GIS 4036 - Spatial Enhancement

This lab follows methods of spatial enhancement using various techniques in both ERDAS IMAGE and ArcGIS Pro. The process summary for the different sections can be seen below, as well as some finished map products that were produced using these methods. 


Exercise 1:

In this exercise, using the glovis.usgs.gov site, I explored the different freely available satellite data from various satellites and how to view the metadata. I did make an account with the website to be able to download data, however I did not receive the confirmation email yet so I am using the data that was provided already in the UWF drive and did not download any data from the website.

The image file in the folder was in a .tar.gz format. To convert it into a .tif file, the process involved unzipping the file into a .tar folder, and then once again unzipping that folder into a folder which contains 7 tif files and then supporting documents. It seems this process progressively sheds layers of the original file into a more usable format by unzipping the tif files.

Exercise 2: This exercise starts with converting an image in black and white to an image passed through a low pass filter, a high pass filter, as well as sharpened, and then saved as image files. There is a tool to do this right in Erdas Image, using kernel density. You simply select the tool and the number of kernels, this example uses a 3x3 filter. This tool is found under the raster tab, spatial, and convolution tools. The output image file is automatically generated.

The same process is replicable in ArcGIS Pro of performing a kernel density. This is done using the Focal Statistics tools and instead of running a 3x3 kernel density, I ran a 7x7. The statistic was set to mean, meaning that the calculation for each cell would be based on the mean of the surround cells. The 7x7 image also groups the cells into larger areas, so more fine detail gets lost and replaced with a broader look at larger features.

Exercise 3: Write down every enhancement process that you run on this image, and describe any noticeable effects of each. (Consider this the most important part of the process summary – there should be a lot of detail here.)

This section also looks at histograms in both Erdas and ArcGIS Pro. In Erdas, you can view histograms in a couple different locations, including in the metadata and also as a tool in the panchromatic tab, under adjust radiometry. You can manually adjust the image using the histogram to apply changes to the colors shown in the image to highlight dark or lighter areas. Histograms can also be used in ArcGIS Pro by looking under the symbology tab and setting symbology to histogram specifications. In this tool, you can also manually adjust the specifications of different bands shown in the image through the histogram. 

While manually adjusting the appearance in symbology in ArcGIS Pro, I notice you also can change the bands displayed by changing the layer under the RGB option. In mixing these around, I notice that some areas will really stand out more than others. For example, some areas will become bright pink or dark green. By modifying these, you can make things stand out, though it may be confusing to leave bands this way as they may not be representing RGB well. I also notice that you can manually make these changes a bit more gradually and strategically when using the histogram.

Exercise 4:

This section explores changing the band type of a true color image to see if different bands provide a clearer understanding of features on the land. By changing the band value in Erdas, different features will stand out more or less. Looking at the histograms for both these image layers can also provide insight into the data.

Exercise 5:

This section uses tools in Erdas to compare a single image that was uploaded twice, once as a ndvi image and once as a true color image. This method allows one to compare the features at different bands. Depending on the goal of the analysis, features can be emphasized using these different techniques.

Exercise 7:

This part required locating and identifying features based on clues. The first features clue is an object in the greyscale frame of layer 4 that is between pixel values 14 and 18. This feature appears to be a river, as it is a large spike in the histogram, indicating a large feature. The spike is on the left side of the histogram, indicating a dark feature. When using the inquire tool in Erdas, the pixel values fall within the specified range when looking at the river.





The second feature is described as having a small spike at around pixel value 200 in layers 1-4 and a large spike in pixel values 9-11 in layers 5 and 6. These features are the top of mountains with no foliage cover. This is identified with the same method as the first, using the histogram to determine if the feature should be large or small, and dark or light. The feature should be light as the spike is on the right in the last several layers, and is large, indicating that it is the feature with the most concentration of light colors in the greyscale. It is also a small spike in the layers 1-4, indicating that overall, it is a smaller feature, as there are very few light-colored areas in the overall image. Additionally, the inquire tool identified this area to fall within the specified pixel range roughly. I can identify this as being the cap of terrain features as the shadows and patterns in the image indicate mountains, and in the infrared false color image, the surrounding areas is red which would indicate foliage and the top of the mountain is still white.  



The final feature is described as certain lighter areas in the water that are represented differently in different layers. In some layers they appear brighter and others they are unchanged. I believe this is referring to areas where water is shallower and some flow pattern can be seen in some sand beneath the water, near to the coast. These features behave in the way described. Additionally, some trails of water behind boats can be seen in this way, as they largely disappear in layers 5 and 6 but are somewhat emphasized in layers 1-4.




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