Unsupervised Classification

This week I learned about the two ways to classify pixels on an image: Supervised and Unsupervised. For a Supervised classification, I would identify "training pixels" to use to identify the rest of the features on the image I was working with. For Unsupervised classification, the pixels are simply divided into a number of classes depending on the number you specify. The tools to accomplish this in ArcMap are: Iso Cluster Tool and the Maximum Likelihood Classification tool

We also used ERDAS Imagine to classify pixels. In ERDAS, the classification process was more tedious but generally worked the same. In the Raster tab, under the Classification group, click the Unsupervised button, then select Unsupervised Classification.

I performed an Unsupervised Classification using an aerial of the UWF Campus. I made five classes, named them and designated colors for each class:

1. Grass – green  

2. Trees – dark green 

3. Buildings/Road – grey 

4. Shadows – black 

5. Mixed - orange

The process was long but I am happy with the results. Additionally, I created another column or field for "Area" and then I totaled the area as "Permeable" or "Impermeable" and listed the percentages of each on my final map.