Lab 8

Introduction

The main goal of this lab is to gain experience on the measurement and interpretation of spectral reflectance signatures of various Earth surface materials captured by satellite images.  This lab will teach how to collect spectral signatures, graph them, and conclude on what the curves on the graphs mean.  The graphs will show reluctance on the y axis and the band number of the x axis. 

Methods

12 surface materials form the image eau_claire_2000 were measured and plotted the spectral reflectance 

1. Standing Water  2. Moving Water  3. Vegetation  4. Riparian vegetation  5. Crops  6. Urban Grass  7. Dry Soil  8. Moist Soil  9. Rock  10. Asphalt highway  11. Airport highway  12. Concrete surface. 

First Erdas Imagine was opened and the image eau_claire_2000 was opened and I zoomed into the Eau Claire county and Chippewa area.  Then home and drawing shown by arrows A and B below to activate area of interest tools. 

Figure 1: A- Drawing   B-Polygon tool

First to collect standing water a polygon was drawn in the middle of Lake Wissota, this was done by clicking somewhere inside Lake Wissota and drawing any shape of polygon and double clicking to finish the circle.  After the polygon is complete then next step is to click Raster followed by Supervised and Signature Editor shown by arrows A and B below. 

Figure 2:  A- Supervised   B-Signature Editor

This will open the signature editor shown by figure 3 below.  To bring in the polygon just drawn, crate new signature from AOI, arrow A, was clicked and the name was changed to Standing water.  To show the spectral curve/graph, the display mean plot window, arrow B, to open up a graph like figure 4 below. 

Figure 3: Signature Editor.  A- create new signature from AOI
B- display mean plot window. 

Figure 4: Signature Mean Plot: the results I got from collecting a
polygon from Lake Wissota. 

The next step is to collect and draw polygons on the surface materials 2-12 listed above.  When collecting the surface materials I brought up Google Earth to help me determine what features are what, because it is very difficult to see where farms and vegetation exist.  To display all materials 1-12 on one signature mean plot you can click, arrow B, shown in figure 5 below.  Arrow A will scale chart to fit current signatures, which was used often to fit the curve on the graph. 

Figure 5:  A- scale chart to fit current signatures
B- show all signature featurs

One final step to help see the colors is to change the background of the graph, this can be done by clicking: edit>chart options>Plot background.  I recommend changing the background to white.  Also changing materials 1-12 to different colors is important,  this can be done by clicking the color on figure 3. and choosing a color that is open. 

Results

Specify the spectral channel (bands) in micrometer for the highest and lowest reflectance for

the standing water signature.
     Band 1= highest,   Bands 4 and 6 = Lowest

Why did standing water demonstrate the highest and lowest reflectance at the spectral channel (bands) you specified in Q.1 above?

   Standing Water doesn't absorb band 1 well and also 2 and 3 (blue, green, red), therefore it deflects it making the color contain a lot of blue.  While on the other hand reflectance is almost non-existent in the NIR, 4,5, and 6.

Specify the spectral channel (band) in micrometer for the highest and lowest reflectance for 

signatures 2 through 12.

 Sig 2- high 1, low 6 

Sig 3- high 4, low 3 & 6

Sig 4- high 1, low 6

Sig 5- high 5, low 4

Sig 6- high 4, low 3

Sig 7- high 5, low 4

Sig 8- high 1, low 6

Sig 9- high 4, low 3

Sig 10- high 5, low 4

Sig 11- high 5, low 4

Sig 12- high 1, low 4

All graphs below are the results I obtained after drawing polygons of each surface material.

Why did vegetation display the highest and lowest reflectance at the spectral channels you 

specified in Q3 above? 

The reason band 4 is the highest is because bands 1-3 are absorbed by the chlorophyll for photosynthesis.   Also plants deflect NIR, 4-6, to avoid destruction of protein cells. 

