Lab 9 - Unsupervised Classification

This week's lab focused on using an automated method to classify aerial imagery: the unsupervised classification.  This method isn't exactly hands free - it's just called unsupervised because the program that completes the process does so without a training data set.  If it had training data to guide it, then the process would be considered supervised.

With unsupervised classification the computer program iterates through the image using whatever algorithms and input parameters were assigned at the start of the process.  When the program creates the classes it does so by grouping similar brightness values together.  Once the process is complete it is necessary to review the results and then manually classify (or re-classify) as needed.

Map depicts an image that had been re-classed into 5 land use/land cover categories using unsupervised classification.

The above image represents an unsupervised classification that was run using the ERDAS Imagine program.  An ISODATA classification was used; specified input parameters included the choice of 50 classes to be created, setting the maximum number of iterations to 25, and setting the convergence threshold to 0.950.  All other options were left at their defaults. 

After the image was re-classed with 50 (!) classes, I then manually pared this down to 5 based on very general land use/land cover types (grass, trees, urban areas, mixed, and shadows).  The shadow category was something of a surprise, but given the time of day the image was taken there were quite a few shadows!  The mixed category represents those pixels that actually could be assigned to more than one land use/land cover category.

*Originally published on November 4, 2015.  Updated on 2/27/2017 to reset image links.