Lab 3: Unsupervised Classification

Introduction

  Unsupervised classification is a fairly quick way to extract surface uses from a study area. Unsupervised classification requires minimal input from the user and doesn't require any knowledge of the study area prior to processing data. This lab details how unsupervised classification is performed within Erdas Imagine 2016. Two different sets of parameters will be tested and then both outputs will be assessed.  For this analysis, the imagery used will be a Landsat 7 ETM+ image captured on June 9th, 2000 of Eau Claire and Chippewa counties in Wisconsin. After performing unsupervised classification, a map of the LULC classes will be created in ArcMap.

Methods

Experimenting With Unsupervised Classification

Step 1: Run the Unsupervised Classification Algorithm
 This is done by opening the unsupervised classification window by navigating to Raster → Unsupervised → Unsupervised Classification. In this window, the Isodata radio button is checked, and the following parameters were set as shown in Figure 1. Then, the tool was ran.

Fig 1: Unsupervised Classification Window and Parameters

Step 2: Assign LULC Classess to the Generated Classes
  Step 1 generated an output image with 10 different classes. This can be seen below in Figure 2 along with the attribute table. These classes / attributes were then classified into 5 LULC classes by changing the colors of the classes and by using 2005 imagery from Google Earth Pro as a reference. Often, the class fell into multiple actual LULCs. However, the analyst did the best to make sure that the class represented the LUL . The following LULC classes were classified and assigned the following color:

• Water (Blue)
• Forest (Dark Green)
• Agriculture (Pink)
• Urban / Built up (Red)
• Bare Soil (Sienna) 

  After reclassifying the image, the image was saved as a new raster.

Fig 2: Output from Unsupervised Classification Tool

Improving the Unsupervised Classification

Step 1: Run the Unsupervised Classification Algorithm
  To improve the unsupervised classification, some of the parameters were changed in the Unsupervised Classification window as shown below in Figure 3. The convergence threshold was changed from .95 to .925, and the number of classes generated was changed from 10 to 20.

Fig 3: Making Changes to the Unsupervised Classification Parameters

Step 2: Assign LULC Classess to the Generated Classes
  Step 1 generated an output image with 20 different classes. These classes were then assigned to one of the five LULC classes just like in Step 2 of the first run through. Once again, Google Earth was used as a reference to classify the LULC classes.

Step 3: Reclassifying the Raster
  The next step was to reclassify the raster. As of now, each of the 20 classes is assigned a color as to whether it is urban / built up, agriculture, bare earth, forest, or water. These LULC classes can be aggregated into just 5 classes instead making it easier to create a map in ArcMap. This is done by navigating to Raster → Thematic → Recode and then by assigning the appropriate LULC name to each of the 20 classified values. Figure 4 shows what the attribute table looks like after assigning the LULC its name.

Fig 4: Using the Recode Tool to Reclassify the Raster

  Then, a new field was created in the raster table to assign a label to each of these values. A new field was added to the table by navigating to Edit → Add Class Names and then by changing the labels in these cells to reflect its corresponding LULC. Then name of this field was changed to "Class name".  This can be seen below in Figure 5.

Fig 5: Assigning the Values its Appropriate LULC Label

Lastly, a map of the improved LULC raster was created in ArcMap.

Results

  Figure 6 is a screenshot of the first attempt at creating a LULC image. The accuracy of these classifications is not very good. Many areas that are urban / built up are classified as agricultural and many areas that are agricultural are classified as urban / built up. Another common error is that many agricultural areas got classified as bare earth. Overall though, this image does a good job of generalizing the LULC for Eau Claire and Chippewa counties.

Fig 6: First Attempt at a LULC Classification

  Figure 7 is a map of the output of the improved LULC for Eau Claire and Chippewa counties.  There is less of a salt and pepper effect in this image, especially when analyzing the bare earth land cover. In comparing the two LULC outputs, the improved one is better because it is smoother and less areas are misclassified. This is because there were 20 classes generated from the Unsupervised Classification tool rather than 10, and because the conversion threshold was lowered. 

Fig 7: Map of LULC of Eau Claire and Chippewa Counties in the Year 2000

Sources

United States Geological Survey. (2017). Earth Resources Observation and Science Center
Wilson, C (2017) Lab 3 Unsupervised Classification Data retrieved from
     https://drive.google.com/open?id=1tbBaoMEQybAzqeuxnrfgL6Xpvcqm25em