CN101887595B - Three-dimensional digital earth-space data organizing and rendering method based on quad-tree index - Google Patents
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Abstract
The invention provides a three-dimensional digital earth-space data organizing and rendering method based on a quad-tree index, which belongs to the technical fields of cartography, geography information systems and virtual reality. The method comprises the following steps of: unifying common spatial data formats under multi-scale and multi-projection conversion, multispectral, multi-temporal and high-resolution remote sensing satellite images and aerial images as well as digital thematic maps with different scales into same coordinate system, carrying out operation on the attribute of each element and the parameter regulation of quad-tree tiles, outputting in the form of the quad-tree tiles, carrying out quad-tree cutting on three-dimensional landscape map data, leading spatial data into a relevant database, and carrying out unified management. By using the method, common vector data, raster data, altitude data and three-dimensional map data are organically integrated and issued into a three-dimensional digital earth prototype, thereby shortening the time of data preprocessing, improving the execution efficiency and providing a new integration method for three-dimensional digital earth fundamental geographic data dissemination.
Description
Technical Field
The invention belongs to the technical field of geographic information systems, cartography and virtual reality, and particularly relates to a three-dimensional digital geospatial data organization rendering method based on a quadtree index.
Background
The digital earth is based on computer technology, multimedia technology and large-scale storage technology, and carries out multi-resolution, multi-scale, multi-space-time and multi-variety three-dimensional description on the earth by using massive earth information by taking a broadband network as a link.
The digital earth is mainly composed of spatial data, text data, an operation platform and an application model. The space data mainly comprises global space data with large, medium and small scales, various multispectral, multi-temporal and high-resolution remote sensing satellite images, aerial images and various digital thematic maps with different scales of the earth. A basic idea of the digital earth is to cover a high-resolution remote sensing image on the whole globe, and establish an earth landscape model by establishing a digital elevation model and vector data covering the whole globe to form a virtual earth.
The spatial data is mainly realized by combining various different types of spatial data. The surface area of the earth is close to (1.5 multiplied by 10)12) The data volume of the three-dimensional digital earth is extremely large and the data type is complex due to square kilometers, and the data volume increases in a geometric progression along with the improvement of resolution and often reaches hundreds of G or even tens of TB bytes. In the current network environment or on a PC, the huge data is difficult to meet the requirements of real-time transmission and display, so that the transmission of massive geographic data and the real-time drawing of three-dimensional scenes become important contents in digital earth research. The updating of computer storage equipment and the reduction of price provide support for large-capacity data storage, and for a network-based system, the method reasonably organizes spatial data, improves the access speed of mass data, reduces unnecessary data transmission on the network as far as possible, and has important significance for improving the display rate of global three-dimensional scenes.
The global terrain data volume is very large, for example, 1: 25 ten thousand terrain data in the world has a total data volume of more than 20G, and the data volume is conceivable together with images. In order to reduce the consumption of computer memory, accelerate computer processing time, save network transmission time and access multi-resolution ultra-large geographic data at the fastest speed, a reasonable database storage structure must be designed for flexibly managing data from coarse to fine, and such an adaptive database structure must be implemented by using spatial indexes. Data is typically organized hierarchically, with the data at each level being divided into small chunks, and this method of organizing data is commonly referred to as a quadtree structure or a pyramid data structure. FIG. 1 illustrates the structural hierarchy of a quadtree.
Reference to the literature
[1] The basic institution of national spatial information deals with the digital earth [ M ]. beijing: qinghua university Press, 1999.
[2] Goji keyya, duchesnea, li qingquan, etc. contemporary geographic information technology [ M ]. beijing: scientific press, 2004: 87 to 88.
[3] The fourth generation of GIS software research, china graphics press, 2001b, 6A (9): 817 to 823.
[4] Songkefu, bells, research and development of modular geographic information systems, graphic presentations of images, 1998, 4: 314.31.
[5] the method comprises the steps of child dawn thoroughfare, cardia admission, Zhang Yong, construction and rapid display of a global multi-resolution data model, surveying and mapping science 2006.1.
[6] White-built military, Zhao national wins, Chen military based on the global terrain visualization of ellipsoid triangular network, Wuhan university newspaper, 2005.05.
[7] Li de ren, information highway, space data infrastructure and digital earth, proceedings of surveying and mapping, 1999.
[8] Li de ren, development of remote sensing and development of century, university of wuhan, 2003.02.
[9] Wu Yanlan, DEM and several applications, Wuhan university of surveying and mapping science and technology academic papers, 1998.
