Lab 4

Goal and Background

The purpose of this blog is to show the different labs completed throughout the semester in the course: remote sensing of the environment taught be Dr. Cyril Wilson.  This lab is broken down into different parts and has numerous different goals.  They include 1) to delineate a study area from larger satellite image scene, 2) demonstrate how spatial resolution of images can be optimized for visual interpretation purposes, 3) introduce radiometric enhancement techniques in optical images, 4) linking a satellite image to Google Earth which can be a source of ancillary information, and 5) introduce students to various methods of re sampling satellite images.   When finished these skills should be learned: enhancing images for visual interpretation, and be in a position to delineate any study area from a larger satellite image scene. 

Methods

All images produced were done in the application, ERDAS IMAGINE 2013.

Part 1 

Section 1

In ERDAS IMAGINE (EI), open the image, eau_claire_2011.  To do this click the folder button in the upper right of the screen shown in figure 1 below, then navigate to the image.  W drive>Geography>Wilson>338>WilsonC>Lab4>Resampling>eau_claire_2011.  This step will be done many times when opening up new images in lab 4 in these folders: Image fusion, Radiometric, Resampling, and Subset.  

Figure 1: Click on the yellow folder in the upper right to add an image.

Next after the image was added, raster was clicked to activate the tools, then the image was right clicked to open an Inquire Box, shown in figure 2 below.

Figure 2: Black arrow showing the inquire box over the city of Eau Claire.
Image taken from Dr. Cyril Wilson 

After the inquire box was added, it was dragged to the proper size over Eau Claire and Chippewa area.  Next under raster, subset & chip was clicked followed by create subset image.  This will bring up a box where the input image will show the eau claire 2011 and the output file can be edited.  In the output folder I navigated to my name and lab 4 and re named the image eau_claire_2011sb_ib.  Next from inquire box was clicked, this brought the coordinates from the inquire box to match the coordinates in the box.  After these steps are completed hit okay>dismiss>and close out.  Then I navigated to the folder containing the new image and it was brought on to the screen.  The results can be seen in the results section below. 

section 2: Subsetting with the use of an area of interest shape file

In ERDAS IMAGINE eau_claire_2011 was opened under re sampling and a shape file, ec_cpw_cts.shp wad added from the folder subset.  To obtain the shape file files of type was changed from image to shape file show in figure 3 below.  

Figure 3: Changing the type from image to shape file
Image taken from Dr. Cyril Wilson

After adding the shape file onto the image it, should appear in color on the image.  Next the shape file was selected to create the area of interest file.  The shift key is held down and both counties were clicked.  This changed the counties from blue to yellow.  Then under home, paste from selected object was selected to create the aoi around the shape file.  Then the aoi of interested was saved to be used in later down the road: file>save as- AOI Layer As>navigate to folder> save as ec_cpw_cts.aoi.  Once the shape file was saved raster followed by subset & chip was used just like in section 1.  This time the output was saved as ec_cpw_2011sb_ai.img and AOI was clicked at the bottom of the box and the ec_cpw_cts.aoi was chosen to create the subset image.  After the process the new AOI, ec_cpw_2011sb_ai was brought in and can been seen under the results tab below.  

Part 2: Image fusion

ec_cpw_2000.img was opened in ERDAS IMAGINE from the image fusion folder.  Then in a second viewer the image ec_cpw_2000pan was also added from the image fusion folder.  The goal of this part was to pan sharpen a 30 meter image by using the 15 meter image to pan sharpen it.
To pan sharpen the image these steps were taken: Click on Raster>Pan Sharpen> followed by Resolution Merge to open up a window to be filled out.  In the high resolution input file ec_cpw_200pan.img was added.  In the Multispectral Input File ec_cpw_2000 was added and the output file was saved as ec_cpw_2000ps.img.  Also multiplicative was chosen as the the method and nearest neighbor was used under re sampling techniques.  These steps can be seen in figure 4 below:

Figure4 : Resolution Merge window showing the necessary steps to pan sharpen an image
Image taken from Dr. Cyril Wilson 

After the correct steps were taken OK was clicked to run the resolution merge model and the new image was brought in to ERDAS IMAGINE. The new image can be seen below in the results section.

