Comparison between raster data and Vector Data

Raster Data Structure

A grid structure uses a regular array to represent the data organization of spatial objects or phenomena. Each data in an organization represents the non-geometric characteristics of objects or phenomena.

The distinctive feature of the raster structure is that the attribute is obvious, and the positioning is implicit. That is, the data directly records the pointer or data of the attribute, and the position is converted to the corresponding coordinate based on the row and column numbers.

The encoding method of raster data: Direct raster encoding means to regard the raster data as a data matrix and record each row (or column) one by one.Code; Compression encoding, including

Freann chaincodes are suitable for storing graphic data;

The length encoding is implemented by the code that records the similarities of several adjacent attributes in rows or columns;

The block code is extended to two-dimensional with a fixed length code. The square area is used as the record unit;

Quad-tree encoding is one of the most effective methods to compress raster data. It can also improve image operation efficiency and have a variable resolution.

Vector Data Structure

The vector data structure accurately represents geographical entities such as points, lines, and polygon by recording coordinates as much as possible. The coordinate space is set to continuous, allowing precise definitions of any position, length, and area.

Significant Features of vector structure: obvious positioning and implicit property.

Vector data encoding method:

For point and line entities, space information and attribute information are directly recorded;

For polygon, coordinate sequence, tree index, and Topology encoding are used. The coordinate sequence method is composed of the X and Y coordinates of the boundary of a polygon and its description information. It is the simplest method of polygon vector coding, and the file structure is simple, however, the polygon boundary is stored twice to generate data redundancy and lacks the neighbor information. The tree index encoding method digitizes all the boundary points and stores coordinate pairs sequentially, the vertex index is associated with the boundary line number, and the line index is associated with each polygon to form a tree index structure, eliminating the border data redundancy problem of adjacent polygon; the topological structure encoding method completely solves the problem of neighbor and island information processing by establishing a complete topological relationship structure.AlgorithmThe complexity and size of the database.

Comparison of vector Raster Data

Advantages and disadvantages of Vector Data:

The advantage is that the data structure is compact and the redundancy is low, which is conducive to network and retrieval analysis. The graphic display is of good quality and high accuracy;

The disadvantage is that the data structure is complex and polygon overlay analysis is difficult.

Advantages and disadvantages of raster data:

The advantage is that the data structure is simple, which facilitates Space Analysis and surface simulation, and the current situation is strong;

The disadvantage is that the projection conversion is complicated because the data volume is large.

Comparison:

Operations on raster data are generally easy to implement, while operations on vector data are complicated;

A raster structure is an approximation of a vector structure to some extent. A large amount of data is required for the same ground object to achieve the same precision of vector data; in terms of Coordinate Position search and Polygon Shape and area calculation, the grid structure is more effective, and it is easy to combine remote sensing and information sharing. The vector structure is more efficient for searching topological relationships, only vectors can be used to completely describe network information, and the accuracy is high. For geographic information system software, it is very effective for both of them to take advantage of each other.

Vector raster mutual conversion algorithm

Vector to raster: the inner point diffusion method, that is, the inner point of the polygon extends to the neighboring point until it reaches the boundary.

Until the complex integral algorithm, that is, the complex integral is calculated based on the Closed Boundary of the polygon to be recognized to determine the relationship between the two points.

Generation; Ray algorithm and scanning algorithm, that is, the ray directed from a point outside the graph to the point to be determined, through the intersection of the ray and the boundary of the Polygon

Number to determine the internal and external relations. The boundary algebra algorithm is a vector to Grid Algorithm Based on the integral idea, suitable for recording

Returns the vector data of a polygon that records the topological relationship. The method starts from a point on the polygon boundary and searches the boundary clockwise.

Line, the grid with the same row coordinate on the left side of the upper line minus a value, and all the grid points on the left side of the lower line boundary plus

This value completes the polygon conversion after the boundary search is complete.

Raster to vector: This is the topology that extracts the boundary and boundary of the polygon area represented by the grid set with the same number.

