The spatial database management system has been used in the construction of ... models, the integrated management of image data, DEM and vector data are ...
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Object-Oriented and Integrated Spatial Data Model for Managing Image, Dem and Vector Data
Jianya Gong
Deren LI
Wuhan Technical University of Surveying and Mapping, Wuhan 430079, China Abstract
The new generation of GIS demands the integrated management of vector, image and DEM data.
This paper brings forward a vector, image and DEM integrated spatial data model on the basis of the object-oriented idea. It also discusses the implementation method of this integrated spatial database management system. The spatial database management system has been used in the construction of China Spatial Data Infrastructure as the base of a GIS software, GeoStar. It reveals that the system has high efficiency and feasibility to manage national, provincial and city spatial data of multi-scales and multi-sources.
1. Introduction: The integration of remote sensing and GIS is the long-term goal both remote sensing and GIS has sought to. On one hand, remote sensing is the important data source of GIS and the means of data updating. On the other hand, GIS is the auxiliary information of remote sensing image processing and classification, which is used to automatically extract semantic information. In the history of developing, there are three different kinds of integration (Ehlers, 1989). One is the separated but parallel integration (different user interface, different tools and different database). The second is seamless (the same user interface, but different tools and different database). The third is total integration (the same user interface, tools and database). However, all the integration is aiming at the single image or the integrated management of two data in one workspace. With the rapid development of remote sensing and digital photogrammetry technologies, the image data become more and more, and gets more and more attention (Fritsch, 1989). For example, one basic idea of digital earth is to cover the surface with high resolution remote sensing images, and by means of DEM covering the world, to establish the virtual landscape of the Earth. On the other aspect, the cybercity has got more and more attention. In the establishment of these models, the integrated management of image data, DEM and vector data are in great need. It is not the multi-sources data management of a single image or a small area, but the management covering a city, a province, or a country and even the whole world. The commercial GIS software packages can handle the large spatial database with vector data format. However, most of them are not efficient for managing image database and DEM database. Therefore, it is necessary to develop a spatial database management system that can integrate various data (Figueroa, 1990). As the development of contemporary computer technology, hardware absolutely meets the need of the multi-sources integrated management. The external memory of computers is able to extend to tens of TBs, and even thousands of TBs. The current urgent problem is that the commercialized GIS software cannot meet the need. The popular GIS software in the current market has already had the mature technology to manage vector database. However, it is not adaptable to manage image database, DEM database and the integrated three databases. In order to manage the three-integrated spatial databases, Wuhan Technical University of Surveying and Mapping (WTUSM) has developed a new generation of GIS software, GeoStar, on the bases of object-oriented technology and
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integrated spatial database management system. The paper firstly introduces the object-oriented integrated spatial data model. Then it presents the strategies of implementation of the proposed data model and the integrated spatial database management system. Later on, an exemplary project is illustrated. At last the paper concludes by summarizing the major findings. 2. Object-oriented integrated spatial data model Object-oriented integrated spatial data model is the outcome of the combination of object-oriented and database technologies. Object-oriented technology has become the mainstream of the contemporary computer technology. The object-oriented technology will become the headstone of the new generation of software system structure among numerous fields. Object-oriented data model and object-oriented spatial data management system has always been the objective that the field of GIS has pursued (Gong, and Li, 1992). From the end of the 1980s and the beginning of the 1990s, people have paid comparative attention to the application of object-oriented technology in the field of GIS, as the software technology is changing constantly. There are some object-oriented GIS software packages. Some spatial data transfer formats also adopt the object-oriented logical model, such as, SDTS and CNSDTF (Gong, 1998). U.S. Spatial Data Transfer Standard (SDTS) (USGC, 1992) and China GeoSpatial Data Transfer Format (CNSDF) also adopts the object-oriented logical model. In object-oriented data model, the core is object. Object is the abstract of entity