lab 4: Pixel-Based Supervised Classification

Introduction
The objective of this lab was expand upon the image classification of biophysical and sociocultureal information from remotely sensed images through pixel-based supervised classifications. Building of the previous lab, this will specifically focus on the selection of training samples for a supervised classifier, evaluate the quality of training signatures collect and produce a meaningful informational land use/land cover classes through supervised classification.

Methodsdf
To begin the lab we were asked to collect training samples for a supervised classification using a Landsat 7 (ETM+) image that covered both Eau Claire and Chippewa Counties. To do this, we collected samples of 5 different landuses. Google Earth was used as a reference image to help determine land cover types. This land uses are water, forest, agriculture and bare soil. Each of the different land uses had a minimum number of samples required (figure 1).

Water was the first land cover type that signatures were collected for. Signatures were taken from lakes, ponds, and rivers to account for the various spectral characteristics that different water bodies produce. Once the spectral signitures were collected, the signitures were displayed a chart to make sure that they followed the expected profiles of water. In the case of this image, there were abnormally high values in band 1 which is more likely the result of atmospheric scattering. 

Example of a sample being taken on the Chippewa River

List of samples taken for the water class

Spectral profile for the water samples. 

This procedure was then repeated for the forest, agricultural, urban and bare soil classes.

Spectral Profile for the Forest class

Spectral Profile for Agriculture class

Spectral Profile for the Urban class

Spectral Profile for the Bare Soil class

All the spectral profiles were placed into a single plot. A convergence analysis the Evaluate-Separability function was used to see which four bands had the most separability amongst the different classes. The results showed that the greatest seperability was in bands 1,2,4,6. The average seperability score was 1976, which was quite good. All the training samples spectral signitures were then combined into their respective classes 

All four classes spectral displayed into a single blot

A separability analysis displaying the four bands that showed the greatest amount of separability (1,2,4,6) with a score of 1978. 

All individual spectral signatures combined into their respective classes and plotted together. 

Once the signatures were combined, the table was then saved and used to complete the pixel-based supervised classification. The new classified image was then imported into ArcMap where a final map was created.

Map created in ArcMap displaying newly created supervised classified image

Sources
The images for this lab were provided by Dr. Cyril Wilson