Difference Between Raster and Vector
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
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,
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.
Although vector data structure is the choice as the primary
form for handling graphical data in most GIS and CAD packages,
vector data acquisition is often more difficult than raster image
acquisition, because its abstract data structure, topology
between objects and attributes associated.
In the following, we explain the commonly used methods for
getting vector data, their advantages and drawbacks.
- Manual digitizing
Manual digitizing using a digitizing tablet has been widely
used. With this method, the operator manually traces all the
lines from his hardcopy map using a pointer device and create an
identical digital map on his computer. A line is digitized by
collecting a series of points along the line.
Although this method is straight forward, it requires
experienced operator and is very time consuming. For a complex
contour map, it can take a person 10 to 20 days to get the map
Another major drawback of this method is its low accuracy. The
accuracy of manual digitizing merely depends on how accurate the
hardcopy map is duplicated on a computer by hand. The spatial
accuracy level the human hand can resolve is about 40 DPI (dots
per inch) in the best case and will be lower while the operator
is tired and bored after working on it for a period of time. One
experiment was done at a university, a group of geography
students were asked to digitize the same map and the final
digitized maps were overlaid on top of each other to create a new
map. The result is not surprising, the new map is heavily
distorted as compared to the original map.
Manual digitizing is supported by most GIS packages with
direct link to a digitizing tablets through a computer I/O port.
- Heads-Up Digitizing and Interactive Tracing
Heads-up digitizing is similar to manual digitizing in the way
the lines have to be traced by hand, but it works directly on the
computer screen using the scanned raster image as backdrop. While
lines are still manually traced, the accuracy level is higher
than using digitizing tablet because the raster images are
scanned at high resolution, normally from 200 DPI to 1600 DPI.
With the help of the display tools, such as zoom in and out, the
operator can actually work with the resolution of the raster data
therefore digitize at a higher accuracy level. However, the
accuracy level is still not guaranteed because it is highly
dependent on the operator and how he digitizes. This method is
also time-consuming and takes about same amount of time as the
manual digitizing method.
The interactive tracing method automates individual line
tracing process by tracing one line at a time under the guidance
of the operator. This is a significant improvement over manual
heads-up digitizing in terms of digitizing accuracy and speed,
especially when fully automatic raster to vector conversion can
not be applied in cases such as low image quality and complex
layers. The main advantage of using interactive tracing is the
flexibility of tracing lines selectively and better operator
- Automatic Raster to Vector Conversion
Automatic digitizing or so called automated raster to vector
conversion, traces lines automatically from the scanned raster
image using image processing and pattern recognition techniques.
The idea behind automated raster to vector conversion algorithm
is to let the computer do the actual line tracing and eliminate
tedious manual tracing the human operator has to do.
Because of the importance to automate raster to vector
conversion process and the difficulties involved, it has been a
major research focus during the past two decades. Only in recent
years, automated raster to vector conversion software on PCs and
small computers become practical and commercially available for
data acquisition applications.
Challenges in Doing Raster to Vector
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 originals and scan at reasonable 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. The R2V software was developed by Able Software Corp
(www.ablesw.com) and available since 1993 with a focus on
vectorization of scanned maps and GIS data creation. R2V is
currently being used in more than 60 countries for map digitizing
and GIS data capture applications.
Choosing The Right Conversion Tool
There are quite a few commercial packages available for raster
to vector conversion. Below are few questions one should ask when
selecting the right tool for the task:
1. Does it support different image types, such as 1-bit
black/white, greyscale and 24-bit RGB color?
This is quite important for people whose source images are in
color. Treating color images as black and white or greyscale
apparently loses all color information and a significant amount
of editing may be needed to separate colors by hand.
By starting directly with a color image, one can use color
classification or separation functions to separate colors into
layers and vectorize layers.