Processing hyperspectral images allows you to decode images and recognize objects in the scene on the base of analysis of spectrums. In some problems, information about the spectra may not be sufficient. In this case, visualization of data sets may use, for object recognition, by use additional non-formalized external attributes (for example, indicating the relative position of objects). Target visualization is a visualization adapted to a specific task of application. The method discussed in this chapter uses a way to visualize a measure of similarity to the sample. As a result of the transformation, the hyperspectral (multichannel) image is converted into a single-channel synthesized image in grayscale, on which the objects of interest for the problem under consideration are selected. By changing the brightness and contrast of the synthesized image, it is possible to interactively adjust the results of automatic processing.
Part of the book: Hyperspectral Imaging in Agriculture, Food and Environment