Difference Between Raster and Vector Images

Raster and vector are the two basic data structures for storing and manipulating images and graphics data on a computer. All of the major GIS (Geographic Information Systems) and CAD (Computer Aided Design) software packages available today are primarily based on one of the two structures, either raster based or vector based, while they have some extended functions to support other data structures.

Raster image comes in the form of individual pixels, and each spatial location or resolution element has a pixel associated where the pixel value indicates the attribute, such as color, elevation, or an ID number.

Raster image is normally acquired by optical scanner, digital CCD camera and other raster imaging devices. Its spatial resolution is determined by the resolution of the acquisition device and the quality of the original data source. Because a raster image has to have pixels for all spatial locations, it is strictly limited by how big a spatial area it can represent. When increasing the spatial resolution by 2 times, the total size of a two-dimensional raster image will increase by 4 times because the number of pixels is doubled in both X and Y dimensions. Same is true when a larger area is to be covered when using same spatial resolution.

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Vector data comes in the form of points and lines, that are geometrically and mathematically associated. Points are stored using the coordinates, for example, a two-dimensional point is stored as (x, y). Lines are stored as a series of point pairs, where each pair represents a straight line segment, for example, (x1, y1) and (x2, y2) indicating a line from (x1, y1) to (x2, y2).

In general, vector data structure produces smaller file size than raster image because a raster image needs space for all pixels while only point coordinates are stored in vector representation. This is even more true in the case when the graphics or images have large homogenous regions and the boundaries and shapes are the primary interest. Besides the size issue, vector data is easier than raster data to handle on a computer because it has fewer data items and it is more flexible to be adjusted for different scale, for example, a projection system in mapping application. This makes vector data structure the apparent choice for most mapping, GIS (Geographic Information System) and CAD (Computer Aided Design) software packages.

Also, topology among graphical objects or items are much easier to be represented using vector form, since a commonly shared edge can be easily defined according to its left and right side polygons. On the other hand, this is almost impossible or very difficult to do with pixels.

Challenges in Doing Raster to Vector Conversion

While vector data structure provides a simpler and more abstract data representation than raster image, it is not easy to do an automatic conversion from raster to vector, or so called vectorization process, although the opposite direction (from vector to raster) is quite trivial. There have been extensive research efforts focused on the issued involved in raster to vector conversion during the past decades.

A complete raster to vector conversion process includes image acquisition, pre-processing, line tracing, text extraction (OCR), shape recognition, topology creation and attribute assignment.

The image acquisition process generates the initial raster image at a certain spatial resolution. The quality and resolution of the raster image are key factors for the quality and accuracy of the vectorized data. It is always recommended to start with clean and sharp resolution. The scanning resolution should match the resolution at which the original image source was created. If scanning resolution is set too high than the original image source, it not only uses unnecessary amount of system resource to process, but also noise and artifact are scanned.

For most good quality black and white maps and engineering drawing, such as color map separates, can be scanned as 1-bit monochrome. For maps with dirty and smearing background, they can be scanned as 8-bit greyscale and enhanced using imaging software to remove background and noise.

Although color scanners have come a long way, large format and high resolution scanning is still quite expensive. Color classification and color separation are very sensitive to the color quality of the scanned image. Other color images, such as satellite and aerial photos, have been used directly to create vector data, such as region boundaries, street and road lines. Because of more bits (normally 24-bit) are used, color image files are normally bigger and requires more system resource to store and process.

Recent developments in automated raster to vector conversion technology have made it possible to take a hardcopy image, scan it and convert it into vector format in a matter of minutes or even seconds. With manual method using a digitizing tablet, this process can take days or weeks to complete because all lines have to be traced by hand.

Several raster to vector conversion software packages are commercially available for various types of applications, such as engineering drawing conversion, map digitizing and GIS data capture.