Lab 6

Goals and Background

The purpose of this lab was to learn how to preform geometric correction on an image.  There were two forms of rectification in this lab

1.  Image to map rectification
2.  Image to image rectification

Methods

For Image to map rectification

1.  First the images Chicago_2000.img and Chicago_drg.img were brought into the two viewers.  Then I clicked on multispectral and Control Points.

2.  I selected Polynomial for the geometric model, in the next window I accepted the default Image Layer(New Viwer) in the GCP Tool Reference Setup.  I then selected Chicago_drg.img as my reference image.   After this step I accepted all of the default properties. 

3.  I deleted the default GCPs and selected my own.  For the equation being used only 3 pairs of GCPs were needed, but 4 were selected. 

GCP points on Reference image shown on the right and input image shown on the left

4.  The GCP points were moved around until the total RMS error (shown at the bottom of the screen) was less than .5

Total RMS error shown at bottom of the screen

5.  I then clicked on the Display Resample Image Dialog button, I named the output file Chicago_2000gcr.img and saved it to my Lab 6 folder.  I left all of the default parameters and executed the geometric correction.

For Image to Image rectification

The process was similar to the image to map rectification.  For my reference image I used Sierra_Leone_east1991grf.img and for my input image I used Sierra_leone_east1991.img.  The only differences in the process were as followed

1.Under the polynomial model properties I changed the polynomial order to 3

2.  12 GCP pairs were used to account for the higher polynomial order

3.  The resample method was changed to bilinear interpolation

Results

The resulting geometrically corrected images can be seen below

Geometrically corrected image of Chicago

Geometrically corrected image of Chicago

Through this lab I was able to learn the steps that need to be taken in order to properly geometrically correct an image

Sources

Satellite images:  Earth Resources Observation and Science Center, United States Geological Survey.

Digital raster graphic (DRG): Illinois Geospatial Data Clearing House.

Lab 5

Goals and Background

The main purpose of this lab was to introduce important analytical skills in remote sensing.  The specific skills this lab emphasized were:

1.  How to mosaic two adjacent satellite images with Mosaic Express and Mosaic Pro
2.  How to implement the normalized difference vegetation index
3.  How to apply various filters
4.  How to apply various contrast stretches
5.  How to perform a histogram equalization
6.   How to perform binary change detection

Methods

To mosaic two images together using Mosaic Express, the following steps were taken:

1.  I Imported the two images found in the Lab 5 Mosaic subset folder.  When adding these I selected Multiple images in virtual mosaic under the multiple tab, and I selected Background transparent and fit to frame under raster options.  When this was done, the below image could be seen in the viewing window

Two imported images

2.  Images were added into Mosaic Express the image intended to be on top was added first, and the other image was added second.  I clicked next until I got to the output dialog box, where I named the output image and saved it to my lab 5 folder.  The output image can be seen below

Output image from Mosaic Express

To mosaic images together using MosaicPro the following steps were taken:

1.  I Repeated step 1 from the previous section.

2.  I Clicked on Mosaic and then click MosaicPro.  When adding images, I clicked the Image Area Options tab and clicked the compute active area button for both of the images.  I added the image I wanted displayed on top first and the other image second.  When this was done the below image was shown in the MosaicPro window

MosaicPro Window

3.  I clicked on the color corrections icon and checked the use histogram matching option and selected overlap areas and clicked ok.

4.  After selcting the default settings for output image options and set overlap fucntion, I ran the mosaic process and saved the output image to my lab 5 folder.  The resulting mosaic image is shown below.


Mosaic image produced with MosaicPro

To implement the normalized difference vegetation index

1.  I brought the image in, and then went to Raster:Unsupervised:NDVI.  I checked that the sensor said Lansat TM, and then selected NDVI for the function under select function.  I saved the output image in my Lab 5 folder.  The resulting image is shown below


NDVI Image

To apply a low pass convolution image

1.  I brought the high frequency image in and then went to raster: spatial: convolution.  I selected 5X5 Low pass for the Kernel and saved the output image in my Lab 5 folder.  The comparison of the two images can be seen below.



Original image on left, 5X5 low pass image on right

To apply a high pass convolution filter

1.  I brought in the low frequency and then followed the same steps as the low pass filter, except I selected the 5x5 high pass filter.  The comparison of the two images can be seen below.


Original image on left, 5X5 high pass image on right

To apply a Laplacian convolution filter

1.  I brought in the image and then followed the same steps for the other filters, except selecting 3X3Laplacian filter instead.  The comparison of the two images can be seen below.


Original image on Left, Laplacian edge detection image on right

To apply a minimum maximum contrast stretch

1.  I imported the image, went to general contrast and changed the method to Gausian and applied the change.  The resulting image is shown below.

To apply a piecewise contrast stretch

1.  I imported the image and clicked on General contrast then piecewise contrast.  I set the specific ranges for each mode seen on the image histogram and then clicked apply.  The result is shown below



Piecewise contrast stretch

To perform a histogram equalization
1.  I imported the image and clicked on radiometric and histogram equalization.  I saved the resulting image in my Lab 5 folder. 

To perform Binary change detection

1.  I imported the two images, clicked on raster function then clicked on two image function.  I changed the detection process to layer 4 and then I clicked ok to run image differencing.

2.  I then changed the pixels using model maker, setting the model so that it was for binary change detection. 

3.  I then developed another model maker that would show the changed pixels within the study area.


4.  I mapped the resulting image over an NIR image for the same area, and changed the pixels to a brighter color.  I added a legend and made a few adjusting to make a basic map.

Results

All of the resulting images from this lab I saved to my lab 5 folder.   During this lab, I learned ways that it is possible to take images and adjust them for particular uses or to make them easier to interpret by improving their quality.

Sources:  All image functions were performed in Erdas Imagine and ArcMap.
                All images were made available in our lab folder