Lab 5: Pixel-based Supervised Classification

Introduction:

This weeks lab was designed to properly educate the class on how to extract sociocultural and biophysical data from remotely sensed images through pixel-based supervised classification.  The lab is designed to instruct the class how to properly select training samples in order to create a supervised classifier, how to analyze the quality of the training samples which were collected, and how to produce a useful and meaningful land use/land cover map with this data.  This method will be compared and contrasted with the unsupervised classification run in Lab 4.


Methods:

The first step in performing supervised classification is to collect training samples (Figure 1).  These training samples will be of the different classes that are desired in the final land use/land cover map.  They should have typical spectral signatures of the desired features.  For example, water training samples should be of both standing and turbid water, and forest samples should include both dry and riparian vegetation.  These samples are simply selected by drawing a polygon in the desired area to be sampled and then uploading it to the signature editor tool.  These training fields can more accurately be delineated by performing field work or by using high resolution aerial photos.  For this lab, the class was just asked to link Google Earth to an image of the Eau Claire and Chippewa County area.  Twelve water training samples were collected along with eleven forest, nine agriculture, eleven urban area, and seven bare soil.  The various sample signatures were organized, classified (Figure 2), and plotted (Figure 3).

This shows a the first training sample collected for water.  As can be seen, a simple polygon is drawn in the desired area of the training sample.  Its spectral signature is then uploaded into the signature editor tool to be saved.  (Figure 1)

The various classes were all given similar colors
after they were organized and named.  (Figure 2)

After the training samples were classified and colorized they were
plotted here.  One of the objectives of this plot is to make sure there
is maximum separability between the classes.
(Figure 3)

Once training samples that may not have had enough separability were eliminated it was time to put the classes together and merge the signatures (Figure 4).

This is the signature mean plot that resulted from merging the training samples.  The five desired classes in the land use/land cover map can be seen on the right.  (Figure 4)

The training samples collected were then saved as a signature file.  From here the supervised classification tool (Figure 5) was opened and the signature file was uploaded.  The tool was then run and a land use/land cover classified image was created.  Then a map was generated from the image to show land use/land cover of the area (Figure 6).  This map that was generated doesn't seem very accurate as the urban/built-up area is much more spread out than it is in real life.  This error could be due to the lack of separability between bare soil, agricultural land, and urban/built-up areas.

This is the supervised classification tool.  Running it is as easy as inputting the image and the signature file that was saved from the training samples.  (Figure 5)

This is the land use/land cover map that was generated from the supervised classification.  Unfortunately it appears as if the urban/built-up area covers much of the areas that should be bare soil or urban land.  (Figure 6)

Conclusion:

The supervised classification in this case didn't create a very well done map.  The classes don't seem correct and just seem unnatural.  This is likely due to user error in gathering training samples and a lack of separability.  Compared to the map of Lab 4, this map seems to misrepresent many features, particularly the urban, agricultural, and bare soil classes.  In the future, higher quality reference imagery, instead of just Google Earth, should be used to collect better training samples.  Also, a higher separability should be the goal to try and avoid this.  This was a good lesson in teaching the errors that can occur in supervised classification and what should be done in the future to avoid them.