Lab 4: Unsupervised Classification

Background and Goal

The goal of this lab is to learn how to identify and classify different physical and manmade information from a remotely sensed image by using an unsupervised classification algorithm. Specifically, this lab helped create an understanding for the input and execution requirements for unsupervised classification. In addition to this, we learned how to recode the different spectral clusters into a useful land use/land cover classification scheme.

Methods

To start, we ran a very basic unsupervised classification algorithm using ISODATA. In ERDAS, we used all the default settings in the Unsupervised Classification tool, while bringing the minimum and maximum number of classes down to 10 and increasing the iterations to 250 (to ensure that the threshold is met before iterations run out). This algorithm will organize the pixels into 10 different classes, according to their spectral signatures. After the model was complete, Google Earth was used to classify the created image into five classes: Water, Forest, Agriculture, Urban/Built-up, and Bare Soil. By highlighting each of the 10 created classes and comparing the highlighted areas with the Google Earth viewer, each of the 10 classes were reclassified into the five classes mentioned above. This part of the lab was used to try out unsupervised classification.

In order to make the previous model more accurate, we went back and did a second unsupervised classification. This time 20 classes were created, with a Convergence Threshold of 0.92 instead of 0.95. These 20 classes were then reclassified into the five previously mentioned classes in the same manner described above. Once everything was reclassified, the Recode tool was used to compile all 20 of the classes into the five needed for a Land Use/Land Cover map (Figure 1 displays this change). Once this was complete, a map of the Land Use/Land Cover was created by using ArcMap.

Figure 1: The image's table before and after the classes were re coded. In the before picture there are 20 classes and 5 colors. and the after picture there are only 5 classes (with their respective colors).

Results

Figure 2 displays the results from the first unsupervised classification. Because there were only 10 classes, the image is over generalized. This creates overlap between some of the features in the image. This is easily seen in the northwestern region of the image, where there are several Urban/Built-up regions. Most of these areas are actually agriculture, but they were falsely classified. Also, Agriculture seems to swallow the Bare Soil and Forest classes.

Figure 2: The Land Use/Land Cover image after reclassifying an unsupervised classification of 10 classes.

Figure 3 displays the results of the second unsupervised classification. Because there were 20 classes, the image is more accurate, with less generalized classification. You can see more Bare Soil and Forest classified in this map, though the areas to the northwest are still classified as Urban/Built-up. The spectral signatures are too similar for the classification to pick up the difference.

Figure 3: The Land Use/Land Cover map after reclassifying an unsupervised classification of 20 classes.