of the objective world in matter space. The spatial object has two obvious features: one is the geomatric feature, which has size, shape and position. The other is physical feature, viz. it is road, river or house. According to the physical feature, the spatial object will be coded generally. The country has the sorting and coding standards for the spatial features as well. According to the geomatric feature, the spatial object is defined into the following types: ⑴ Pure geomatric type. It only gets the geomatric position such as an independent point or a contour line. There is no correlative relation among objects. ⑵ Geomatric topological type. It has both the geomatric position and topological relation, such as common arc and node. ⑶ Pure topological type. It has only the topological correlative relation. It is often used in the operation of defining spatial analysis. ⑷ Spatial feature. It has the corresponding feature coding and attribution description, such as oil well, house and park. ⑸ Non-feature type. It has no certain feature definition. It is used as a convenience for the expression of spatial data and organization of the in-between object, as a pure node or the common arc of a polygon. The first three types are distinguished by geomatric concept, while the left two types are distinguished by attributive concept. The above definitions are helpful for the description of the following spatial objects. 2.1
Zero-dimensional spatial object
⑴ Independent point feature. It belongs to the pure geomatric type. But it is a spatial feature having corresponding to feature coding and attributive table. ⑵ Pure node. It is not a feature but a geomatric topological element. The node is used to describe its correlative relation and geomatric position with the arc. ⑶ Node feature. It either belongs to a geomatric topological type or is a spatial feature. For instance, the node among electronic lines is often a power distribution station.
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⑷ Annotation reference point. It is used as a reference to record the position of annotation. It can be stored in the data structure of annotation. ⑸ Polygon label point. It is the auxiliary information of the polygon and can be stored in the polygon data structure. The above first three types of spatial objects have much comparability and overlapping concept relation. Therefore, when designing the data structure, they are treated as one type of object, called node-point type. They are divided into different objects by means of the descriptive code. 2.2
One-dimensional object
⑴ Topological arc. It belongs to the geomatric topological type. Arc has got no branches but the starting node and the ending node. It may be a part of a linear feature or the border of a surface feature. It even may be either the border of a surface feature or one part or whole part of one or more linear feature. Generally speaking, the arc itself has no feature meaning. However, if an arc itself is a linear feature, it can endow the feature with coding directly and link the feature to the attributive table. The topological arc may have geomatric types of links, circles, arcs and smooth curves. ⑵ Non-topological arc. It is a pure geomatric feature. It may be called Spaghetti in same systems. The contour line is a non-topological arc. Commonly, it is unnecessary to take its starting node, ending node, left polygon and right polygon into consideration. It is much simpler than the above topological arc. According to the shape, non-topological arc may be divided as smooth or not smooth. Topological arc and non-topological arc may be merged into one type. They share one data structure and are distinguished by geometric descriptive code. ⑶ Linear feature. A linear feature is composed of one arc or several arcs. Linear feature is allowed to have branches and intercross, so that it can deal with the problems as fluvial drainage area and traffic. Linear feature must have the attributive coding and be linked to the attributive table. 2.3
Two-dimensional object
⑴ Simple polygon: Polygon with one external border but without any internal island. ⑵ Polygon with island: Polygon with external border and one or more internal island. ⑶ Compound polygon: Polygon composed of several simple polygons or polygons with island. ⑷ Universal polygon: Polygon with internal island but without external border. ⑸ Pixel: One two-dimensional image element. It is the smallest image element that cannot be divided any more. ⑹ Grid: The intersection grid point of two-dimensional lines. 2.4 Aggregative object ⑴ Digital image: It is a two-dimensional matrix that can make one picture and arrange pixels in the space regularly. ⑵ Raster: It is the regular or contiguous regular grids or pixels of a certain surface. It is often a rectangle or a square. ⑶ Layer: It is composed of one or more feature classes. It may be the aggregation of vector data, or image and raster. In fact, we abstractively call zero-dimensional object as "point object", one-dimensional object as "linear object" and two-dimensional object as "surface object". In order to directly describe spatial object and the state and quality of the surroundings, commonly annotation is needed. Thus, it may be called annotated object as well.
