In Module 9 we learned about using ERDAS Imagine and ArcMap to perform unsupervised automated classifications. In a previous labs, Modules 3 and 4, we had learned about manual land use land cover classifications. Exercise 1 had us use the ArcMap Iso Cluster and Maximum Likelihood Classification tools which are part of the Spatial Analyst toolset.
Exercise 2 required us to use ERDAS
Imagine to classify a high resolution image of UWF’s campus. The goal was to reclassify all
fifty categories of the original image into 5 feature classes. Using
the ERDAS Imagine attribute table we zoomed into the image to view pixels of
several features that were clearly visible.
Next, we changed the color of that feature in the attribute table to one
of the specified four categories: Trees/Dark Green, Grass/Green,
Buildings/Grey, Shadows/Black. Using the
Swipe/Toggle/Blend tools under the Home tab/View Group we could compare our
classification category assignments to the original image. Changing the color of one feature to make it
stand out helped tremendously in assigning class categories. Some features were difficult to classify because
the pixels were split between impermeable and permeable objects. For some pixels it was difficult to tell whether
it represented a roadway, barren land, or grass.
The assigned pixel color might indicate any of those items. Shadows on building rooftops were really part
of the roof but also a shadow creating a darker pixel value than the
other rooftop pixels. Creating a “mixed”
category class was needed for those pixels that were split among the
impermeable and permeable classes of buildings/roads, grass, trees, shadows.
It was necessary to “recode” the image using the Raster tab/Thematic button and select Recode. This is different than the Thematic tab/recode button. The Recode dialog box enables you to sort columns in alphabetical order and create a New Value for each feature row. After the recode we had to go back and add the Class Names column and add an Area column.
Lastly we calculated the total surface area of the image by adding the individual classification area values. We had to determine the percentage of land surface that was permeable and impermeable. In dividing the shadow and mixed categories between permeable and impermeable I chose to split their acreage 50/50. Some shadows of buildings for example were fairly large in comparison to shadows of trees. However, there were more trees with shadows than buildings with shadows. It was hard to determine the exact split so I took the conservative approach and split it evenly. I used the same assumption for the Mixed class.
It was necessary to “recode” the image using the Raster tab/Thematic button and select Recode. This is different than the Thematic tab/recode button. The Recode dialog box enables you to sort columns in alphabetical order and create a New Value for each feature row. After the recode we had to go back and add the Class Names column and add an Area column.
Lastly we calculated the total surface area of the image by adding the individual classification area values. We had to determine the percentage of land surface that was permeable and impermeable. In dividing the shadow and mixed categories between permeable and impermeable I chose to split their acreage 50/50. Some shadows of buildings for example were fairly large in comparison to shadows of trees. However, there were more trees with shadows than buildings with shadows. It was hard to determine the exact split so I took the conservative approach and split it evenly. I used the same assumption for the Mixed class.
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| Unsupervised Classification of the University of West Florida Campus |
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