Wil Blouin's Advanced Remote Sensing Portfolio

Goals and Background: The goal of this lab was for students to practice important and necessary evaluation of classification results. Skills practiced in this lab included collecting ground reference samples to perform accuracy assessment with, using those samples to perform accuracy assessment, and then interpreting the results. The processes practiced were performed on two images, the result of the unsupervised classification performed in Lab 3, and the result of the supervised classification performed in Lab 4. An error matrix, accuracy totals, and Kappa statistics were generated. Methods: Part 1: Creating testing samples Figure 1: Setting up ground reference samples Part 1 involved bringing in the recoded unsupervised classified image from Lab 3 and collecting ground reference sample data for comparison with the classification of those points. Under the raster tab, and then the supervised menu, the Accuracy Assessment tool was opened, the image was opened in the

Goals and Background: This lab had students extract informational classes from Landsat 7 (ETM+) images. The skills practiced included collection of training samples for the classifier to use in classification and evaluation of the quality of the training samples collected. In collecting samples for each of the classes the user must pick enough spectral samples that appropriately show the variance within the class, but must make sure that the samples do not overlap into areas that would be otherwise classified to ensure the class has a unique mean spectral signature. A minimum of 5 to 10 samples must be created for each class and each sample should show a very similar spectral profile. Evaluation of samples in this lab included the use of histograms, an image alarm tool, and a spectral separability report. After these steps were completed, classification was executed using tools in ERDAS Imagine, resulting in a raster classification image that could be made into a neat figure in Arc