Link, and represents the process of forming a line of the vector format. The process includes polygon boundary extraction, that is, using Qualcomm filter.

Binarization raster images; Line tracing, that is, searching for each arc segment from one node to another; Topology

Generation and handling of redundant points and smooth curves.

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Raster Data and Vector Data

GISThe research data is geospatial data, which is the root cause of the difference from other systems. Raster Data and vector data are the most basic spatial data organization methods in geographic information systems.

Raster Data is a two-dimensional matrix that represents the data organization of spatial objects or phenomena. each matrix unit is called a cell ). each data in the grid represents the attribute data of the Ground Object or phenomenon. therefore, the raster data has obvious properties and is implicitly positioned. the vector data structure expresses the real world in the form of points, lines, and planes. It has the characteristics of obvious positioning and implicit attributes. Vector Data has the advantages of compact data structure, low redundancy, high expression precision, and good graphic display quality, which is conducive to network and retrieval and analysis. It is widely used in GIS, especially in small area (large scale) plotting. However, with the wide application of RS and data compression technology, the improvement of computer performance overcomes the disadvantages of large data volumes in raster data. Large-scale applications of raster data will take a dominant role. It is mainly based on the following advantages:

(1) With the development of RS technology and large-scale application, the use of raster data will promote the integrated development of RS and GIS. RS becomes an important data source for dynamic updates of spatial data. Remote Sensing Images are stored in a pixel-based grid structure, which greatly improves the pre-processing capability of raster data. These data can be directly generated or converted to GIS-based grid data.

(2) raster data can greatly improve the spatial and temporal data analysis capability of GIS. Raster Data is widely used in image algebra and spatial statistical analysis, which can facilitate the establishment of GIS models. The high version of ArcGIS software has outstanding performance in this aspect.

(3) 3D visualization has become a new trend for dynamic simulation of the real world. raster Data uses two-dimensional images to simulate geographical entities. Raster data can be used to increase the dimension to achieve three-dimensional visualization.

(4) with the development of Web GIS, the data structure of raster data is simple and realistic.ProgramDesigners and users understand and use it. in particular, the establishment of image sharing standards (such as GIF) facilitates the sharing of grid data in GIS. therefore, raster data is more practical in information sharing. therefore, with the development of GIS, raster data and vector data have developed to varying degrees. However, raster data is more widely and effective than vector data.

To put it simply, vector data means that the image it represents will not change the effect after being enlarged.
Raster data is the image that it shows. After the image is enlarged, it will change the effect and become blurred.
Only vector data can be operated.

Vector Data features
Basic: Space Point
Geographical entity: consists of straight lines and arcs connected by points.
Entity type: point, line, Polygon
The entity has spatial locations (x, y), shapes, sizes, and so on.
Topological Relationship: describes the relationship among the geographical entities, such as the joining, connection, and inclusion. Disadvantage: Data Storage surplus and inconsistency: adjacent public boundaries are digitalized and stored twice
The topological relationship of spatial entities cannot be explicitly expressed.
It cannot express complex polygon: islands, holes, etc.
The form of raster data means that a geographical entity uses the row and column of a grid unit as the location identifier. The number of rows and columns depends on the resolution of the grid and the characteristics of the object. Raster Data is different from Vector Data in that point and line are used as the basic unit of plotting. It is a collection of systems in small areas of the same size. These small areas are considered similar. Raster Data is widely used in remote sensing images, digital photographic images, and various gridded data forms.
Grid Unit: a discrete, consistent area on the ground
Resolution: select the appropriate resolution
Resolution too small large data volume
The resolution is too high and the expression is insufficient.

