2.7. MULTI-SPECTRAL
IMAGES
A multi-spectral image is a collection of several monochrome
images of the same scene, each of them taken with a different sensor. Each
image is referred to as a band. A well known multi-spectral (or
multi-band image) is a RGB colour image, consisting of a red, a green and a
blue image, each of them taken with a sensor sensitive to a different
wavelength. In image processing, multi-spectral images are most commonly used
for Remote Sensing applications. Satellites usually take several images from
frequency bands in the visual and non-visual range. Landsat 5, for
example, produces 7 band images with the wavelength of the bands being between
450 and 1250 nm.
All the standard single-band image processing operators can
also be applied to multi-spectral images by processing each band separately.
For example, a multi-spectral image can be edge detected by finding the edges
in each band and than ORing the three edge images together. However, we would
obtain more reliable edges, if we associate a pixel with an edge based on its
properties in all three bands and not only in one.
To fully exploit the additional information which is contained
in the multiple bands, we should consider the images as one multi-spectral
image rather than as a set of monochrome greylevel images. For an image with
k bands, we then can describe the brightness of each pixel as a point
in a k-dimensional space represented by a vector of length
k.
Special techniques exist to process multi-spectral images. For
example, to classify a pixel as belonging to one particular region, its
intensities in the different bands are said to form a feature vector
describing its location in the k-dimensional feature space. The
simplest way to define a class is to choose a upper and lower
threshold for each band, thus producing a k-dimensional `hyper-cube'
in the feature space. Only if the feature vector of a pixel points to a
location within this cube, is the pixel classified as belonging to this class.
A more sophisticated classification method is described in the corresponding
worksheet.
The disadvantage of multi-spectral images is that, since we
have to process additional data, the required computation time and memory
increase significantly. However, since the speed of the hardware will increase
and the costs for memory decrease in the future, it can be expected that
multi-spectral images will become more important in many fields of computer
vision.
2.8. LOOK UP TABLES AND
COLOURMAPS
Look Up Tables or LUTs are fundamental to many
aspects of image processing. An LUT is simply a table of cross-references
linking index numbers to output values. The most common use
is to determine the colours and intensity values with which a particular image
will be displayed, and in this context the LUT is often called simply a
colourmap.
The idea behind the colourmap is that instead of storing a
definite colour for each pixel in an image, for instance in 24-bit RGB format,
each pixel's value is instead treated as an index number into the colourmap.
When the image is to be displayed or otherwise processed, the colourmap is used
to look up the actual colours corresponding to each index number. Typically,
the output values stored in the LUT would be RGB colour values.
There are two main advantages to doing things this way.
Firstly, the index number can be made to use fewer bits than the output value
in order to save storage space. For instance an 8-bit index number can be used
to look up a 24-bit RGB colour value in the LUT. Since only the 8-bit index
number needs to be stored for each pixel, such 8-bit colour images take up less
space than a full 24-bit image of the same size. Of course the image can only
contain 256 different colours (the number of entries in an 8-bit LUT), but this
is sufficient for many applications and usually the observable image
degradation is small.
Secondly the use of a colour table allows the user to
experiment easily with different colour labeling schemes for an image.
One disadvantage of using a colourmap is that it introduces
additional complexity into an image format. It is usually necessary for each
image to carry around its own colourmap, and this LUT must be continually
consulted whenever the image is displayed or processed.
Another problem is that in order to convert from a full colour
image to (say) an 8-bit colour image using a colour image, it is usually
necessary to throw away many of the original colours, a process known as colour
quantization. This process is lossy, and hence the image quality is degraded
during the quantization process.
Additionally, when performing further image processing on such
images, it is frequently necessary to generate a new colourmap for the new
images, which involves further colour quantization, and hence further image
degradation.
As well as their use in colourmaps, LUTs are often used to remap
the pixel values within an image. This is the basis of many common image
processing point operations such as thresholding, gamma correction and contrast
stretching. The process is often referred to as anamorphosis.
| 
