Module 9: Unsupervised Classification

Unsupervised Classification

The goal of the lab was to perform an unsupervised classification using ArcMap and ERDAS.  Another aspect of the lab was to classify images with different spatial and spectral resolution.  The final goal was to learn how to reclassify and recode images in ERDAS.

The lab started off with using the Iso Cluster tool and Maximum Likelihood Classification tool.  Doing this, it created a classified image which I assigned classes colors.

In ERDAS, I used the Unsupervised Classification tool.  This allowed for me to give the image 50 classes.  Once the image had the classes, I reclassified by opening the attribute tables to change the colors of the pixels that belong to each feature.  I did this throughout using the Swipe tool to help identify from the true image.  It was also helpful to change the pixel group to a red or yellow to see how much it is used in the image.  From there, I continued to classify the pixels.

Once the pixels were classified, I used the Merge tool to group the 5 classifications into 5 classes.  I looked at the classes and assigned them groups to bring the classes from 50 to 5. I added the area of the features to help determine how much is impermeable and permeable classes.