Raster Data has the following advantages:
Simple data structure, used for remote sensing and data scanning, advanced spatial simulation and analysis capabilities
Disadvantages: a large amount of storage space and graphic output are required depending on Pixel scale, difficult projection transformation, and difficult to establish topological relationships.
Vector Data
Advantages: requires a small amount of disk storage space, easy to establish topological relationships, high resolution, beautiful graphic output
Disadvantages: complex data structures, incompatible with remote sensing data, expensive software and hardware, difficult analysis in some spaces, and time-consuming overlay analysis of multi-layer vector maps

Vector images, also known as object-oriented images or graphic images, are mathematical defined as a series of points connected by lines. The graphic elements in a Vector file are called objects. Each object is a self-contained entity with attributes such as color, shape, outline, size, and screen position. Since each object is a self-contained entity, you can move and change its attributes multiple times while maintaining its original clarity and curvature without affecting other objects in the legend. These features make vector-based programs especially suited to legend and 3D modeling because they generally require the ability to create and operate on a single object. Vector-based plotting is independent of resolution. This means they can be displayed to the output device at the highest resolution.
Raster images, also known as bitmaps, are generally used for photo quality image processing and are composed of many "pixels" like small blocks. It is represented by its position and color value, which can show changes in color shadows.
Comparison between a vector chart and a point bitmap
A vector image has nothing to do with resolution. Therefore, no matter how large a vector image is, it does not affect its quality and effect. If the vector image is enlarged, the computer will recalculate the new image based on the existing resolution.
The quality of the bitmap depends on the resolution. After a bitmap is enlarged several times, the "Mosaic" phenomenon appears obviously.
A vector graph can be edited flexibly. The basic element of a vector graph is an object, and each object is a self-contained entity. Changes to an object do not affect unrelated objects.
Click Bitmap Editing is restricted. Point bitmap is the arrangement of points (pixels). If a point is moved or changed, it will affect other points.

