Computer assisted classification of multispectral imagery in remote sensing is useful for thematic mapping of land use, vegetation, soil, geology etc.
Classification methods are classfied into two categories.
Supervised classification: classification with the use of ground truth data in the form of sample sets. Maximum likelihood classifier is one of the typical supervised classification methods.
Unsupervised classification: classification with only spectral features without use of ground truth data. Clustering is an unsupervised classification in which a group of the spectral values will regrouped into a few clusters with spectral similarity.
The following classification methods are widely used depending on the spectral characteristics and availability of ground truth data as shown in Figure 5.10.
Rationing: classification between vegetation and non-vegetation is possible.
Box classifier: very easy to apply level slicing but accuracy is not very high.
Discriminant function: useful in the case when the number of classes is not many.
Clustering: unsupervised classifier with spectral similarity or distance between clusters.
Minimum distance method: several statistical distance measures such as Euclidan, Mahalanobis, Bhattacharya and Jefrey-Matsushita distance are used to determine the class.
Maximum likelihood classifier: see 5-8
Knowledge-based classification including decision tree classifier will be specified as classification model by users.