Mod5 Unsupervised and Supervised Classification

This module introduces and explores both unsupervised and supervised classification methods. Unsupervised classification utilizes an algorithm to determine which pixels in the raster image are most like other pixels throughout the image and groups them based on a defined accuracy percentage. After the software has grouped the various pixels together it is up to the user to define what the grouped classes represent. For this type of classification the software is given certain user defined parameters such as number of iterations to run, confidence or threshold percentage to reach, and sample sizes. These essentially tell the software how long to run, what the minimum "correctly grouped" pixel percentage is, and how many pixels to look at adjusting at a time. 

On the other hand, supervised classification utilized user created training sites to tell the software what to look for spectrally to garner the user desired classifications. This is accomplished by creating a polygon area or a software grown similarity region. Examples would be forest, grassland, or water. Each area has a distinct spectral signature. These signatures are used to evaluate the whole of the image and allow the software to automatically reclassify all matching spectral signatures. The overall process is usually in 4 steps, get your image, establish spectral signatures, run the classification based on the signatures, then reclassify or identify rather what your class schema is.

Lets look at the two different areas that were used for the unsupervised and supervised processes respectively. 

This is not a refined map, but a screenshot from the software itself highlight an unsupervised classification output that has been recoded from 50 classes down to 5 specific classifications. This is the UWF campus which was originally a true color image whose pixel data has been reclassified. 

This is a complete map for the supervised classification portion of the module, centered on Germantown Maryland. It was created using a base image and supervised classification looking for the categories displayed in the legend. This map shows the acreage of areas as they currently exist and is intended to provide a baseline for change. As areas get developed the same techniques can be used on more and more current imagery to map the change and gauge which land uses are expanding / shrinking most and by how much.

Thank you.

v/r

Brandon