Goal and Background:
The purpose of this lab is to continue learning core components of remote sensing while using ERDAS Imagine software to carry out basic remote sensing procedures.
Images often need to be enhanced in ways that make them more easily observable. It is up to the remote sensing analyst to work with programs and use the procedures available to best enhance these images for the given goal.
Skills obtained throughout the lab include: image subsetting, image fusion, radiometric enhancement techniques, linking image viewer to Google Earth, resampling, image mosaicing, and binary change detection (image differencing) .
Methods:
Part 1: Image subsetting (creation of area of interest AOI) of study area
This section included creating an inquire box for an image subset, which is shown in Figure 1. Because the area of interest is often not a perfect rectangle, a second method must be used for an image subset. Figure 2 shows how shapefiles for Chippewa and Eau Claire counties were used as the area of interest.
Part 2: Image fusion
In this section an image with coarse resolution was combined with a panchromatic image to create a reflective image with a higher spatial resolution. Using Pan Sharpen, the two images were merged with nearest neighbor as the resampling technique and multiplicative as the algorithm. When comparing the image with coarser resolution to the new image, the new image was slightly more detailed with evidently smoother features when zoomed in.
Part 3: Simple radiometric enhancement techniques
In this section the haze reduction tool was used to enhance the spectral and radiometric resolution of an image.
Part 4: Linking image viewer to Google Earth
This section involved linking Erdas Imagine images with Google Earth. This can be helpful because Google Earth has some high resolution images of areas and can serve as a selective interpretation key in remote sensing.
Part 5: Resampling
In this section an image was resampled up in order to decrease pixel size. When comparing a resampled image to the original image, the new image was smoother and looked less pixelated when zoomed in.
This section involved combining two overlapping images together. This of commonly done when the area of interest lies in both images, so image mosaicking is often necessary. This process was done twice: once with Mosaic Express (Figure 3) and once with Mosaic Pro (Figure 4). Each has its strengths and weaknesses, and that can be seen in the results of this lab.
Part 7: Binary change detection (image differencing)
In this section image differencing for two images of the same area taken several years apart. This showed the changes that occurred from 1991 to 2011. Once the areas detected to have a significant change in brightness value were identified, that map was brought into Arcmap to make a map of this change, as seen in Figure 6. Figure 5 shows the distribution of data in the map.
Results:
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| Figure 1 |
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| Figure 2 |
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| Figure 3 |
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| Figure 4 |
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| Figure 5 |
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| Figure 6 |
Sources:
Satellite images are from Earth Resources Observation and Science Center, United States Geological Survey.
Shapefile is from Mastering ArcGIS 6th edition Dataset by Maribeth Price, McGraw Hill. 2014.
