GIS4035 - Lab8 - Thermal & Multispectral Analysis

     For the deliverable assignment I scanned both composite images from the lab to look for unique areas of interest.  Ultimately, I chose to use the composite image of Northwest Florida as I am more familiar with that area.  I opened two views in ERDAS with a grey scale band 6 image and a multispectral image of the same location.  I adjusted the breakpoints of the gray scale histogram by using the Discrete DRA button.   This removed the “whitewash” appearance of the image and made features appear more distinctly by providing more contrast.  I chose to change the band combination of the multispectral image to be a false natural color or Thermal IR composite of 647 as we used in the lab Exercise 3.

After beginning with the setup described above, I began to scan the synced images.   I honestly used size, shape, and texture to notice my area of interest.   It appeared very bright in both images but had a distinct, unusual shape that was highlighted in the multispectral image.   The overall feature was a square shape that had a Star of David like symbol in the center of the feature square.   The symbol in the center was a square with a rotated 45° square on top.  The details of this feature could not be seen in the gray scale image.   I began displaying the image in various multispectral bands to see which band combination could display the feature the best.  Ultimately, the RGB 742 band combination made the feature stand out the most from its surroundings.   I used the Portland State University webpage (PDX) mentioned in the lecture to help me understand how this combination could provide more detail about the feature.   Urban areas in this band combination appear in different shades of magenta.   The symbol at the center of this feature area had two distinct shades of magenta.   The shape of the feature and width of the sides of the square and diamond comprising the center symbol resembled that of an airport runway.   I was confused by the shape of the symbol as most runways appear as an X or cross or as parallel long lines.  The PDX website described differences of reflectance responses depending on the terrain feature.   If this feature was a runway the square and the diamond may appear as different shades of magenta because one is concrete and the other is asphalt.   Another explanation for the color shade difference could be the age of the terrain feature.   Maybe the square and diamond are the same material but one was resurfaced or is older than the other surface.  To confirm my suspicion that this was some type of airport I used Google Earth.   The feature is a Naval Outlying Field that is used for helicopter training which explained the difference in the runway layout versus a traditional airport.

I feel less sure of my understanding of the relationship between bands and layers and how to know which band combination is covering what wavelengths.   I think some of my confusion is related to semantics and the interchanging of the words bands and layers.

Spencer Naval Outlying Field Used For Helicopter Training Displayed Best in R-7, G-4, B-2 Band Combination

GIS4035 - Lab 7 - Multispectral Analysis

In Lab 7 we learned four different steps to identify features in ERDAS Imagine and in ArcMap.   Each lab helps me better understand some basic EMR concepts as well as become more familiar ERDAS Imagine but I always feel a little overwhelmed with the amount of material and information covered.  The first four lab exercises explained how to view and use an image's histogram in order to identify features.  Spikes in the histogram indicate a cluster of pixels at certain brightness values.  If a spike is high/large then the frequency is high meaning there are a lot of pixels in the image with that same pixel brightness value or brightness range.   Conversely, a small spike indicates low frequency and there are a few clusters of pixels with that pixel brightness value.  Pixels with low brightness values (near 0) have a dark appearance and pixels with a high brightness value (maximum brightness depends on Data Type - Ex. 8 bit data type has a Max value of 256) have a light appearance.  Examining an image visually by displaying it in gray scale or various multispectral band combinations can aid in feature interpretation.  Features' reflectance properties usually vary across different bandwidths.  Using an Inquiry Cursor can let you look at the pixel values for that selected pixel (or coordinate if using Lat/Long) across all layers.

Exercise 5 required us to identify three different features with provided clues in the image.

Feature 1:

First I started off using Panchromatic view of the image displaying Layer 4.  I knew that pixels with values ranging from 12-18 would be dark.  Since the spike on the histogram of this layer was large/tall I new the frequency of pixels with this value was high.  This helped me deduce that the feature was a water body because what appeared dark all over the map was water bodies.  I verified that different water bodies in the image had the same pixel range in Layer 4.  I then chose a particular water body from the image to create a subset.  I then displayed it in a multispectral band combination using False Natural color RGB-543.   This band combo made the feature stand out in my subset image.

Feature 2:

I used multiple views in ERDAS Imagine to examine the image in different multispectral bands as well as in grey scale.  The information provided for this feature helped me deduce that the feature was very bright (light colored/white) in layers 1-4 and there was not a high frequency or a lot of pixels with this brightness due to the small spike.  This made me zoom in on the white areas on the image.  I could then use the Inquiry cursor and corresponding table to see the different pixel values for each layer.  I then compared the pixel values for these white areas on the mountains with smaller white areas in the image.   The smaller white areas did not have the same pixel values so I deduced these were cloud features.  Various band combos helped identify the larger white area feature more distinctly.   Probably the False Natural color combo displayed the feature best but I had used that combo for Feature 1 so I chose to display with Near IR band combo.  I deduced that these features were snow/ice because I looked at each layer in grey scale and could see that they appeared white/bright in layers 1-4 and dark in layers 5-6.

Feature 3:

This last feature stumped me even though at first look of the tm_00 image file I noticed something different in the bottom right of image.  I knew that the vast majority of water bodies were appearing very dark in the image and I scoured the image, or so I thought, trying to zoom in and see a different looking water body.  I finally found the area that I had originally noticed at the very beginning of the assignment.  I used the Inquiry cursor and table to compare the water body feature in the bottom right of the image with the bodies of water in the rest of the image.  I deduced that layers 1-3 appear more bright than normal because the body of water is shallow.  I googled and found that a 453 (RGB) band combo can help display the differences in water bodies.   I did change the band combo and liked the colors of the image; it reminded me of Klimt’s “Kiss” painting – sparkly/gold.   I didn’t have a name for this band combo like False Natural or Near IR so I didn’t use it but instead displayed the feature in True Color. 

I enjoyed the riddle if you will of this assignment in trying to solve/identify the unknown features.   I still do not fully understand which multispectral combinations are best for identifying certain feature types.  I also do not know what band number a layer is as the lab stated that an image's layer numbers do not always correlate to a band number.   I am hoping with more experience I will better understand these relationships.