ERDAS Supervised Classification

Lab Objectives:

By the end of this lab, students should be able to:

• Create spectral signatures and AOI features 
• Produce classified images from satellite data 
• Recognize and eliminate spectral confusion between spectral signatures

This lab involved collecting/creating spectral signatures and classifying them. This is done under supervision (by the creator/user) instead of the computer program. Creating spectral signatures can be done by manually drawing polygons to classify the area of interest (AOI) after an AOI layer is established, and also by growing "seeds" (using spectral euclidean distance and neighborhood). The user can evaluate signatures and appropriate bands by using histogram plots and signature mean plots. These can be used to mitigate spectral confusion between classes.

The image of interest is classified by having the signature file undergo the supervised classification process. Optionally, the user can also create at the same time a distance file image, which shows possible error in the classified image. After this, the user can merge certain classes of the new supervised image if the user wishes. After the classes are merged (or recoded), class names can be established in the recoded image and area of each class can be calculated. The supervised classification process was done during this lab to create land classification of Germantown, Maryland.

The map above shows land classification in the area, such as agriculture, urban, forest, grass, etc.