
| CN112184900A (en) * | 2019-07-04 | 2021-01-05 | 北京四维图新科技股份有限公司 | Method and device for determining elevation data and storage medium |
| CN112184900B (en) * | 2019-07-04 | 2024-03-19 | 北京四维图新科技股份有限公司 | Method, device and storage medium for determining elevation data |
| CN111325791A (en) * | 2020-01-17 | 2020-06-23 | 中国人民解放军战略支援部队信息工程大学 | OSP space reference line through-view domain analysis method based on regular grid DEM |
| CN111325791B (en) * | 2020-01-17 | 2023-01-06 | 中国人民解放军战略支援部队信息工程大学 | OSP space reference line through-view domain analysis method based on regular grid DEM |
| CN111504322A (en) * | 2020-04-21 | 2020-08-07 | 南京师范大学 | Scenic spot tour micro-route planning method based on visible field |
| CN111504322B (en) * | 2020-04-21 | 2021-09-03 | 南京师范大学 | Scenic spot tour micro-route planning method based on visible field |
| CN112002012A (en) * | 2020-08-26 | 2020-11-27 | 中南大学 | Visibility analysis method for urban area |
| CN112002012B (en) * | 2020-08-26 | 2022-07-08 | 中南大学 | Visibility analysis method for urban area |
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