The goal of
this lab is to demonstrate how to enhance images in order to preform analysis
and also to focus in on a precise area in a large satellite image. I have delineated the area of interest from a
large image, explored techniques to enhance spatial and radiometric resolution,
linked Google earth with Erdas Imagine, and used bilinear and nearest neighbor
resampling methods.
The first part of the lab deals with
subsetting images using an inquire box and an area of interest shape file. First I made an image subset of the Eau
Claire area using an inquire box found in the raster tools.
Next, I used
a shapefile to create an area of interest file.
This was done using the subset and chip option in the Raster tools.
In the second part of the lab
exercise, I have used image fusion to improve the spatial resolution of an
image. I did this using the resolution
merge icon in the pan sharpen tools.
First I used the nearest neighbor resampling technique.
Then I used
the bilinear interpolation resampling technique to fuse the images.
Both strategies of image fusion result in a pan sharpened
image. The pan sharpened image
definitely has a greater range of colors, brighter colors and a higher
resolution than the input image. A pan
sharpened image has a higher spatial resolution, which is noticeable especially
when you zoom in on the images. The pan
sharpened image also has a larger range of more vibrant tones, the river is a
darker black and the pinks are slightly darker or bolder. Much of the area that was a light blue tone
in the input image is more of a grey in the pan sharpened image.
In part
three of the lab I improved the radiometric resolution of an image using the
haze reduction tool under radiometric in the raster tools. The input image is much lighter in color and
cloudier than the haze reduction image.
When you zoom in and out the resolution of the images is the same but
the colors are definitely brighter and clearer in the haze reduced image. There are a few places in the input image
that have light colored clouds or haze and these do not appear in the haze
reduced image. The rivers went from a
blue color to black and the pinks went to more vibrant red tones. The haze reduced image is definitely clearer.
In part
four of the lab I have linked Google Earth with Erdas Imagine in order to
create synchronized views of an image.
This was done using the connect to google earth icon, then selecting
match GE to view and Link GE to view icons. You can zoom in very close at a
high resolution on the google earth viewer, which can be very useful if you are
trying to identify elements of an image.
When you zoom in it becomes much easier to identify objects on the
google earth image and there are some labels.
In part
five of the lab I used resampling to change the pixel size of an image. This was done by selecting resample pixel
size under spatial in the Raster tools tab.
I changed the output cell size to 20 meters, from the input cell size of
30 meters. I used the nearest neighbor method for the first output image, then
the bilinear interpolation method for the second.
There is some
difference in the pixilation between the input image and the nearest neighbor
resampled image. When you zoom in very
close, you can notice that the pixel size is smaller in the nearest neighbor
image and that the formation of the pixels is slightly different. When zoomed out very far it is hard to tell
the difference between the images. The
nearest neighbor method uses the brightness and tone of the closest pixel. The
bilinear interpolation image is also similar to the original image when zoomed
out, but definitely has a different pixel formation when closely zoomed in. The bilinear interpolation uses the tone and
brightness that is calculated using the four surrounding pixels. This results in a pixel formation different
from the original image and the nearest neighbor image.
Works Cited
"Earth." Google. N.p., n.d. Web. 01 Nov. 2013
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