Satellites usually take several images from frequency bands in the visual and non-visual range. 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 graylevel images. For an image with k bands, we can then 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. 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 will decrease in the future, it can be expected that multi-spectral images will become more important in many fields of computer vision.