Lab 4: Unsupervised Classification

Goals and Background

The purpose of this lab is to develop the analyst skills in extracting biophysical and sociocultural information from remotely sensed images.  The analyst will be employing an unsupervised classification algorithm to perform image classification.  Additionally, the lab will  help develop the analyst skills in recoding multiple spectral clusters from the unsupervised classification into a thematic map displaying land use/land cover classes.

Methods

All of the following methods were performed in Erdas Imagine 2015 unless otherwise stated.

Experimenting with unsupervised ISODATA classification algorithm 

In the first section of the lab I will be using the Iterative self-organizing data analysis technique (ISODATA) classification algorithm as the first step in reclassifying and image of Eau Claire and Chippewa counties in Wisconsin.  I will be reclassifying the image to the type of land use/land cover throughout the study area.

From the Raster menu I opened Unsupervised Classification from the Unsupervised sub-menu after open the subset image of Eau Claire and Chippewa county provided to me from my professor (Fig. 1).  From the Unsupervised Classification menu I set a number of the parameters with in the window. First I change the Method to Isodata which allowed me to alter the # of Classes 10 (from & to).  This allow the classification scheme to only produce 10 classes.  Then I changed the Maximum Iterations to 250.  Changing the Maximum Iterations allows the algorithm to run up to 250 times to properly group like features together.  I left all of the other parameters alone and proceeded to run the classification tool.

(Fig. 1) Unsupervised Classification window with parameters set.

Running the classification tool does not produce a output which can easily be understood (Fig. 2).  The analyst must recode the unsupervised clusters into meaningful land use/land cover classes. 

(Fig. 2) Display with orginal image (left) and unsupervised classification output image (right)

I will be classifying the land use/land cover to the following display:

• Water = Blue
• Forest = Green
• Agriculture = Pink
• Urban/built-up = Red
• Bare Soil = Sienna

I utilized Raster Attribute Table found under the Show Attributes tool under the Table tab to access the color table for the 10 classification clusters for the image (Fig. 3).  From this window I sync my view to Google Earth and started comparing the location on my image to the same location on Google Earth.  I made several comparisons throughout the image as I tried to decide which land use/land cover best described the cluster then selected and set the appropriate color to be displayed.  I utilized this same method to set the appropriate color to all 10 of the clusters.

(Fig. 3) Show Attribute tool window in Erdas Imagine.

Improving the accuracy of unsupervised classification

The accuracy of the previous unsupervised classification is limited due to the number of  classes set in the parameters.  The algorithm has group similar spectral signatures together though they would be classified in different classes.  Expanding the number of classes will allow a narrower amount of variability between the class clusters thus allowing different land use/land covers to be better represented in the display image.

To improve the accuracy I changed the number of classes to 20 and reduced the Convergence Threshold to .92.  The rest of the parameters were left the same and the unsupervised classification was ran.  I utilized the same method to recode the output image from the classification.

Recoding LULC classes to enhance map generation

The final step of my lab is to produce a map of the land use/land cover (LULC) for Chippewa and Eau Claire counties in Wisconsin from the unsupervised classification with 20 classes.  Before creating a map I need to recode the 20 classes to 5 classes.  One does not want to display 20 classes in a legend with a number of those 20 classes being duplicate classes (colors).

I utilized the Recode tool found under the Thematic tab to access the recode window (Fig. 4).  The class numbers were as follows:

1. Water
2. Forest
3. Agriculture
4. Urban/Built up
5. Bare Soil

The same color scheme was applied to the new recode utilizing the Raster Attribute Table as in previous steps.

Results

You can see the most noticeable difference between the forest and agricultural area when comparing the results from the unsupervised classification with 10 classes and 20 class.  I had a tough time separating (representing) the smaller forest and small agricultural areas with the unsupervised classification with 10 classes. While the 20 classes did help the representation there were still areas which overlapped and are not perfectly represented.

(Fig. 4) Unsupervised Classification recoded results with 10 classes (Left) and 20 classes (Right)

(Fig. 5) Map created to display the LULC for Chippewa and Eau Claire County in Wisconsin from the 20 class unsupervised classification.

Sources

Lta.cr.usgs.gov,. (2016). Landsat Thematic Mapper (TM) | The Long Term Archive.