Supervised classification of remote sensing imagery, the subject of the previous two chapters, involves the use of a training dataset consisting of labeled pixels representative of each land cover category of interest in an image. We saw how to use these data to generalize to a complete labeling, or thematic map, for an entire scene. The choice of training areas which adequately represent the spectral characteristics of each category is very important for supervised classification, as the quality of the training set has a profound effect on the validity of the result. Finding and verifying training areas can be laborious, since the analyst must select representative pixels for each of the classes by visual examination ...
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