[10] Penhui, establishment of a digital elevation model based on MicroStation and application research thereof, academic paper of Wuhan university of surveying and mapping technology, 1998.
[11] Xue Yong, Wangjiaqin, Guo Huadong digital Earth grid computing was young [ J ]. remote sensing academic, 2004.8(I).
[12] Yangyong, Guodazhi, digital map calculation model [ J ] based on geographic coordinates, university of southwest traffic, 6 months 2005, Vol 40, No3.
Disclosure of Invention
The invention aims to provide a three-dimensional digital geospatial data organization rendering method based on a quadtree index. The method can unify common spatial data formats under global multi-scale and multi-projection changes, various multispectral, multi-temporal and high-resolution remote sensing satellite images, aerial images and various digital thematic maps of different scales of the earth into the same coordinate system, can perform operations such as attribute adjustment and quadtree tile parameter adjustment on each element, finally outputs visual data in a quadtree pyramid tile form, performs quadtree cutting on three-dimensional landscape map data, and imports geometric data and attribute data of the spatial data into a relational database for unified management. The invention provides a set of detection framework for the final data, and ensures that the data can be stably and correctly provided for the digital earth platform.
The technical scheme of the invention is a three-dimensional digital earth space data organization rendering method based on the quadtree index, which comprises the following design ideas (or the following basic steps):
1) hierarchical blocking mechanism using digital earth data
2) Generating and rendering tiles
3) Fusing multi-source data
4) Detection tool adopting three-dimensional digital earth prototype data
The invention has the beneficial effects that: by using the method, common vector data, raster data, elevation data, three-dimensional map data and the like can be organically fused and issued to the three-dimensional digital earth prototype in a split storage mode of a quadtree pyramid, so that the time for preprocessing the data is obviously shortened, the execution efficiency is improved, and a new integration method is provided for issuing the three-dimensional digital earth basic geographic data.
Drawings
FIG. 1 structural hierarchy of quadtrees
FIG. 2 Cartesian coordinate System
Width and height of the tiles of FIG. 3
Graph 436 degree division
FIG. 5 is a schematic diagram of different levels of division
FIG. 6 level quadtree tile chunking representation
FIG. 7 segmentation rules for layer n quad tree to layer n +1 quad tree
FIG. 8 Tile Pre-load and transition display processing
FIG. 9 map data Source
FIG. 10 three-dimensional landscape map rendering process in 3Ds Max
FIG. 11 single map cutting process
FIG. 12 multiple-map-related cutting process for super large map
FIG. 13 is a hierarchical relationship between linear vector layers and DEM and image
FIG. 14 supports geodetic to Lambert projection coordinates
FIG. 15 supports Jpeg2000 raster data
FIG. 16 supports DEM data
FIG. 17 simple element data model for OpenGIS
FIG. 18 Tile rendering method tool prototype screenshot
FIG. 19 Tile rendering method tool prototypes export data to three-dimensional digital Earth prototypes
FIG. 20 Multi-layer grid fusion with vector data
Detailed Description
The design idea of the invention is described below with reference to the drawings of the specification.
1 digital earth data layered block division mechanism
1.1 surface Tile partitioning mechanism
Cartesian coordinates are taken with the origin (X-0, Y-0.) at the lower left of the projection coordinates, i.e., south poles (-90, -180) (in longitude and latitude), see fig. 2 and 3.
As shown in FIG. 3, the Size of each Tile width and height is determined using "Level Zero Tile Size" (all tiles are squares. the standard Level Zero Tile Size has not been established, but it must be satisfied that it is divisible by 180.
The first layer is divided by 36 degrees, see fig. 4, with 360 degrees longitude and 180 degrees latitude of the earth as the criteria.
The total number of layers is (360/36) × (180/36) ═ 50 layers, the second layer is 18 degrees, the third layer is 9 degrees, and the third layer is similarly arranged, as shown in fig. 5 and 6.
