Module 9 - Remote Sensing - Unsupervised Classification

Result of Unsupervised Classification
of an Air Photo of the UWF Campus

If we want to convert raw pixel data (eg. reflectance, etc.) into thematic material, we'll need to decide what spectral characteristics equate with what sorts of features.  Both ArcMap and ERDAS Imagine have automated tools for this conversion.  There are two ways of having the software do this.  In unsupervised classification, the software clusters groups of pixels with similar spectral signatures and assigns them to thematic classes, without regard to types of features the classes represent.  The user specifies how many thematic classes will be used then refines and corrects the final image and assigns the classes to the groups of similar pixels.   We did this type of exercise this week in Lab.  We started with a color air photo of the UWF campus with only the visible red, green and blue bands.  Then, we instructed ERDAS imagine to classify all the pixels in the image, based on color, into 50 classes.  We then examined the classes and assigned them to one of five thematic classes:  buildings or streets, trees, grass, shadow, and mixed.  The software gave us a considerable head start in this task, by grouping the pixels (and features) with similar colors.

Next week, we'll do supervised classification, in which we'll "train" the software with some samples that we've identified ahead of time.