Lab 8

In lab 8 we took samples of reflectance from 12 different types of surfaces from a Landsat ETM+ image.

From this image we had to take samples of the following surfaces.

1. Standing Water
2. Moving water
3. Vegetation
4. Riparian vegetation.
5. Crops
6. Urban Grass
7. Dry soil (uncultivated)
8. Moist soil (uncultivated)
9. Rock
10. Asphalt highway
11. Airport runway
12. Concrete surface (Parking lot)

Using the chart below and the knowlegdge of shapes and association, I was able to locate all of the surfaces in the image.

Using Erdas I went to Drawing and drew a small polygon, which is my AOI, in the featured surface. I then went to Raster -> Signature Editor. Then created a new signature. This than gave me a line graph showing the different reflectance in 6 bands. All 12 features are in the line graph below.

 

Lab 6

In lab 6 we used Image Geometric Correction to combine two images. In the first part of the exercise we use Image-to-mpa rectification. That is, we took a satellite image of the Chicago area and combined it with a USGS map of the same area. Below are the to images to be combined.

The USGS 7.5 minute digital raster graphic.

Satellite image.

Using Multipoint Geometric Correction we use the map as the reference image and the satellite image for the input image. We then select 4 Ground Control Points(GCP) spread out over the entire map to ensure all parts of the image are stretched and adjusted equally. We then must make sure that all 4 points are within an RMS error of <2.

Here is the image showing my RMS error of .402. The resulting image is below.

If you look closely the image is very similar to the original satellite image. Though some adjustments can be seen and the second image is not quite is high a quality in detail.

The second part of the lab was to use Image to image registration. This is the process of taking two images of similar geometry and the same geographic location and combining them.

The top map is the first image and is highly distorted. The distortion can be seen in the bottom image showing the better quality image overlaid on the poor image. This is done using the Swipe option in Erdas. Now to combine them using 12 GCP's.

The above image shows the GCP's applied to both images.

Lab 7

In lab 7 we first calculated scales and distances of photographs that were nearly vertical when taken. The first exercise was to measure the distance between two points with a ruler. The image below was used.

Notice points A and B in upper left corner.

I had to physically take a ruler and measure the distance between point A and B. The distance came out to be 2 11/16 inches. Each inch indicates 3000 feet, so the distance came out to be 8062.5'. The distance was also recorded by a surveyor of 8822.47', take with a engineer's chain. So my distance came within 800'.

Second part was to calculate the scale of a photo. The phot was described as being taken 20,000 feet above sea level with a camera that had a focal length of 152mm. Formula for this is

f= Focal Length
H= Altitude of photo
h= Elevation of terrain

My calculations came out to be 1/38408 scale.

Next we took calculation of an given area in an image. We were to calculate the area of a lagoon in the image below.

 

 

We first had to digitize the lagoon, then the area was calculated.
Area
37.8128 Hectares
93.43759041069 Acres
Perimeter
4151.55 Meters
2.579653 Miles

Relief displacement was next. Using the image below we calculated the relief displacement of the smoke stack.

 

d = (h x r)/H

d = relief displacement.
h = height of object (real world).
r = radial distance of top of displaced object from principal point (photo).
H = height of camera above local datum.
h= 7/16"
r= 31,125'
H= 3980
All comes out to be 10264.21168 relief displacement.

Stereoscopy is process that creates an illusion of depth to an image. We took two images and combined them using GCP's to form an image with 3D illusion.
Here is the final image.

 

With the use of polariod glasses slight elevation changes can be seen. The areas that show the most elevation changes are in wooded areas.

The last part of the lab was Orthorectification. With the use of GCP's we combined multiple images.

 

The two images to be orthorectified. Notice that the images are of the same general area, but not the same.

After many GCP's applied the final image comes out as this.

 

The bottom two images uses the slide tool showing how accurate the orthorectification was.