     At which spectral channel (band) does dry and moist soil vary the most? Explain reasons 

     for the differences.

Wet soil has a low reflectance in band 1 vs dry soil having very high reflectance in band 1.  Overall dry soil has a higher reflectance than wet soil, it is hard to see by looking at the graph but when looking at the numbers it easy to see the difference.  This is because dry things reflect more than wet things and wet things absorb more than dry things causing the difference. 

Above is the final product with all 12 surface materials on one mean plot.

Describe spectral signatures that are most similar across the spectral channels and those that 

greatly differ. Make sure you identify the surface features you are talking about. In your 

response, outline reasons for similarities and differences of these spectral signatures across the 

spectral channels.

 Both Standing and Moving water follow very similar paths if not exact.  Riparian vegetation and wet soil follow very similar paths.  Crops and Dry Soil also follow very similar paths.  Airport, highway, and parking lot all follow similar paths with medium reflectance.  There seems to be a difference in wet vs dry, wet being low reflectance and dry being high reflectance.  As I stated before this is because dry features reflect more than moist features.  

      If you are asked to develop a sensor that collects data for the identification of most of the 

      above surfaces, which specific spectral channels would you use and why? 

When developing a sensor that collects data for types of vegetation I would use bands 1-4 because chlorophyll uses bands 1-3 for energy in the form of photosynthesis so we would see low reflect and high absorption.  As for bands 4, plants reflect this to protect damage to proteins.

As for water I would use bands 1-3 because it has a high reflectance compared to the rest of the bands causing most of the water to appear blue.  

Sources

All images were provided by Dr. Cyril Wilson of the Geography Department at the University of Wisconsin Eau-Claire. 

Lab 7

Introduction

The goal of this lab is to develop skills in performing photogrammetric tasks on aerial photographs and satellite images.   Specifically the lab is designed to train and understand the mathematics behind the calculation of photographic scales, measurement of areas and perimeters, and calculating relief displacement. 

Methods 

Part 1: Scales, measurements and relief displacement. 

From the image above the task was to create a scale of the photograph by measuring the distance between point A and B.  The real world distance is 8822.47 feet and by measuring the distance with a ruler was 2.25 inches or .1875 ft.  From here both measurements were divided by .1875 feet to give the scale of 1/47,053.17.  

The second part of the question also has to do with scale.  This time with the aerial photograph, ec_east-sw.img.  The photograph was acquired at a height of 20,000 feet above sea level with a camera focal length lens of 152 mm.  The elevation of Eau Claire County is 796 feet.   Using an equation Scale = f/ H-h the answer can be seen in the results tab below. 

Section 2: Measurement of areas on aerial photographs 

In Erdas Imagine open the image ec_west-se.  Click measure>show panel>point drop down arrow> and polygon tool.  Then the figure near x was digitized to find the area. To complete the polygon double click on the last point to complete the circle.  For this part we want the area in hectares and acres, it can be switched near the top right below manage data.  

The same steps were taken to find the perimeter of the feature but instead of choosing polygon, polyline was chosen.  For this step we want the perimeter in meters and miles. 

The figure below is an example of digitizing the lake to give either the area or perimeter. 

Section 3: Calculating relief displacement from object height. 

Using the image ec_west-se the relief displacement of the smoke stack, A, was determined from the image below.  The scale of the image is 1:3209 and the height of aerial photograph is 3980 feet.  The smoke stack measured at .4 inches and it is 8.7 inches away from the principal point.  Real word of smoke stack =1283.6 inches.  Equation then 1283.6 x 8.7/47, 760 inches.  3980 feet = 47,760 inches 

Part 2: Stereoscopy 

In Erdas Imagine open the image ec_city and in a second view bring in the image ec_dem2.  Then click the Terrain-Anaglyph button to open the Anaglyph geration window show below.  

