Lab 8 - Advanced Classifiers 2

Goal

The goal of this lab exercise was to expose students to the use of two different advanced classification algorithms.  These advanced classifiers provide a much higher level of accuracy for classifying images over traditional classification methods.  The first advance classification conducted in this lab was an expert system/decision tree classification with the use of ancillary data.  The second classification scheme was developing and using a neural network to perform a complex image classification.

Methods

Part 1: Expert System Classification

For part 1 of this lab, we used the Knowledge Engineer tool in Erdas Imagine and created various rules and variables to classify the provided image of the Eau Claire and Chippewa Falls region.  In the Knowledge Engineer window, we created arguments and counter-arguments for each of the desired LULC classes.  The final classification scheme for this specific classification can be seen in figure 1 below, showing all of the various rules and arguments for the classification.

Figure 1. Arguments for expert system classification

Part 2: Neural Network Classification

Part 2 of this lab exercise was devoted to performing a Neural Network classification using the ENVI software.  After inputting the desired image into ENVI, we used the Restore ROI tool to add in provided ROI's that would be used to train the classifier.  Next, the Neural Network Supervised classification was run with 1000 iterations and a Logistic Activation method.  Once all the parameters were set, the classification was run and the resulting image was the one below in Figure 2.

Figure 2.

Once this was done, the next part of the Neural Network classification portion of this lab was to collect our own training samples and run a neural network classification of the University of Northern Iowa campus.  To do this, we imported the image into ENVI just as the previous image was imported and instead of restoring ROI's, we created our own of classes we desired to create, such as buildings, roads, and vegetation.  Many of the same parameters from the first Neural Network were used and the classification was run.  Once it was run however, the classification was run again with the number of iterations set to the value of where there was much variability in the Neural Net RMS Plot window.  Once the classification was run again with a different number of iterations, the classified image appeared to be more accurate for certain classes.

Results

Final output map from part 1 of lab

First NN classification of University of Northern Iowa campus

Second NN classification of University of Northern Iowa campus with altered number of iterations.

Sources

Landsat imagery provided by Earth Resources Observation and Science Center, United States Geological Survey

Quickbird High resolution image of portion of University of Northern Iowa campus provided by Department of Geography, University of Northern Iowa.