Part 3: Simple radiometric enhancement techniques 

In this section radiometric enhancement techniques were used to enhance image spectral and radiometric quality.   The image eau_claire_2007.img was opened from the radiometric folder.  Raster>Radiometric> and Haze Reduction were clicked to open the haze reduction window seen in figure 5 below. 

Figure 5: This window should appear after the correct steps are followed seen above.

The input file should holds the image eau_claire_2007.img and the output file was saved as ec_2007_haze_r.img, all the default values were kept and the window was run by clicking okay.  The new image was brought into ERDAS IMAGINE and the results can be seen below.

Part 4: Linking image viewer to Google Earth

In part 4 a newer technique was used to compare images taken from satellites and Google Earth iimages from GeoEye high resolution satellite, which are very recent.

The image eau_claire_2011 was opened from the subset folder.  Google Earth>Connect to Google Earth were selected to open up Google Earth.  Then Google Earth was opened and moved to the second monitor like the figure below.  Next both the ERDAS IMAGINE and Google Earth were synced so both could be viewed at the same extend by clicking on Link GE to view and Sync GE to view.  

Figure 6: ERDAS IMAGINE and Google Earth matched at the same extent on different viewers.  

After both images are zoomed to the same extend by using the zoom tools (green + arrow on the home screen) they can be compared for analysis.  The results of the two images can be seen below in the results section.

Part 5: Resampling

In part 5 re sampling was done to increase the size of the pixels, changing the image.  The image eau_claire_2011 was brought into ERDAS IMAGINE from the re sampling folder.  To chage pixel size these steps were taken: click on Raster> Re sample Pixel Size which opened a window show below. 

Figure 7: Re sample window 

Next the output image was saved as eau_claire_nn.img and nearest neighbor was chosen under the re sample method.  The output cell size was changed to 20 x 20 from 30 x 30 in the X and Y Cell.  Then Okay was clicked to run the re sample technique.  After the process is done it was repeated again accept only changing the re sample method from nearest neighbor to bilinear interpolation.  This is just a different technique used to show the differences between the two.  After the two images are were added on to ERDAS IMAGINE they were compared for results seen below.

Results

Part 1

: 

Figure 8: The end result of creating an area of interest around Eau Claire and Chippewa Falls.  

Figure 9: The end result of finding the area of interested using the shapefile provided
by Dr. Cyril Wilson

Part 2: 

Figure 10:  The new image of Eau Claire and Chippewa Falls after image resolution

    The new image created is much clearer and has a higher spatial resolution than the input reflective image.  When zooming in to the city of Eau Claire, it is easier to view objects as they are clearer in the new pan sharpened image.  

Part 3:

Figure 11: The new image created after enhancing the resolution

     The new image has a different color from the old one as it appears redder and less pink.  The new image also appears to have a higher spatial resolution than the old but not by much.  Also, the bodies of water in the new image are much darker and black than the old one, the bodies of water also appear smoother than the old image. 

Part 4: 

1   Google Earth’s spatial resolution is very high compared to the Eau Claire 2011 image.  It is very easy to see rivers, trees, buildings and see the difference between objects.  I would argue that Google Earth is one of the best viewers to use when doing image interpretation.  

Part 5

Figure 11: Zoomed in comparison between nearest neighbor and bilinear Interpolation

The image on the right seems to have a less pixels making the image hard to read compared to the bilinear method.  The biniear method is much clearer and easier to interpret figures when zoomed in.

Sources

Lab created by Dr. Cyril Wilson, a geography professor at the University of Wisonsin Eau Claire.  Images taken from Dr. Cyril Wilson's Remote Sensing of the Environment Class.