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Figure 1 shows the object-oriented integrated data model. In this data model, there are four types of spatial entity objects: point object, linear object, surface object and annotated object. They are considered as the superclasses of all spatial features. Each superclass is divided into sub-classes according to its physical attributive features. One or more feature classes constitute a feature layer, which is logical. One feature class may go across several feature layers, so that it is much more convenient to manage data. For example, a running river may be in the water system layer or the traffic layer. In this way, unlike the Coverage model, it is unnecessary to copy data from one layer to another. Feature class is the core here. Project
Partition1
Partition2
Partition3
ٛ
Layer1 Layer2 Layer3 Class3 Class4 Point
LINE ARC Routine
SURFACE Polygon Region
Annotation
Class1
Class4
Class7
Class2
Class5
Class8
Class3
Class6
Class9
X Y (Z)
DEM Image Figure 1
An object-oriented integrated spatial data model
A workspace is a working area including all feature classes in the area or several feature layers. Several workspaces can overlap together. On the transverse orientation, several workspaces may compose a project. A project means the region being researched or the area relating to a GIS project. For example, it may be a city, a province, and even a country. A project is a spatial database. In order to manage spatial objects of integrated image, DEM and vector, image and raster DEM are defined to be two layers. The operation and management of the two layers is similar to that of the feature layer. However, the storing method is different. Image layer and DEM layer can be placed in either a workspace or a project. When they are placed in the workspace, they may be the image or DEM of a single workspace. But when in the project, image and DEM layers databases are to be established at the first hand. An integrated management system to manage vector, image and DEM databases is accomplished.
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Integrated Spatial Database
Vector
Image Database
Figure 2
DEM
Integrated spatial database
Application program Interface
Object Manager
Object Storing Manager
Spatial Database
Figure 3
Spatial object management engine
3. Implementation of the object-oriented integrated spatial database management system In order to simplify the implementation, we put the vector, image and DEM data into three sub-databases. As shown in Figure 2, the three databases can be established separately, i.e., by using three types of approach to manage the databases. After establishing various types of databases, a Dynamic Linking Library (DLL) is developed for the integrated management. Thus, it is able to use image database and DEM database in the vector database management system. Similarly, it is able to use vector database and image database in the DEM database management system. The vector data can directly adopt the object-oriented technology. Therefore, we have developed an object-oriented spatial database management engine in charge of operation and management of vector data. As it is shown in Figure 3, spatial object management is mainly composed of object storing manager and object manager. Object storing manager is mainly in charge of accessing of various spatial objects, establishing spatial index. In our system, a grid-based spatial index is used (Li, Gong and Li, 1998). The object storing manager also accomplishes to store permanent objects and make spatial operation log and resuming the spatial objects when it is necessary. Object manager is mainly in charge of producing spatial objects, distributing the unique ID between object and workspace, attempering spatial objects, completing every fundamental spatial query, maintaining the consistency of spatial objects, accomplishing multi-users management and management of feature classes, feature layers, workspaces and projects under the network circumstance. Image database module is to divide image block and establish pyramid. As the data quantity of one image is too large, it is difficult to meet the need of real-time attempering. Therefore, it is necessary to partition the image further more. For instance, as a block, 512*512 may index to the pointer and access the data block directly, according to the spatial
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position by means of indexing the image block, when the image is panning. Another problem is that when the scale is decreasing, we need to see more abstract feature image. If we directly call data from the original image and then extract, it will be slow. Thus, a pyramid is needed. Data on different layers of the pyramid are called according to different display scales. The aim to establish a DEM database is to organize all relevant data efficiently and establish a spatial index according to the geographical distribution. Therefore, data in the database can be rapidly accessed, so that seamless panning in the whole area can be accomplished. Especially when adopting the pyramid data structure, data in different layers can be called automatically, flexibly and conveniently according to the size of the display area. For example, we can either take everything into our sight, or see tiny details in a part. Through the structure of "Project-Workspace-Row", the elevation of spatial position in DEM database can be decided uniquely. In order to enhance the browsing efficiency of the whole data, DEM database has adopted the pyramid hierarchy and system of calling data from different layers according to the size of the display area. The pyramid hierarchy of DEM database is shown in Figure 4.