Anaglyph Generation Window 

For Input DEM, input ec_dem2 and input image use ec_city.  Then click on the folder for output image and name it ec_anaglyph.  Increase the vertical exaggeration to 2  and all the other factors should be left at default.  Dismiss the run and bring in the new image for results, which can be seen below.

Part 3: Orthorectification 

The final result should give you something similar to the image below,

Results

Part 1: Scales, measurements and relief displacement. 

   1.      A-B= 2.25 inches, =.1875 ft.  .1875/8,822.47=

1/47,053.17   ft.

    2.      152mm/20,000-796 ft.  152mm = 15.2 cm.  15.2cm=5.98 in.  5.98in = .498 ft.

.498/19,204 ft = 1/38,562.249  ft. 

Section 2: Measurement of areas on aerial photographs 

a.  38.83 hectares   95.97 acres

b. 4,122.07 meters   2.56 miles

Section 3: Calculating relief displacement from object height. 

        0.4 inches of smoke stack,  8.7 inches to principal point.  .4 x 3209 for scale = 1283.6 inches.  Equation = 1283.6 x 8.7/ 47,760 inches.  Relief Displacement = 0.23 inches.

Because the object is above the datum and leaning outwards, the objects and features must be plotted inwards. 

Part 2: Stereoscopy 

Describe the elevation of features in Eau Claire.

The elevation features in Eau Claire appear very flat, probably because Eau Claire does not have much elevation change.  You can easily see elevation change around the Chippewa River and some lakes.  It is hard to see elevation change in the city. 

How different are these features from reality? 

 The features to me, do not appear to differ in elevation as much as they would in reality.  When the image appears in the same color it is hard to tell a difference in elevation especially in the city where it appears grey.  I cannot see a big difference in elevation near “the hill” on campus or on State Street. 

What factor(s) might be responsible for differences between what you observe in the city and what you are now seeing in the anaglyph image?

One factor is that the image on the computer screen is hard to view in 3D, but in real life everything is 3D.   Also the scale of the image and spatial resolution do not contribute to the easiness to view the images elevation change.

Part 3: Orthorectification 

Comment on the degree of accuracy of spatial overlap at the boundaries of the two 

orthorectified images.

 The spatial accuracy of the overlap between the two images is very clean.  The only thing that stands in the way of the two images is the black line that separates between the images.  When zooming in it is the cleanest line between two images I have ever seen in during this class. 

Sources

All images were provided by Dr. Cyril Wilson of the geography department at the University of Wisconsin Eau-Claire.  The course is title Geogrpahy 338: Remote Sensing of the Environment 

Lab 6

Introduction

The goal of this lab was to introduce students a image pre-processing tool called geometric correction.  The lab was designed to develop skills on the two major types of geometric correction, image-to-map rectification and image to image registration.

Methods

Part 1

In part one a United States Geological Survey (USGS) 7.5 minute digital raster graphic image of the Chicago Metropolitan Statistical Area was used to correct a Landsat TM image of the same Chicago area.  This was done by collecting ground control points from the USGS image to rectify the TM image.  

In ERDAS IMAGINE, the images Chicago_drg and Chicago_2000 were opened in two separate viewers and fit to frame.  With the image Chicage_2000 highlighted, next Multisprecrtal>Control Points were hit to open up a window titled, Set Geometric Model.  Under "Select Geometric Model" Polynomial was chosen and ok was clicked. 

Figure 1: GCP Tool Reference Setup

Next the window seen if figure 1 above and the default was accepted and OK was clicked.  Then I navigated to the folder containing, Chicago_drg, and it was added. OK was clicked on the reference Map Information dialog.  This will bring up the Polynomial Model Properties window and the default settings were accepted by clicking close.  Now in the Multipoint Geometric Correction window the two Chicago images appeared, figure 2, and the geometric correction was performed.

Figure 2: Multipoint Geometric Correction window containing the two
Chicago images. 