2 Concepts of raster and Vector Data Structures
The data structure based on the Raster Model is short for the raster data structure, which refers to dividing the space into a regular grid, the corresponding attribute values are given on each grid to represent a data organization form of a geographical entity. The vector data structure is based on the vector model, using Euclidean (Euclid) point, line, surface, and their combination in ry represent the spatial distribution of geographical entities. For spatial data, raster data includes various remote sensing data, aerial test data, aerial radar data, and image data of various photos, and gridded map image data, such as geological maps, topographic maps, and other professional image data. From the perspective of type, it can be divided into two-value map, grayscale map, 256-color index and classification chart (single-byte chart), 64 K high color chart (index chart, classification chart and integer professional data) (dubyte chart), RGB true color chart (3 byte chart), rgbp transparent true color stacked chart, and so on. Common data formats include tiff, JPEG, BMP, PCX, and GIF. There is more vector data, and almost all GIS software has its own vector data in a specific format. Currently, the most common vector data formats include coverage, e00 of ARC/INFO, MRG of 正, MIF of Mapinfo, DXF of Autodesk, DWG, and dgn of Intergraph. In GIS and digital plotting, the fusion of the same data structure and the two data structures forms the main content of spatial data fusion. Professional 3 s station 3s8.cn
3. Convergence of raster data
In digital plotting and GIS engineering, raster image data of different sources, precision, and content is often used for combination to generate new raster images. Currently, the use of various multi-source image processing and analysis systems opens up a new way for the implementation of raster geographic information systems, which can achieve various fusion of raster data. In digital plotting, the fusion of multi-source Raster image data is already very common.
3.1 Integration Method
In digital graphics, image fusion involves color, optics, and other fields. It is specialized in image processing software (such as ERDAS, PCI, photomapper) or general image processing software (such as PhotoShop) can be done, mainly through the way of image processing transparent Overlay display of each layer of the grid map. Generally, image registration, image adjustment, and image combination are required. The specific process is as follows:
(1) image registration. Various images may produce geometric distortion due to various reasons. To make the two or more images correspond to different features, the resolution is consistent. Before fusion, geometric correction and registration of image data are required, which is the prerequisite for image data fusion.
(2) image adjustment. To enhance the image effect after fusion and the needs of a specific content, perform some necessary processing, such as the contrast and brightness changes to improve the image definition, edge enhancement (sharpening) or contrast enhancement made to highlight the edges or specific parts of an image, and color changes made by changing the color of a certain part of the image.
(3) image combination. For the superposition of two or more common Raster image data, you need to perform transparent processing on the upper layer image to display the image of each layer. The transparency depends on the specific situation. In the processing of remote sensing images, due to the particularity of their images, the composite methods between them are relatively complex and diversified. Among them, color synthesis is the most effective and most widely used. China 3 s bar 3s8.cn
3.2 Application Analysis
In practical applications, the following are the most commonly used methods for data fusion between raster images:
(1) fusion between remote sensing images. It mainly includes the fusion of remote sensing data of different sensors and the fusion of different remote sensing data. Information sources from different sensors have different characteristics. For example, the fusion of TM and SPOT remote sensing data can improve the resolution of high-tech images and maintain rich spectral information; the integration of different remote sensing data is of great practical significance for dynamic monitoring, such as flood monitoring and meteorological monitoring.
(2) fusion of remote sensing images and map images. This is a method that is widely used at present. First, remote sensing images and raster DEM are integrated to generate three-dimensional 3D landscape images, showing realistic results; second, with the help of the Information cycle dynamics and richness of remote sensing images, after fusion with various map images, we can find the changed regions from the rapid changes of remote sensing images, update data and perform various dynamic analyses.
(3) fusion between map images. To better understand the terrain and landform of the region, or to comprehensively compare and analyze the relationships between various resources in the region, the map and image data of different contents in the region are integrated. Such as topographic maps and various professional images such as geological maps, land use maps, geographic maps, forestry resource status maps, and so on.
4. fusion between Vector Data
Vector Data is the most important data source in GIS and digital plotting. Currently, many GIS software have their own data formats. Each software has its own specific data model. The diversity of these software leads to the differences in the format and structure of vector data storage. To share data among systems, multi-source data must be integrated. The fusion between vector data is the most widely used form of Spatial Data Fusion, and is also the focus of spatial data fusion research. Currently, there are many methods to integrate vector data. The most important and widely used method is to convert the data format, that is, to integrate the spatial data model, and then correct the geometric position, the last step is to re-classify and combine and define all the map data elements. China 3 s bar 3s8.cn
4.1 Data Model Integration
