Supervised Classification

Introduction:

Land use/land cover (LULC) information can be extracted using numerous methods, one of which is "Supervised Classification". Supervised classification requires the remote sensing expert to collect training samples for the classes they wish to identify. These training samples must be evaluated, to ensure they reflect the spectral variety contained within each LULC class. Once the training samples have been vetted, the remote sensing expert uses them to classify the image.

Methods:

The first step of supervised classification is the collection of training samples. I collected a total of 50 training samples, by using a combination of my Landsat 7 image and Google Earth Imagery. Once all of the samples were collected, I analyzed each class's samples to ensure their spectral signatures resembled the spectral signatures of their respective land cover types (Figures 1&2).

Figure 1: One of these water signatures doesn't match the others, so it will need to be eliminated.

Figure 2: All of these forest signatures resemble one another

Once all of the classes' signatures were assessed, it was necessary to evaluate the spectral separability of the classes. This was done to ensure the classifier will be able to discern one class from one another (Figure 3).

Figure 3: The spectral separability value of 1964
 for bands 1,3,4, and 5 is very good

Once the spectral separability was assessed, the image was classified using the Maximum Likelihood method.

Results:

The result of the Maximum Likelihood classification appears to be slightly less accurate than the image generated from the unsupervised ISODATA algorithm. This inaccuracy was likely introduced as a result of poor quality training samples.

Figure 4: The result of the Maximum Likelihood classification

Conclusion:

Supervised classification's strengths are that it allows the analyst substantially more control over what classes the data are sorted into, and doesn't require manual sorting of clusters. The weaknesses of supervised classification are that it requires training samples to be collected, and that the quality of the output classification depends on the quality of the training samples. The collection of good training samples is fairly dependent on the level of background knowledge the analyst has of the study area.