First the GCP's were deleted to to start the process at its first step.  This was done by holding down the shift key and clicking each of the GCPs. Once they were all selected a right clicked was performed and delete selection was clicked.  This should delete all the GCPs except one with no X or Y coordinates.

Both images were right clicked and fit image to window was chosen to correctly fit the image in the frame.  Now the create GCP too, figure 3, was chosen to create GCP points.  

Figure 3: Create GCP tool.

A total of four ground control points were added to image in the same spot in each image to rectify the image.  Each time the GCP buttom was clicked to at a GCP in the same spot to each image looking like figure 4 below.  Under the control center the color purple was chosen simply by clicking the empty space for each GCP. 

Figure 4: The two images with 4 GCP's each in the same location to rectify the image. 

An important component of placing the GCPs in the same position in each image is the RMS error.  Total RMS error can be found in the lower right of the screen and for part one an RMS error under 2 is required.  To reduce RMS error I zoomed way in to each GCP and choose a landmark or coastline to match each GCP with.  It took a while to play around with and obtain a total RMS error under 2 but it was done.  

Figure 5: example showing total RMS error. 

Once the RMS error was under 2 the display re sample image dialog button was chosen and a new name was chosen for the output image.  The function was run and brought into ERDAS IMAGINE.  The results can be seen in the results section below.

Part 2

The directions as part one except this time the correction will be image to image with Sierra Leone images.. 

To do a image to image function the same directions were followed from part 1 except a few changes.  This time 12 GCPs were choose because polynomial order 3 was performed.  While adding 12 GCP's throughout the map a RMS error under 1 was desired.  Comparing this to part 1 it was much tougher because the RMS was lower and their was 12 points.

Figure 6: The 12 GCPs were set in place similar to the image above. 

After the 12 GCPs were added and a RMS below 1 was achieved the display re sample image dialog button was clicked and an output name was chosen.  In the re sample method option it was changed from nearest neighbor to bilinear interpolation because it called for better spatial accuracy.  The model was run and the output image was brought into ERDAS IMAGINE for results. 

Results

Part 1

Figure 7: Rectified Chicago area image. 

Figure 8: Total RMS error of 1.54

I found the image to map conversion to be more difficult and not as clean as the image to image correction.  The map made it really difficult to find the correct location of GCPs to lower the RMS error.  Figure 9 below was very accurate in terms of features and elements in the image.  Image to map corrects more of a city landscape I believe compared to image to image correcting a landscape.


Part 2

Figure 9: Image to Image registration of Sierra Leone

Figure 10: Total RMS error of .2514

How geometrically correct is your rectified image? In other words, how spatially accurate is 

it in relation to the reference image you used? 

    The new image is very geometrically correct. Even though there is a lot of cloud cover compared to the old images when zooming in on the new image land forms and shapes are much clearer and easier to interpret compared to the old images.  The old image was much distorted and after correcting it the landforms are much clearer because they were fixed to be connected together in the correct position. 

Sources

All images and directions were provided by our professor, Dr. Cyril Wilson who teaches in the Geography Department at the University of Wisconsin Eau-Claire.  The class is Geography 338: Remote Sensing of the Environment. 

Lab 5

Introduction

The goal of this lab was to introduce some analytical process: image mosaic, spatial and spectral image enhancement, band ratio, and binary change detection.  All these processes involve changing or converting an image to fit the needs of a study area.  Image mosaic is to teach how to process individual scenes of satellite images to arrive at one seamless scene.  At the end of the lab the goal was to be able to understand and use all of the analytical processes in real life situations or projects. 

Methods

In the methods sections I will explain the processes I took to arrive at an end result.  All process were done in ERDAS IMAGINE and one part in ArcMap.  Images and directions were provided by Dr. Cyril Wilson of the Geography Department and University of Wisconsin Eau-Claire. 

Part 1: Image Mosaicking

Opened Erdas Imagine and navigated to the image eau_claire_1995p26r29.   In the select layers to add multiple was clicked followed by multiple images in virtual mosaic, shown by figure 1 below.  