1.2 Tile positioning mechanism
Defining:
A. the map level is from 1, and the number of rows and columns is from 0;
B. extreme values of the map range are expressed by XMin, XMax, YMin and YMax;
C. x Δ represents an abscissa difference, and y Δ represents an ordinate difference;
D. naming of the quad-tree map tiles by adopting a mode of containing map levels and row and column numbers, such as 'zoomx _ row _ column', wherein x represents the map level, and row and column analysis represents the row number and the column number of the current tile;
1.2.1 map quantity calculation
If the first level map is m rows and n columns, the number of rows and columns of the map at the second level is as follows:
line number: mx 2level-1，level≥1
The number of columns: n x 2level-1，level≥1
1.2.2 conversion of geographic coordinates to Tile position
1.2.2.1 calculating geographic range from Tile location
Inputting: rank pair (i, j)
And (3) outputting: extent Range (currentXMin, currentXMax, currentYMin, currentYMax)
The coordinate range of the ith row and j column position slice at the level of the level is calculated by the following formula:
1.2.2.2 calculating Tile position from coordinates
Inputting: (x, y) coordinates
And (3) outputting: i, j (number of rows and columns of picture)
Wherein,
with the data structure of the quadtree, referring to fig. 4, Level 0 divides the earth into 50 regions at 36 degrees, each block at the 0 th layer is divided into four subtrees, and the Level 1 layer is calculated as shown in fig. 7.
Each block has four subtrees, including its own information, and the data is defined as:
double West; v/four directional planar Cartesian coordinate values
double East；
double North；
double South；
Angle CenterLatitude; // tile center latitude and longitude
Angle CenterLongitude；
double LatitudeSpan；
double LongitudeSpan；
int Level; // Level
int Row; // row, column
int Col; // subtree
QuadTile northWestChild；
QuadTile southWestChild；
QuadTile northEastChild；
QuadTile southEastChild；
Each block corresponds to a 512 x 512 texture of the current Level, each edge of each block is divided into 40 equal parts, 41 x 41 or 43 x 43 vertexes are generated, and then the real-time drawing is performed by combining the height information.
Due to the layered and partitioned structure, when terrain data is loaded and rendered in real time, texture pictures and elevation information of a required layer and an area only need to be downloaded from a server, and some pictures can be preprocessed to form direct pictures of three-dimensional fruits without elevation information.
Transition treatment:
the Level (n +1) Level does not download the unprocessed pictures, and still displays the effect of the Level n Level, as shown in fig. 8.
2 generating tiles
2.1 three-dimensional Tile data
The map pre-generation is to cut a map in a specified range into square pictures in a plurality of rows and columns according to a specified size and a specified format (such as TIFF, JPEG, PNG, etc.), a map slice obtained by cutting the map is called a Tile (Tile), and the Tile acquisition flow is shown in fig. 9. Rendering the tiles to obtain only raster graphs corresponding to two-dimensional vector data, image data or digital elevation data, performing three-dimensional modeling on the cut graphs of the three-dimensional landscape map by using tools such as AutoCAD (auto computer aided design), 3DMax and the like, adjusting the cut graphs by using a VRay plug-in, and rendering the whole three-dimensional map.
The three-dimension of the three-dimensional electronic map is 2.5-dimension with rich observation surface, and is a static rendering picture with fixed view angle generated by a three-dimensional model. Before manufacturing, the angle of a camera is determined according to the orientation characteristics of a regional house, the camera and the lighting effect are set in three-dimensional model manufacturing software, a base map is manufactured, and a standard scene is formed and is used by a manufacturer in a unified mode. Objects that are occluded, invisible or in secondary positions may be omitted or simplified. And point, line and surface city entity information layers in the three-dimensional scene are manufactured in a layering mode during model manufacturing, and the three layers of models are overlapped to form the city three-dimensional landscape. Fig. 10 shows an implementation process of rendering a three-dimensional model into a landscape picture in 3d scax.
2.2 Tile cutting
The Net class library has a limit to the image volume and limits the length and width of a loaded single map to be within 15000 × 15000, so that the map cutting is divided into two types of single map cutting and multiple related cutting of an oversized map:
1. single map cutting process:
step 1: correcting the original value of the cutting range to make the length and height values of the original value be multiples of the tile;
step 2: the fixed range is cut from the upper left corner (northwest), a quad tree data structure is adopted among subsequent maps of different levels, a Tile (Tile) on the level two is split into 4 tiles from the level +1, the structure is beneficial to cutting and displaying, but the obtained map has no fixed scale, and the scale changes along with the vertical coordinates of the geography, so that the geography calculation is directly calculated according to the geography coordinates instead of the scale. The map naming rule is that the Nth level map picture is named as zoomN.
The tile naming rule of the x-th level chunk row chunk column is zomx _ chunk.
Wherein the variables define:
x: a current zoom level;
chunkrow: the number of rows of the current tile in the map;
chunkcol: the number of columns of the current tile in the map;
2. a large map multiple correlation cutting process:
step 1: the method comprises the steps of dividing a super large map into a plurality of mutually related small maps, wherein the naming rule of the small maps is uperchunk _ row _ col \ zoomn.
