The goal of this lab was to explore various image functions in the Erdas software. These functions were: delineating a study area, image fusion (pansharpening), radiometric enhancement, linking satellite images to Google Earth, resampling images, image mosaicking, and using binary change detection.
METHODS
Delineating a study area was completed by using an inquire box to outline the study area, then creating a subset image using the coordinates of the inquire box. A subset was also created using an area of interest shapefile.
The next image function used was image fusion. This is done to optimize spatial resolution. The multiplicative function of pansharpening was used to raise an image with a 30 m spatial resolution to a 15 m resolution.
Next, radiometric enhancement techniques were performed. The technique used was haze reduction. It was dont with a raster image function within the Erdas program.
Next a satellite image was linked to Google Earth. This was done using a tool within the Erdas program. The viewers were linked and synced so a movement in one was mimicked in the other.
The next function performed was resampling. A satellite image was first resampled to a higher spatial resolution (30 m to 15 m) using the nearest neighbor method, then bilinear interpolation was used.
Image mosaicking was then performed using adjacent satellite images. First Mosaic Express was used, then Mosaic Pro. The Mosaic Pro mosaic used histogram matching to make the images more similar.
The last image function used was binary change detection. A binary change image was first created using a tool within Erdas. The histogram was used to determine the threshold of values that would signify a change. Areas with values above this threshold were exported to crate a binary change image.
RESULTS
The study area subsets are shown blow. The first is the subset created by the inquire box. The second was created by using an area of interest shapefile.
The results of pansharpening are shown below. The left window is the original image, and the right window is the pansharpened image.
The result from the haze reduction is shown below. The original image is on the left panel, and the product of haze reduction is on the right. Notice the haze that was in the Southeastern section was removed.
The results from resampling are shown below. The original image is first, followed by nearest neighbor and bilinear interpolation. There is no discernible difference between the original and nearest neighbor. However, bilinear interpolation is noticeably smoother at the price of being blurrier and less accurate.
The results from image mosaicking are shown below. The first was done with Mosaic Express. Notice how the colors of the images differ and the border is noticeable. The next image was created using Mosaic Pro. Notice how the colors were matched with histogram matching and the border between the two images is less pronounced.
The next images are from the binary change detection. The histogram with marked thresholds is shown below. Followed by that is the binary change areas overlaid on the original study area. The red signifies areas that have changed.
SOURCES
Data obtained from Cyril Wilson for use in 338 course.
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