Because each data format has its own data model, format conversion is to convert data in other formats through a dedicated Data Conversion Program to change the data format of the system, this is the main way for GIS software systems to share data. For example, the integration of Arc/Info and MapInfo requires format conversion and unified to a spatial data model. This method is generally performed through the interchange format. Many GIS software have developed clear code interchange formats to exchange data with other software, such as the e00 format of ARC/INFO, the shape format of ArcView, And the MIF format of MapInfo. Data conversion between different software can be achieved through the exchange format. In this mode, other data formats are converted by a dedicated Data Conversion Program and copied to the data in the current system. Currently, it is recognized that several important commonly used spatial data formats are: ESRI's ARC/INFO coverage, arcshape files, and e00 formats; Autodesk's DXF format and DWG format; mapInfo format, Intergraph dgn format, and so on.
4.2 ry Position Correction
For data with the same coordinate system and scale, the data accuracy may vary depending on the technology, human or frequent data conversion, or even due to different software factors. During the fusion process, the geometric positions must be unified. If the precision requirement is not high, in order to improve work efficiency, the data accuracy of the current system should prevail within the permitted range, and the geometric position of the other or several types of data should be corrected. For example, data with high precision should be taken as the standard to correct data with low precision.
4.3 redefinition of map data elements
The integrated spatial vector data should be reorganized and defined based on issues such as element hierarchy, encoding, symbol system, and element selection.
(1) Unified Classification and encoding. Spatial data is generally divided by map elements, such as water systems, transportation, topography, and notes. Each layer can be divided into three types as needed: points, lines, and surfaces, and uses the encoding method to express its attributes. For data that is integrated into the current system, a unified element layer and element encoding should be formulated based on the map elements or specific needs based on the current data.
(2) Unified symbol system. This is currently a difficult point for Vector Data conversion. Due to the different definitions of symbols in GIS software, the symbol generation mechanism may vary greatly, the converted data is difficult to unify the symbols, and the accuracy of the symbols may be different from that of the original data.
(3) Comprehensive Selection of data. Repeated expression of the same element should be involved in space vector data of different formats in the same region. There are generally the following principles: replacing simple, accurate, low-precision, and new-replaced old in detail, but sometimes to highlight certain thematic elements or to adapt to certain needs, comprehensive trade-offs should be made based on specific situations.
The disadvantages of the data conversion mode are obvious. Due to the lack of a uniform description method for spatial objects, it is difficult to fully and accurately display the original data information after conversion, which often causes some information loss, for example, the topological relationship of ARC/INFO Data may no longer exist after format conversion.
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5. Fusion of vector data and grid data
The Grid Structure and vector structure of spatial data are two completely different ways to simulate geographic information. In the past, the two structures were generally considered incompatible. The reason is that the raster data structure requires a large amount of computer memory for storage and processing to achieve or close to the same spatial resolution as the vector data structure, while the vector structure is in some specific forms of processing, many technical problems are hard to solve. The raster data structure is easy for spatial analysis, but the accuracy of the output map is slightly poor. On the contrary, the vector data structure has a small amount of data and can output beautiful maps, but space analysis is quite difficult. At present, the data fusion of the two formats has become possible and widely used. In GIS engineering, many GIS systems have been integrated to centrally manage spatial data in vector and raster structures. In digital plotting, the fusion of the two data structures is also widely used.
5.1 convergence of raster images and line-drawn vector Images
This is a simple superposition of two types of structured data, which is the lowest level of data fusion in GIS. For example, remote sensing raster images and line vector images are superimposed, and Remote Sensing raster images or aerial digital normal shooting images are used as the bottom layer of the composite images. Line-drawn vector graphs can be all superimposed, or partially superimposed as needed, such as water system edge lines, main traffic lines, administrative boundaries, and annotation elements. This fusion involves two problems: first, how to display the raster images and vector data simultaneously in the memory, and how to scale and roam at the same scale; and second, how to correct geometric positioning, combine the dot and line in the Raster image with the same name in the line vector. If the data of a line-drawn vector image is collected from the Raster image, the combination of the data is not a problem. If the line-drawn vector image data is digitalized by other sources, it is difficult to completely overlap a raster image with a vector line. This map has a certain mathematical foundation, rich spectral information and geometric information, administrative boundaries and other attribute information, and the visualization effect is very good. For example, the combination of the core elements DLG and Dom has gradually become a mainstream digital map.
5.2 fusion of remote sensing images and DEM
This is a common method used to produce the DOM of digital normal image maps. In digital photography and measurement systems such as jx4a and virtuozo, geometric correction and registration of remote sensing images are performed using existing DEM obtained by image-oriented modeling. Because DEM represents precise terrain information, it can be used to correct the precision of Remote Sensing and aerial images, which can eliminate the pixel displacement of remote sensing images due to terrain fluctuations, the positioning accuracy of remote sensing images is improved. Dem can also be used in the classification of remote sensing images. During the classification process, it is necessary to collect and analyze the ground reference information and related data. In order to improve the classification accuracy, we also need to use DEM to correct digital images with Radiation Correction and ry.
