Lab 10 - Supervised Image Classification

For this lab we used a supervised image classification method to create a thematic land use/land cover (LULC) map of Germantown, Maryland.

This method uses what are called 'training areas' to guide the computer in assigning a LULC value per pixel.  These training areas are selected prior to running the automated classification method, which does imply that the thematic map creator knows quite a bit about what to expect land cover-wise prior to beginning the process.

The LULC classes as created with supervised classification.

The map above was created with several training classes provided for most categories - the idea here is that more than one example is better for the program when it assigns classes to the various pixels.  The pixel assignments are neighborhood based and used a maximum likelihood assignment method.  This means that pixel assignments are based on those having the highest probability of matching the spectral values as provided in the training class(es). 

As can be seen in the map above, there are quite a few acres devoted to roads... and that is not technically correct.  Quite a bit of those roads seem to represent urban areas, or possibly even grasses.  Some tweaks are needed for the roads training classes (I had used two).  Unfortunately the spectral signatures for roads is very similar to that of the urban areas, so there will always be some error on the map no matter how much those training classes get altered.

A spectral euclidean distance map is also shown above as an inset.  As I understand it, this map represents the amount of error on my thematic map - and is displayed as bright pixels.  Since my inset map is quite bright, that means there happens to be quite a bit of error on my map... and most of those errors seem to follow along my roads class.  It seems that this process requires a lot of trial and error before a final product can be presented.

*Originally published on November 10, 2015.  Updated on 2/27/2017 to repair image links.