Step 2: cutting the small map by adopting a single map cutting process
The naming rule of the tile after cutting is hyperchronk _ row _ col | zoomx _ chunkrow _ chunkcol \ x _ chunkrow _ chunkcol.
Wherein the variables define:
row: the current small map occupies the row position in all the cut small maps;
and (5) Col: the current small map occupies the column position in all the cut small maps;
x: a current zoom level;
chunkrow: the number of rows of the current tile in the map;
chunkcol: the number of columns of the current tile in the map;
single map cutting process/multiple associated cutting processes for super large maps, as shown in fig. 11 and 12.
3 Multi-source data fusion
3.1 fusing data layers
With the difference of the distance of the viewpoints, the definition of the three-dimensional terrain changes in real time, and at the moment, the system automatically calls DEMs and image data with different resolutions to construct a ground model. When the terrain transits from one detail level to another detail level, the system calls a plurality of terrain data displays at the same time, and the segmentation of the terrain patches changes. If the ground features are not fused with the terrain, the space vector entity can be caused to have the phenomena of 'sinking' and 'lifting'. In order to increase the display speed and the graphic quality, the data of the vector layer is hierarchically managed as the DEM and the image, as shown in fig. 13.
3.2 support projective transformation
By reading the. prj file corresponding to the.shp file and parsing in the WKT format standard, fig. 14.
The parameters include:
the coordinate system is described in terms of OpenGIS WKT (Well known Text). It includes the following information:
A. the name of a general coordinate system.
B. The name of a geographic coordinate system.
C. The earth frame of reference.
D. Reference ellipsoid, ellipsoid semi-major axis and. The derivative of the ellipsoidal ellipticity (i.e., a/(a-b)).
E. A 0 degree meridian name car and its degree of deviation from this initial meridian.
F. One projection type.
G. A list of projection parameters.
H. A unit name and a conversion factor to meters or radians.
I. The name and ordering of the axes.
3.3 multiple vector and raster data type support
The OpenGIS standard is used as a uniform interface, and different data can be supported by the same platform, such as
■ vector data: shape data, mapinfo data, gml data, geojson data
■ raster data: geotiff data, ecw data, jp2 data (see FIG. 15)
■ digital elevation data: DEM data (see FIG. 16)
■ GPS data: GPX format
The GDAL library is used for realizing the support of vector data such as Shape data, mapinfo data, gml data, geojson data and the like.
The vector data is based on a rectangular coordinate system, and a data model or a data structure is obtained by describing geographic elements by points, lines and polygons. Each geographic element has a series of sequential coordinate (x, y) descriptions, which are combined with attributes. We have designed a Geometry model that strictly follows the simple element data specification of the OGC OpenGIS, where basic Geometry in vector graphics is defined, and some simple spatial correlation analysis functions. The most important geometries are points, lines, facets, and thus derived multi-points, multi-lines, multi-facets, curves, lines, loops, polygons, etc., see fig. 17.
The relationship of grid position (pixel coordinates or line coordinates) and geographic reference coordinates is described in two ways. The first, also the most commonly applied one, is the affine transformation.
The affine transform consists of six coefficients, which map the pixel/line coordinates to the geographic reference space with the following relation:
Xgeo＝GT(0)+Xpixel*GT(1)+Yline*GT(2)
Ygeo＝GT(3)+Xpixel*GT(4)+Yline*GT(5)
for the downward-ordered images, GT (2) and GT (4) are both zero, GT (1) is pixel wide, GT (5) is pixel high, and GT (0), GT (3)) represents the upper left coordinates of the upper left pixels of the grid.
Note that: the upper pixel and line coordinates are from the upper left coordinate (0.0 ) of the upper left pixel to the lower right coordinate (pixel wide, pixel high) of the lower right pixel. The center position of the top left pixel/line is then (0.5 ).
Defining: wave band
The raster data for one band corresponds to the GDALRasterBand class in GDAL. It describes the band/channel/layer of a single band. It does not describe the entire image at once. For example, a 24-bit RGB image is generally described in a dataset as three bands, corresponding to the red/green/blue colors.
Each band is specifically interpreted as:
GCI _ defined: default, unknown information.
GCI _ gray index: is an independent gray-scale image.
GCI_PaletteIndex：this raster acts as an index into a color table。
GCI _ RedBand: the red band of an RGB or RGBA image.
GCI _ GreenBand: the green band of an RGB or RGBA image.
