Spatial Enhancement, Multispectral Data, and Band Indices

There are several techniques that can be employed within ERDAS Imagine to identify specific features using multispectral imagery. These features can then be displayed using different color band combinations to make them easier to distinguish visually. In general, the four steps used to identify features in ERDAS are:

1. Examine the histogram for shapes and patterns in the data.
2. Visually examine the image as grayscale for light or dark shapes and patterns.
3. Visually examine the image as multispectral, changing the band combinations to make certain features stand out.
4. Use the Inquire Cursor to find the exact brightness value of a particular area.

In this lab, we were asked to identify three features within a multispectral image using these methods of interpretation based a given set of criteria. We were then asked to select an appropriate color band combination to display these features in a way that makes them clearly stand out from their surroundings. 

The map below displays the first features causing a spike between the pixel values of 12 and 18 in Layer_4. These are all the water features in the image. I first used the panchromatic image to display only Layer_4 in grayscale. I knew that the features causing a spike between the pixel values of 12 and 18 would appear very dark in the Layer_4 panchromatic image, and the darkest features appearing almost black were the water features. I then went to the multispectral image and experimented with various color band combinations to see which combination contrasted the water features the most with their surroundings. TM False Color IR is the color band combination I selected. 

The second features causing a small spike in layers 1-4 around pixel value 200, and a large spike between pixel values 9 and 11 in Layer_5 and Layer_6 are the snow and ice features in the image. Since a pixel value of 200 would appear very bright in the image, I again looked at Layers 1-4 using the panchromatic image to see what areas appeared the brightest. I then used the panchromatic image to view Layers 5 and 6 to see what areas appeared darkest since pixel values of 9-11 would appear very dark. The snow and ice features met both criteria. I selected the TM False Natural Color which displays the snow and ice in a vibrant teal color.  

Lastly, the areas of water that appear much brighter in Layers 1-3, somewhat brighter in Layer_4, and unchanged in Layers 5 and 6 are the areas of shallow water. I used the same method of viewing each layer in grayscale and identifying what areas of water met these criteria. My conclusion was that shallow areas of water appeared brighter than deeper water in Layers 1-3 but relatively unchanged in Layers 5 and 6. I then used a custom color band with Layer_4 displayed as red, Layer_2 displayed as green, and Layer_1 displayed as blue. Using this color band combination, shallower water appears bright teal, and deeper water appears darker blue.