Lab 4: Pixel-Based Supervised Classification

Goal and Background:

The extraction of information from remotely sensed images is a very important aspect of the remote sensing discipline.  One method of extracting information is land cover classification.  In the previous lab unsupervised classification was used.  In this lab, supervised classification will be used.  Supervised classification involves more user input and oversight than unsupervised, as the name suggests.

Methods:

The first step in this method is to collect samples from the image to "train" the classifier.  The following image (figure 1) shows the amount of samples that should be collected from each class.

Now the user must collect spectral signatures.  This is done by using the signature editor and polygon tool in ERDAS.  The following image is what the polygon and its corresponding signature looks like in the signature editor.

In total, 50 signatures were collected from around the image.

To ensure the signatures are accurate each of the signatures should be examined in profile view.

Looking at the water signatures, they match those signatures of non-turbid water.  This is done for each group of spectral signatures.

The classes are then analyzed to make sure there are no bad samples.  This is done by examining the histograms, image alarm and the signature separability.  If bad or outlier signatures are found those should be deleted from the project and new signatures should be taken in it's place.  Once that is done the signatures are combined into their respective classes.  The following image shows the spectral signatures of the classes combined.

The next step is to actually perform the supervised classification, the classes have now been trained.  This is done using the supervised classification tool in ERDAS.  The user inputs their image and the classification scheme they just created.

Finally a visually appealing map was created in ArcMap with this final raster image.


Results:

The supervised classification method turned out okay except that there was much overlap between the forest and agricultural areas.  This is because the samples taken had a similar spectral signature in the first 3 bands.  There is also much overlap between the urban and bare soil areas.  This can be fixed using more advanced methods of classification.

The following image is the unsupervised classification that was created in lab 3.  The supervised and unsupervised classification resulted in very different results.  Most of the unsupervised map is agriculture and most of the supervised map is forest. 

In later labs, more advanced methods of classification will be explored that will fix some of the problems these simple methods of classification have.


Sources:

Cyril Wilson