GIS 4035 Module 10: Supervised Classification

Supervised classification and spectral distance file of Germantown, Maryland.

The task for module 10 was to utilize ERDAS Imagine to create, analyze, and edit supervised classifications of multispectral images. We began by reviewing the fundamentals of the Signature Editor tool (to create and edit the training samples used for a supervised classification) and creating an AOI (area of interest) layer. We then used the Inquire tool to locate specific points on the image on which we created polygons to capture to areas to be used as the spectral signature of a class. We also reviewed the use of the Seed tool; this tool automatically creates a polygon capturing pixels of a set spectral distance from the initial point based on input parameters. I preferred using the Seed tool to manually inserting polygons as it gives more control over keeping each class distinct. We also spent some time analyzing the histograms and mean plots of spectral signatures. These tools give use a more precise way to ensure our classifications capture unique features and are not spectrally confused.

The assignment required us to use all the tools reviewed in the module to create our own supervised classification of an image of Germantown, Maryland. We were given coordinates of three urban features, two fallow field features, four agriculture features, and one each for grasses, deciduous forests, and mixed forests. We also were to create signatures for water and roads. With this completed, we then needed to recode the number of classes down to eight, consolidating the categories with multiple classes into one each. We also needed to include the spectral distance file in our final map; this image shows us at a glance the pixels in the image that are furthest away from any of our classes. The final layout was completed in ArcMap.