Lab 8: Spectral Signature Analysis

Goal and Background

This purpose of this exercise was to get some experience interpreting spectral reflectance of features on the Earth's surface from images taken by satellite sensors. We found the spectral signatures of various materials and surfaces in a remotely sensed image of Western Wisconsin.

Methods

Figure 1 - AOI in Lake Wissota

First, I digitized an area of Lake Wissota using the draw polygon tool (figure 1). Then, under the Raster toolbar, I clicked the Supervised drop-down menu and selected Signature Editor. This brought up the Signature Editor, where I could create a new signature from AOI (area of interest). Once this was added to the menu, I changed the name to Standing Water to differentiate it from the other signatures I would be collecting. Figure 2 shows the Signature Editor menu.

Figure 2 - Standing Water signature

From this menu, I could also display the Mean Plot window, shown in figure 3.

Figure 3 - Standing water mean plot

We were then tasked with finding the signatures of 11 more features using the same technique:

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)

Figures 4 and 5 show the signatures and the mean plots of all the features together.

Figure 4 - Signature of 12 features

Figure 5 - Mean plot of all 12 features

Results

The results demonstrated the difference in spectral reflectance between many different features and materials. A skilled remote sensing technician would be able to identify these features by their signature plots alone.

Lab 7: Photogrammetry

Goal and Background

The goal of this lab was to improve our understanding of the math behind scales, measurement of areas and perimeters, and calculating relief displacement. It also serves as an introduction to stereoscopy and orthorectification.

Part 1: Scales, measurements and relief displacement

Section 1

The first part of the lab involved finding the scale of an aerial image. Given a section of highway that we already knew the real world distance for, we were able to find the scale of the image by measuring the distance from one point to the other on the image itself.Next we found the scale of a photo when only knowing the altitude of the aircraft carrying the camera, the focal length of the camera, and the elevation of Eau Claire County.

Section 2

Figure 1 - Digitized area to be measured

Section 2 involved finding the area and perimeter of features in an aerial photo of Eau Claire. I opened the 'Measure' tool and selected 'Measure Perimeters and Areas'. This allowed me to digitize an area and find out what the area or perimeter was. After digitizing, I was able to change the units of measurement and have the results update on the fly.

 Section 3

Figure 2 - Image of Eau Claire area used for calculating relief
displacement

For section 3, we calculated relief displacement. Figure 2 shows the image and feature that was used for this exercise.

Knowing the height of the aerial camera above datum (3,980 ft), the scale of the image (1:3,209), and by finding the real world height of the smoke stack (by using the image scale), we can find the relief displacement of the smoke stack labeled 'A' in the image.

Part 2: Stereoscopy

In part 2 of the lab, we learned how to create a stereoscopic image of Eau Claire. First, I opened an image of Eau Claire at 1 meter spatial resolution and a DEM (digital elevation model) of the same area at 10 meter spatial resolution. Under 'Terrain' I chose 'Anaglyph' to open 'Anaglyph Generation', the tool I would be using. I brought in my two images and set the vertical exaggeration to 2. 

When the tool finished running, I had a 3-D image of Eau Claire needing polaroid glasses in order to view it. The result was impressive, though slightly too exaggerated in some places. It did, however, make it easier to interpret geographical features in Eau Claire.

Part 3: Orthorectification

Figure 3 - LPS Project Manager window

Satellite and aerial images usually have many geometric errors that must be corrected before they can be used professionally. Orthorectification is the process of using currently orthorectified photos to correct new ones.

LPS Project Manager (Figure 3) is the tool used for orthorectification. This is found under the "Toolbox" tab.

Figure 5 - GCP collection

By setting a projection, and adding two overlapping images, we can orthorectify (figure 4). Similar to geometric correction, we used GCPs (ground control points) on both images to match the locations. Figures 4 and 5 illustrate this process.

Figure 4

The final images are perfectly lines up at the edges. Figure 6 shows how effective the process of Orthorectification is. This process is normal done by the company or group collecting the images, so this is completed already when the final image gets to public use.

Figure 6 - Orthorectified image.

Lab 6: Geometric Correction

Goal and Background

The purpose of this lab was to introduce us to geometric correction. Geometric correction is performed on satellite images before they can be utilized for data interpretation. This is done to ensure that spatial errors are kept to a minimum. We explored two major types of geometric correction during this exercise.

Part 1: Image-to-Map Rectification

Method



Figure 1 - Uncorrected Landsat TM image of Chicago on right,
USGS 7.5 minute DRG of Chicago on left

Image-to-map rectification involves geometrically correcting an aerial photo using a scanned topographic map of the same area as a reference. These digital maps, called digital raster graphics (DRG), have a coordinate system while the uncorrected aerial photo does not yet.

To practice this technique, I opened a Landsat TM satallite image of the Chicago area as well as a USGS 7.5 minute DRG of the same area (Figure 1) that I could use as reference. The tool for doing this is found in the "Multispectral" tab by clicking on the "Control Points" button. I chose to use a first order polynomial equation because the image was not distorted enough to justify using a higher order polynomial. I also chose to use the Chicago DRG as my reference layer.



Figure 2 - Multipoint Geometric Correction window.
First set of GPCs placed, but RMS error too high.

Once I was in the Multipoint Geometric Correction window, I was able to start correction. This window had my image to be corrected in the pane on the left and my reference image in a pane on the right. Both panes also contained smaller windows with the full image and the zoomed Inquire box images (Figure 2).



 Next, I began the process of adding ground control points (GCPs) on each image. This involved adding pairs of points on each image as geographicly close as possible. This can be time consuming, as the points in each image must be very close to reduce error. Since I was using a first order polynomial, I only needed three pairs of control points (though I used four to be more precise). The higher the order of polynomial, the more GPCs are required. Figure 2 shows my original GPCs, however the RMS (root mean square) error is still too high. I took additional time to perfect the GPCs.

Results

Idealy, the total RMS error should be below .05. For the purposes of this lab, however, we were only required to have an RMS below 2.0. Once this was accomplished, I ran the tool and created a geometrically corrected image.