GCI _ BlueBand: the blue band of an RGB or RGBA image.
GCI _ AlphaBand: the alpha channel of the RGBA image.
GCI _ hue band: hue of HLS image.
GCI _ SaturationBand: saturation of HLS images.
GCI _ LightnessBand: light intensity of HLS image.
GCI _ CyanBand: CMY or CMYK images.
GCI _ magenta band: magenta bands for CMY or CMYK images.
GCI _ YellowBand: yellow bands for CMY or CMYK images.
GCI _ BlackBand: black band of CMYK images.
Support data attribute tuning: supporting data attribute tuning
3.4 quadtree tile parameter adjustment
Adjusting the tile according to the quadtree parameters, then exporting the tile, and exporting a parameter configuration file, wherein the format is as follows:
<MapParam>
<tilesize>128</tilesize>
<ColumnNum>4</ColumnNum>
<RowNum>2</RowNum>
<centerX>-243383.913445131</centerX>
<centerY>4376213.31793891</centerY>
<ZoomParam>2</ZoomParam>
<MaxLevel>4</MaxLevel>
<Xmin>-4663419.25215648</Xmin>
<Xmax>4176651.42526622</Xmax>
<Ymin>2166195.64858323</Ymin>
<Ymax>6586230.98729458</Ymax>
</MapParam>
3.5 building of spatial database
A. Database connection
Comprises the following steps: checking in a Sql mode; and ② Windows identity authentication.
B. A new database is built, including database files (. MDF) and log files (. LDF).
C. Spatial database deployment: fields "blue. general _ COLUMNS" and "blue. spatial _ REF _ SYS" are generated in the specified database and used for storing the geometry layer and the spatial WKT information, respectively.
D. Vector data import
And importing the shape data into a database. The shp file generates fixed fields oid, the _ geom, zID, the _ geom _ Envelope _ MinX, the _ geom _ Envelope _ MinY, the _ geom _ Envelope _ MaxX, and the _ geom _ Envelope _ MaxY, which respectively store a stream number, geometric feature data, a map layer number, a bounding box abscissa minimum value, a bounding box ordinate minimum value, a bounding box abscissa maximum value, and a bounding box ordinate maximum value. From the dbf file, attribute information fields are generated, such as: "city" and "population".
3.6 geographic data bulk search
And searching the corresponding longitude and latitude by inputting the geographic position based on the longitude and latitude search of the Geocoding. Transmission format is "http://ditu.google.com/maps/geooutput＝xml&qAnd analyzing the returned xml format file according to the request of the geographic position, wherein point. By using datasets and data adapters, a batch of data search is conducted based on sqlserver 2005.
4 the map is edited by the method and the data is exported to the digital earth for demonstration, see fig. 18, fig. 19 and fig. 20.
Claims (2)
1. A three-dimensional digital earth space data organization rendering method based on a quadtree index comprises the following steps:
(1) performing mutual operation on geographic coordinates and tiles by adopting a digital earth data hierarchical partitioning and tile positioning mechanism based on a quadtree pyramid, and calculating a geographic range according to tile positions;
(2) rendering a three-dimensional map based on a quadtree pyramid, layering and superposing three-dimensional models to form urban three-dimensional landscape, and cutting the rendered three-dimensional map into a single map and a plurality of map tiles of an oversized map;
(3) establishing a two-dimensional Cartesian coordinate system to support multi-source data fusion, abstracting vector data into multi-point, multi-line, multi-surface, curve, straight line, circular line and polygon for storage fusion based on a Geometry model following simple element data specification of an OGC OpenGIS, performing affine transformation on raster data according to the coordinate system, extracting data fusion of red, green and blue three wave bands, reading a universal wave band of elevation data, converting various projection transformations into a unified coordinate system of geodetic coordinates, and performing fusion processing on the multi-source data.
2. The method of claim 1, wherein in step (1), the earth is divided into 50 regions at level 0 by 36 degrees, each region corresponds to a 512 × 512 texture of the current Leve1, each edge of each block is divided into 40 equal parts, and 41 × 41 vertices are generated;
in the step (2), the city entity information layers of points, lines and surfaces in the three-dimensional scene are overlapped to form a city three-dimensional landscape during model making, and the rendered three-dimensional map is cut to ensure that the length range of the loaded single map is within 15000 x 15000;
in the step (3), a two-dimensional Cartesian coordinate system is established to support multi-source data fusion; and finally, storing bounding box information and attribute information of elements in the fusion data into a relational database, and adopting Geocoding for indexing.
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