CN106530398B - A kind of visual figure network establishing method towards analysis of Terrain Visibility - Google Patents
A kind of visual figure network establishing method towards analysis of Terrain Visibility Download PDFInfo
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Abstract
The present invention provides a kind of visual figure network establishing method towards analysis of Terrain Visibility.This method comprises: (1) is scanned DEM Grid square, analyse whether there are empty data, and if it exists, carry out interpolation calculation, supplementary data；(2) the DEM grid unit adjacent modes of structure figures network are selected, mode type includes 4 grid unit adjacent modes, 8 grid unit adjacent modes and 16 grid unit adjacent modes；(3) it calculates the attribute value on vertex in Visual Graph network: in conjunction with recallable amounts algorithm, calculating the visual grid points number or visible range size of the grid unit of each vertex correspondence in network；(4) according to the adjacent modes type of selection, the weight on side in Visual Graph network is calculated；(5) Visual Graph network data is saved.Present invention is fully applicable to the Optimization Modelings and solution aspect based on figure network of the different field of the analysis of Terrain Visibility of extensive mass data, can be improved treatment effeciency.
Description
Technical field
The invention belongs to the visualization analysis technical fields of digital Terrain Analysis, relate to the use of the DEM of Rule acquisition
Data conversion at the Visual Graph network conversion method based on graph theory realization, and then for based on Visual Graph network model can
It lays the foundation depending on property analysis with application.
Background technique
Digital Terrain Analysis (Digital Terrain Analysis, abbreviation DTA) is in digital elevation model
The digital information of terrain properties calculating and feature extraction is carried out on the basis of (Digital Elevation Model, vehicle economy M)
Processing technique.Visualization analysis is the important terrain analysis factor of digital Terrain Analysis, including visibility analysis and visible range point
Analysis.Recallable amounts are also known as landform flux-vector splitting, refer to the terrestrial range that can be seen from single or multiple geographical locations
Or the visible level between other geographical locations, it is indispensable a part in digital Terrain Analysis.Landform is visual
Domain analysis has important meaning in many related fieldss, have become landscape Analysis and assessment, building plans, military affairs,
The important research means in the fields such as spatial cognition and decision, archaeology.
DEM model is a kind of field model, 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 answer
It is analyzed with DEM is all based on, these applications take visual feature into account and establish various Optimized models, and establish in DEM model
On Optimized model either in terms of modeling, or in terms of model solution be all more complicated, and towards magnanimity DEM number
According to the solving speed using algorithm can not effectively improve, even if using parallel computing, but these application involved in
Data have dependency characteristic, and can not improve the efficiency calculated by parallelization means.
On the one hand, the reflection of DEM model be landform altitude data, and in the landform that is beyond expression point of observation and target point it
Between relationship, need by application parsed.If being at a kind of figure network model based on graph theory by DEM model conversion
The various geographic applications modeling for taking visual feature into account brings great convenience, and new for analysis of Terrain Visibility and application foundation
Theoretical basis and tool.On the other hand, with the appearance of various novel sensors and measuring technique, dem data is in series
Increase, is a very difficult thing so as to cause processing is carried out to large-scale data 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 indicated based on gridded DEM data, each grid unit can be regarded as a node,
Relationship (such as distance, depth displacement etc.) between grid unit can be described as the side with weight, thus by regular grid list
The digital elevation model region with 2.5 dimensions of member composition is abstracted as the virtual graph network model with 2 dimensional plane features.
Different regular grid units has 4 grid unit modes, 8 grid unit modes, 16 grid unit modes substantially adjacent to mode.Choosing
Different adjacent modes are selected depending on the precision of problem and solution efficiency requirement and data volume and computation complexity constraint.
Summary of the invention
The present invention proposes a kind of field model for indicating DEM terrain data in view of the above-mentioned problems, take visualization analysis into account
It is converted into the construction method for the Visual Graph network model figured.
The technical solution adopted by the invention is as follows:
A kind of visual figure network establishing method towards analysis of Terrain Visibility, comprising the following steps:
Step 1, dem data initializes: being scanned to DEM Grid square, analyses whether there are empty data, and if it exists,
Carry out interpolation calculation, supplementary data；
Step 2, the DEM grid unit adjacent modes of structure figures network are selected, mode type includes 4 grid units adjacent to mould
Formula, 8 grid unit adjacent modes and 16 grid unit adjacent modes；
Step 3, it calculates the attribute value on vertex in Visual Graph network: in conjunction with recallable amounts algorithm, calculating each in network
The visual grid points number or visible range size of the grid unit of vertex correspondence；
Step 4, the adjacent modes type selected according to step 2 calculates the weight on side in Visual Graph network:
(1) it for 4 grid unit adjacent modes, is considered as grid unit and is connected along 4 directions of reference axis, weight meter
Calculate formula are as follows:
Wherein, d is square grid unit sampling interval, i.e. side length；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 reference axis and diagonally
To connection, weight computing formula are as follows:
Wherein, d is square grid unit sampling interval, i.e. side length；Ei、EjIt is source grid points and target mesh point respectively
Height value；
(3) for 16 grid unit adjacent modes, grid unit is along reference axis 4 direction connections, 4 sides diagonally
It is connected to connection and along 9,10,11,12,13,14,15 with 16 points of 8 directions, weight computing formula are as follows:
Wherein, d is square grid unit sampling interval, i.e. side length；Ei、EjIt is source grid points and target mesh point respectively
Height value；
Step 5, Visual Graph network data is saved.