DEM Database Project (1: 10,000)
Basic Scale Data
Auxiliary Database (1: 250,000)
Auxiliary Database (1: 50,000)
Auxiliary Database (1:1,250,000)
DEM Workspace
Elevation Matrix Row
Source Data INFO resolution, sizes, position, etc. Figure 4
DEM database structure
4. Application examples of object-oriented integrated spatial database management system Having object-oriented and integrated spatial database management system as the core, WTUSM has developed GIS software, GeoStar. This software is a package, which includes the functions of GIS, remote sensing image processing and digital photogrammetry. The system structure of the software is shown in Figure 5. The physical correlative relation of the software is shown in Figure 6.
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Map scanning vectorizing
Image processing
Digital pho-togramm
DEM and application
Mapping
Spatial query
Spatial analysis
Spatial data management engine and interface (API)
ODBC
SQL Server
Oracle
Object-oriented database management system
Sybase
…..
Figure 5
ARC/ INFO
Spatial data transfer
MGE
MapInfo
……
The architecture of GeoStar
Application Geomap
GeoStar Project
Internet GIS
GeoGrid
GeoTIN
GeoStar
GeoImageDB
DPW
GeoImager
GeoScan Query Digitization Edit Desktop Cartography Analysis Application
Field surveying Figure 6
The physical correlative relation of the software
Being the foundation of GeoStar, the object-oriented integrated spatial database management system is used to manage multi-types of spatial data with scales of 1:1,000,000 and 1: 250,000 for whole China. A provincial GIS pilot project
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including vector, image and DEM data has been built in Guangdong Province. The system involves TM remote sensing image, DEM and vector data with the scale of 1: 250,000 covering the whole province, SPOT-TM image data with the scale of 1:50,000 covering the Zhujiang River Delta, and aerial digital orthophoto and DEM, vector data with the scale of 1:10,000 covering the Zhujiang River Delta. All of them have formed a multi-scaled and multi-sources spatial database. Since a multi-scaled database is built, the system can access data in different levels according to the scales users have decided. Figure 7 is the general picture of a province. When we are interested in a city, we may zoom out gradually and get images with scales of 1:250,000 (Figure 8), 1:50,000 (Figure 9) and 1:10,000 (Figure 10). This method is also used for vector data, DEM data or combination of the three data.
Figure 7
The general image map of Guangdong Province
Figure 8
The image map with the scale of 1:250,000
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Figure 9
The image map with the scale of 1:50,000
Figure 10
The image map with the scale of 1:10,000
Conclusions With the development of remote sensing and digital photogrammetry technologies, there will be more and more image data and DEM data. How to manage the two kinds of databases and integrate them with vector database in a GIS, has been received more and more attentions. An object-oriented and integrated spatial data model has been proposed in this paper and a spatial database management system based on the data model has been implemented in GeoStar, which is developed by WTUSM in China. A pilot project based on GeoStar platform has finished. The result illuminates that the data model and the database management system are of high efficiency and great adaptability.
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REFERENCES [1] Fritsch, D., Et al.: Object-oriented management of raster Data in Geographic Information System, 16th Congress of ISPRS, 1988,B4, Kyoto. [2] Figueroa, D. Et al.: Integrated GIS Discussion and Examples, Proceedings of EGIS’90 Amsterdam, 1990. [3] Ehlers, E. Et al,: Integration of Remote Sensing with Geographic Information: a necessary Evolution, Photogrammetric Engineering and Remote Sensing, 1989,No. 11. [4] USGC: Spatial Data Transfer Standard, 1992. [5] Jianya Gong,: A proposal for China GeoSpatial Data Transfer Format, Proceedings of CAGIS’98 Conference.1998. [6] Jianya Gong, Deren Li: Object-oriented model based on the unified data structure. Archives of 17th ISPRS, Washington D.C. ,1992. [7] Aiqin Li, Jianya Gong and Deren Li: Organizing for large seamless geographical databases, Journal of Wuhan Technical University of Surveying and Mapping, 1998,Vol. 23, No.1
