GIS 4035 Module 9: Unsupervised Classification

Five category unsupervised classification of the UWF campus based on true color imagery.

The ninth module of the course guided us through performing unsupervised classifications of imagery in ArcMap and ERDAS Imagine. An unsupervised classification takes basic guidelines from the user (such as the number of desired categories) and creates categories based on the appearance of each pixel. A perfect classification would, for example, classify all water in a category, all trees in another category, and so on. There has likely never been a perfect classification, however, so the resulting classified image must be edited to better capture the desired categories.

In order to create the above classified image of the UWF campus, the original true color image was run through Imagine's Unsupervised Classification tool to create fifty categories. The resulting image (not shown) looked very similar to the original image. We then reclassified each of the fifty categories into the five classes of trees, grass, buildings/roads, shadows, and mixed (grass/urban). The main source of error was the overlap of bright grass and ground areas to some urban areas; this created the need for the "mixed" class.