The present 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 technical characterstic of the invention and has the beneficial effect that compared with prior art
1, the figure network method proposed by the present invention towards the visual conformal analysis of landform is advised in conjunction with the DEM of digital Terrain Analysis
The field model that sound of laughing network data indicates, based in graph theory Fundamentals of Mathematics, structure figures network model is analysis of Terrain Visibility with
It is basic using establishing.
2, in the figure network proposed by the present invention based on recallable amounts attribute value calculating method and side weight computing side
Method can construct complete figure network model, for the visual Optimization Modeling applied and the analysis for taking visual feature into account, provide
New approach.
3, present invention is fully applicable to the different fields of the analysis of Terrain Visibility of extensive mass data based on figure
The Optimization Modeling and solution aspect of network, for example, the tourism path planning based on recallable amounts, hazardous materials transportation path rule
It draws, the hidden path planning of army's march, also can be applied to landscape Analysis and assessment, military, spatial cognition and decision, archaeology etc.
Treatment effeciency is improved in the applications such as the research means based on the recallable amounts in field.
Detailed description of the invention
Fig. 1 is the figure network struction flow chart in the embodiment of the present invention；
Fig. 2 is the DEM grid adjacent modes figure towards landform recallable amounts in the embodiment of the present invention, wherein a) is 4
Grid unit mode；It b) is 8 grid unit modes；It c) is 16 grid unit modes；
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；
Fig. 5 is the weight computing schematic diagram of 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 attached drawing.It may be noted that described embodiment is only deemed as the mesh of explanation
, rather than the limitation to invention.
The embodiment provides a kind of figure network establishing method modeled towards visualization analysis and application, mesh
Be to be divided into two steps for the Optimization Modeling problem for applying analysis of Terrain Visibility to complete, first is that first by digital elevation
The data conversion of the field model of model is at the diagram data expressed with graph model, followed by Optimization Modeling and model based on graph model
It solves.The advantage of doing so is that can simplify the complexity of modeling and the efficiency of model solution.
Such as in the path planning problem based on visualization analysis, digital elevation model is first converted into figure network mould
Type, path planning is then carried out in plan view network to carry out Shortest Path Searching using in relation to the algorithm in graph theory.
Militarily, most hidden path can be found by figure network.In tourism path planning, it is most short to can use the searching of figure network
Route just looks at most sight spots.In hazardous materials transportation route planning, be exactly on the basis of figure network find it is one most short
And the smallest route of harm caused by dangerous material leakage or explosion.
It, can be in the base of figure network in the siteselecting plannings such as Forest Fire Jing sightseeing tower, communication base station, military point of observation
Network composed by one group of point of observation is found on plinth, so that range observed by the sightseeing tower network or military observation spot net
The signal area that (area) maximum or the base station network are covered is maximum.
Method of the invention is exactly the first step realized in above-mentioned application, i.e., the digital elevation model of 2.5 dimensions is converted into 1
The plan view network model of dimension establishes basis to optimize modeling using figure network model.It is simultaneously also to further realize simultaneously
The design of row computational algorithm provides new way, to solve the problems, such as the high-performance calculation of large-scale data.The conversion side of the present embodiment
Method, comprising the following steps:
1:DEM data initialization.DEM Grid square is scanned, analyses whether there are empty data, and if it exists, carry out
Interpolation calculation supplements and improves empty data；
2: selection grid unit adjacent modes.There are 4 grid adjacent modes, 8 grid adjacent modes and 16 grid adjacent modes
It is selective；
3: calculating the attribute value on each vertex in figure network.In conjunction with recallable amounts algorithm, each vertex correspondence is calculated
The visual grid points number or visible range size of grid unit.Vertex attribute expresses the spy on each vertex in figure network
Sign can be described by one group of attribute value.If necessary to visible range size, then each vertex can be calculated by recallable amounts algorithm
The visible range area of corresponding grid unit.It is indicated if it is discrete point, then can be the target point number seen from the point.
4: calculating the weight on side in figure network.According to figure network size and required precision, 4 grid adjacent modes, 8 may be selected
Grid adjacent modes or 16 grid adjacent modes carry out the weight computing formula on side, and calculate weight size:
1) 4 grid unit adjacent modes are based on, referring to Fig. 2 a), grid unit is considered as and connects along 4 directions of reference axis
It connects, i.e. grid center to 1,2,3 and 4 grid points.The weight computing on the side of its figure network model is fairly simple, and geometric representation is such as
Shown in Fig. 3.
