Lab 5: Pixel-Based Supervised Classification

Background and Goal

In this lab, we expand on the classification process. Instead of doing an unsupervised classification like we did last week, we did a pixel-based supervised classification. This process uses training samples from a training image to assist (or supervise) the classification process.

Methods

The first step in a supervised classification is to collect training samples for the desired classes. For this exercise, we collected a minimum of 12 samples for Water, 11 samples for Forest, 9 samples for Agriculture, 11 samples for Urban/Built-Up, and 7 samples for Bare Soil. The study area was again Eau Claire and Chippewa Counties. To collect training samples, we used Google Earth to confirm the land classification in our ERDAS viewer. We then used the drawing feature to create an area of interest within this class, and imported the signature into ERDAS’ Signature Editor tool. This was done for each class, until a total of 50 signatures were collected (keeping in mind the minimums for each class). Figure 1 shows the complete table of signatures that were collected.

Figure 1: The complete table of training samples.

It was then necessary to evaluate the quality of our training samples. First, they are visually examined, by comparing the different spectral signatures of each sample in each individual class. If any samples do not follow the spectral signature of the class that it is suppose to be in, it was discarded and a new sample was collected. Once all of the spectral signatures look as they should, a signature separability test was performed to examine the statistical quality of the samples. This function calculates the four bands with the best average separability of features, and gives a separability score. This score must be above 1900 to be effective. Once it is confirmed that the training samples are of good quality, all the signatures that were collected must be merged to create one signature for each class. These signatures are then used to perform the supervised classification. To do this, we simply used the Supervised Classification tool in ERDAS, using the signatures collected as the training samples. We used a Maximum Likelihood classification to yield the best results. Figure 2 shows the separability score results, and Figure 3 shows the merged signatures.

Figure 2: My results of the Separability test. The best average score was 1974, and the four bands with best average separability were bands 1, 2, 3, and 4.

Figure 3: The merged spectral signatures to be used in the supervised classification.

Results

Overall, this classification technique yielded poor results compared to the unsupervised classification performed last week. The Water class was not represented to its full degree, and the Urban/Built-Up class was even more expansive than in the unsupervised classification. If we had more time to collect more signature samples, this technique would probably show better results. Figure 4 shows the unsupervised result compared to the supervised results, and Figure 5 is a detailed map of the supervised results.

Figure 4: The unsupervised results are on the left, and the supervised results are on the right. In the unsupervised image, Forest, Water and Urban/Built-Up are much better classified.

Figure 5: A complete map of the final result of supervised classification.