6. Prospects for data integration
In digital plotting, the fusion of raster images has been widely used in various sectors, especially in remote sensing image processing; the fusion of raster images and vector images is relatively simple at present, and it is easy to solve in various GIS software. Their development direction is mainly to enrich their integration methods from the application perspective and expand their application fields. The integration of vector data in various formats with complex structures and high requirements on software and hardware is currently a difficulty in GIS and a major research direction. The best solution is, of course, to design a "omnipotent" software that can combine multiple data structures and their data formats, so as to truly unify vector data in different formats. The current research is also moving towards this direction, mainly in the following two trends: professional 3 s station 3s8.cn
6.1 Data Interoperability Mode
The data interoperability model is a specification developed by OpenGIS Consortium (OGC. OGC provides unified standards for data interoperability, making it possible for a system to support different spatial data formats at the same time. According to the specifications promulgated by OGC, the software that provides data sources can be called data servers, and the software that uses data is called Data Client ), a Data customer uses a data request to provide services by the data server. The ultimate goal is to enable the data customer to read the space data provided by any data server. The OGC specification has gradually become an international standard and will be accepted and accepted by more and more GIS software and researchers. Its main feature is its independence from specific platforms. The data format does not need to be disclosed, which represents the development direction of data sharing technology. Data interoperability specifications bring about a new mode for multi-source data integration, but this mode has certain limitations in applications: first, to truly implement interoperability between data of various formats, the host software of each format is required to implement the data access interface according to the unified specification, which is not realistic for a certain period of time. Secondly, when a software accesses the data format of other software, it is implemented through the data server, which is actually the host software of the accessed data format. That is to say, the user must have the two GIS software at the same time, in addition, data interoperability can be completed only when data is run at the same time.
6.2 direct data access mode
Direct data access refers to the direct access to data formats of other software in a GIS software. You can use a single GIS software to access multiple data formats. Direct data access not only avoids frequent data conversion, but also allows users to access the data format of a software in a GIS software without requiring users to own the host software of the data format, and does not require the software to run. Direct Data Access provides a more economical and practical multi-source data integration mode. Currently, GIS software for multi-source data integration is mainly implemented using direct data access mode, that is, geomedia software released by Intergraph and Supermap developed by the Geographic Information Industry Development Center of the Chinese Emy of sciences. Geomedia and Supermap provide direct access to most GIS/CAD data formats, including MGE, ARC/INFO, and microstation dgn. China 3 s bar 3s8.cn
7 conclusion
GIS was a new technology developed in the 1960s S. due to its low development level, many technologies are not mature, such as high construction costs, poor practicability, and lagging behind in theoretical research. In particular, the high construction costs seriously affect the development prospects of GIS. Because the data objects processed by GIS are spatial objects, they have strong spatial and temporal characteristics, which are short in cycle, fast in change, and dynamic in nature, and the methods for obtaining data are also complex and diverse, this forms raw data in multiple formats. In addition, the GIS application system has been developing in an isolated state with specific projects for a long time. Many GIS software have their own data formats, this causes serious lag of GIS in the sharing and standardization of basic graphic data, which is a major bottleneck restricting the development of GIS. At the current development level, the integration of various spatial data is the most important way to reduce the construction cost of GIS. However, many technical problems still need to be solved and further research is needed.

Vector raster mutual conversion algorithm

 

 

Vector to Grid: Inner point diffusion method, that is, the inner point of the polygon extends to the neighboring point until it reaches the boundary.

Until the complex integral algorithm, that is, the complex integral is calculated based on the Closed Boundary of the polygon to be recognized to determine the relationship between the two points.

Generation; Ray algorithm and scanning algorithm, that is, the ray directed from a point outside the graph to the point to be determined, through the intersection of the ray and the boundary of the Polygon

Number to determine the internal and external relations. The boundary algebra algorithm is a vector to Grid Algorithm Based on the integral idea, suitable for recording

Returns the vector data of a polygon that records the topological relationship. The method starts from a point on the polygon boundary and searches the boundary clockwise.

Line, the grid with the same row coordinate on the left side of the upper line minus a value, and all the grid points on the left side of the lower line boundary plus

This value completes the polygon conversion after the boundary search is complete.

Grid to Vector: Is to extract the topology of the boundary and boundary of the polygon area represented by the grid set with the same number.

Link, and represents the process of forming a line of the vector format. The process includes polygon boundary extraction, that is, using Qualcomm filter.

Binarization raster images; Line tracing, that is, searching for each arc segment from one node to another; Topology

Generation and handling of redundant points and smooth curves.