Unsupervised Classification

Unsupervised Classification of the UWF Campus

This week in Remote Sensing we used an aerial image of the UWF campus to try some unsupervised classification techniques.  Unsupervised classification is when we rely on the computer to group like pixels together and make assumptions that these represent similar features.  In the image above, we started by using ERDAS Imagine tools to create 50 unsupervised classes.  Then I manually assigned these 50 classes to 5 categories (Buildings/Roads, Trees, Grass, Shadows, and Mixed).  This process actually is pretty easy and goes quickly with the ERDAS tools.  This was one of the first assignments where I felt like the ERDAS tools were easier to use for this task than a similar task in ArcMap.  From the 5 new classifications, I was able to create an estimate of the permeable versus impermeable surfaces that make up the UWF campus.