Assuming that 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) becomes:
If being normalized, formula (2) becomes:
2) 8 grid unit modes are based on, referring to Fig. 2 b), in addition to 4 directions along reference axis (), also at 1,2,3 and 4 point
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.Assuming that 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
Value are as follows:
If it is square grid, i.e. a=b=d, then formula (4) becomes:
It is normalized, then formula (5) becomes:
3) 16 adjacent to grid unit mode, referring to Fig. 2 c) comprising 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 are as follows: assuming that 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 weight on the side of figure network is (referring to Fig. 5):
If square grid, i.e. a=b=d, then formula (7) becomes:
It is normalized, then formula (8) becomes:
Figure network model is the figure representation method in a kind of graph theory, and the height value of topographic(al) point on regular grid is transformed into
The figure network indicated with vertex and side, provides new way for Optimization Modeling.The attribute of node include visual viewpoint number or
The weight of cartographic represenation of area, side can be calculated according to height value according to switching strategy.In order to consider visual covering problem, according to
Visualization analysis establishes the coverage area (indicating with cartographic represenation of area or with the number of visible point) of viewpoint, and value is as in graph model
The attribute value of node.In the discrete case, it can be modeled using complex network, establish typical viewpoint and be observed point
The degree of visual network model, point of observation or viewpoint can be used for indicating visual coverage.
After the weight for calculating each side, then need to construct the attribute of each vertex V, including visible range size (can be with
Be expressed as size, visual target point number etc.), degree size (directed networks can be in-degree and out angle value).For 4 neighbours
Nearly grid mode, the degree on each of which vertex is 4.8 adjacent to grid mode, the degree on each of which vertex be 8, and 16 adjacent to grid mould
Formula, the degree on each of which vertex are 16.Obviously, the visual network of building is regular network.
5: storage figure network data.According to the calculated result of vertex attributes values each in figure network and the weight on side, and deposit
Storage is in the data file.
In specific path planning, objective optimization model can be established according to figure network, for example shortest path, minimum are visually
Path etc..In siteselecting planning, model and its algorithm in the maximum visual domain based on figure network etc. can establish.
Claims (1)
1. a kind of visual figure network establishing method towards analysis of Terrain Visibility, which is characterized in that this method includes following
Step:
Step 1, dem data initializes: being scanned to DEM Grid square, analyses whether there are empty data, and if it exists, carry out
Interpolation calculation, supplementary data；
Step 2, the DEM grid unit adjacent modes of structure figures network are selected, mode type includes 4 grid unit adjacent modes, 8
Grid unit adjacent modes and 16 grid unit adjacent modes；
Step 3, it calculates the attribute value on vertex in Visual Graph network: in conjunction with recallable amounts algorithm, calculating each vertex in network
The visual grid points number or visible range size of corresponding grid unit；
Step 4, the adjacent modes type selected according to step 2 calculates the weight on side in Visual Graph network:
(1) it for 4 grid unit adjacent modes, is considered as grid unit and is connected along 4 directions of reference axis, weight computing is public
Formula are as follows:
Wherein, d is square grid unit sampling interval, i.e. side length；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 reference axis and diagonally connect
It connects, weight computing formula are as follows:
Wherein, d is square grid unit sampling interval, i.e. side length；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 reference axis, 4 directions diagonally
It connects and is connected along 9,10,11,12,13,14,15 with 16 points of 8 directions, weight computing formula are as follows:
Wherein, d is square grid unit sampling interval, i.e. side length；Ei、EjIt is the height of source grid points and target mesh point respectively
Journey value；9,8 directions of 10,11,12,13,14,15 and 16 point and the angle of X-axis be respectively 116.565 °, 153.435 °,
206.565 °, 243.435 °, 296.565 °, 333.435 °, 26.565 ° and 63.435 °；
Step 5, Visual Graph network data is saved.
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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 |
| CN110097636B (en) * | 2019-01-31 | 2023-05-16 | 南京师范大学 | Site selection planning method based on visual field analysis |
| 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 |
| 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 |
| 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 |
| 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 |
| CN112002012B (en) * | 2020-08-26 | 2022-07-08 | 中南大学 | Visibility analysis method for urban area |
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| CN103927788A (en) * | 2014-04-14 | 2014-07-16 | 北京工业大学 | Building ground feature DEM manufacturing method based on city vertical planning |
| CN106128287A (en) * | 2016-08-31 | 2016-11-16 | 中国人民解放军68029部队 | A kind of multimedia 3D topography mutual based on intelligent sound and control method thereof |
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| US6173067B1 (en) * | 1998-04-07 | 2001-01-09 | Hughes Electronics Corporation | System and method for rapid determination of visibility-based terrain properties over broad regions |
| CN103927788A (en) * | 2014-04-14 | 2014-07-16 | 北京工业大学 | Building ground feature DEM manufacturing method based on city vertical planning |
| CN106128287A (en) * | 2016-08-31 | 2016-11-16 | 中国人民解放军68029部队 | A kind of multimedia 3D topography mutual based on intelligent sound and control method thereof |
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