Unsupervised classification is a form of pixel based classification and is essentially computer automated classification. The user specifies the number of classes and the spectral classes are created solely based on the numerical information in the data (the pixel values for each of the bands or indices). Clustering algorithms are used to determine the natural, statistical grouping of the data. The pixels are grouped together into based on their spectral similarity. The computer uses feature space to analyze and group the data into classes. Roll over the below image to see how the computer might use feature space to group the data into ten classes.
While the process is basically automated, the user has control over certain inputs. This includes the Number of Classes, the Maximum Iterations, (which is how many times the classification algorithm runs) and the Change Threshold %, which specifies when to end the classification procedure. After the data has been classified the user has to interpret, label and color code the classes accordingly.
Advantages and Disadvantages
Unsupervised classification is fairly quick and easy to run. There is no extensive prior knowledge of area required, but you must be able identify and label classes after the classification. The classes are created purely based on spectral information, therefore they are not as subjective as manual visual interpretation.
One of the disadvantages is that the spectral classes do not always correspond to informational classes. The user also has to spend time interpreting and label the classes following the classification. Spectral properties of classes can also change over time, so you can’t always use same class information when moving from one image to another.