The first step was extracting the spectral bands, this function on ArcGIS changes the band combination to see the urban features. This band change can be seen in Figure 1.
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| Figure 1. Band change of Area |
Once this is done, you have to better segment the image. To do this, classes were created for the different terrain in the image: Gray Roofs, Roads, Driveways, Bare Earth, Grass, Water, and Shadow. The classified image can be seen in Figure 2.
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| Figure 2. Different Terrain Types |
The next step performed was to merge the classes into two master categories: pervious, and impervious. These two categories represent land cover. After this, errors were found and the image was reclassified to help with the images. This newer image with the classes merged can be seen in Figure 3.
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| Figure 3. Pervious vs. Impervious Terrain |
The final maps, as shown in Figures 4 and 5 below, show the pervious and impervious terrains of an area. The second map shows this area further separated into specific terrains.
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| Figure 4. Pervious vs. Impervious Terrain |
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| Figure 5. Classification of Terrain |
Assignment Conclusions:
The maps shown above are an example of how versatile UAS data is, this particular data set can be used to calculate how much of a certain area is being taken up by houses or roads. This type of spectral analysis could also be used for other industries as well, such as forestry.