Figure 1: Multiple Window: Choosing Multple Images in Virtual Mosaic

Then also in the Select Layers to Add, Raster Options was chosen followed by choosing Background Transparent and Fit to Frame.  Then OK was hit to bring in the image.   The same exact process was done for the image eau_claire_1995p25r29 and the end result should be seen below in figure 2.

Figure 2: Both images brought in ready to mosaic them into one seamless tile. 

Next to mosoaic the images these steps were taken: Raster>Mosaic>Mosaic Express.  In the Mosaic image both images were brought in by clicking the folder in the middle of the page.  Eau_claire_1995p25r29 was brought in first because we want that image on top of the other.  Then next was clicked until the output tab was reached.  A new name was made and placed in a folder to be brought it.  Finished was clicked and the image was brought in to Erdas Imagine which you can see in the results tab below.

Section 2: Image mosaic with the use of MosaicPro

In this section a mosaic is going to be done with the same two images but this time an advanced mosaic tool called, MosaicPro was used to transform the image.

Again both images were added and adjusting the correct 'select layers to add' (see part 1) was done.  Then clicking on Mosaic>MosaicPro to bring up the MosaicPro window.  In this window the add images icon was clicked shown by arrow A below.  Next the image eau_claire_1995p25r29 was highlighted an the Image Area Options was edited.  Then Compute Active Area was clicked shown by arrow B below.

Figure 3: Image taken from Dr. Wilson

After hitting Ok the same steps were done to bring in the second image and two boxes appeared, see figure 3 above.   Next the select tool was hit, arrow B, to change the stack order of the images.  With the select tool click on the first image in, shown by arrow D, then send selected image to bottom, 3rd arrow C, to send the image to the bottom.  Repeat the process above to repeat the same process with the other image.  In the end the image eau_claire_1995p25r29 ended up on the bottom.

Figure 4; MosaicPro Image taken from Dr. Wilson

Next Color corrections was clicked, arrow E figure 4, to open the color corrections window.  Then Use Histogram Matching, arrow A figure 5, fill the box followed by set>and select overlap areas, arrow c figure 5 then OK to close it.  

Figure 5: Color Corrections and Histogram Matching, image
taken from Dr. Wilson.

To run the technique click process followed by Run Mosaic, the new image was named and saved in the correct folder and was brought into ERDAS IMAGINE.  The new image can be seen in the results section below.

Part 2: Band Rationing
In this part a band ratio will be performed by implementing the normalized difference vegetation index (NDVI) on eau_claire_2011 image.  That image was brought into ERDAS IMAGINE followed by raster>unsupervised>NDVI.  A new window appeared and filled out correctly, the sensor read 'Landsat TM' and under the select function NDVI was highlighted.  OK was clicked and the process was run.  The result can be seen below in the results section.

Part 3: Spatial and spectral Image enhancement
In part 3 different spatial enhancement techniques were used to transform images.

The image chicago_tm1995_b3 was brought in.  Under Raster>Spatial>Convulation a window was brought up like figure 6, below.

Figure 6: Convolution window.

The correct input and output file was set and the Kernel was set to 5x5 low pass.  The image was then compared side by side with the original image which can be seen below in the results. 

Next using the process and steps except with the sierra_leone2002 image and changing the kernel to 5x5 high pass.  The image again was brought into ERDAS IMAGINE to find results.  

This time using the edge enhancement tool the image sierra_leone1991 was used.  Using convolution again and 3x3 laplacian edge detection and checking Fill under handle edges by.  After clicking OK image was brought up to be compared to the old image.  

Section 2: Spectral enhancement

In this section linear contrast stretched was performed to improve the visual appearance of the images.  

The image eau_claire1991b3 was brought into and the histogram was analyzed.  Then pancromatic>General Contrast> General Contast to open up a new window.  Method was changed to Gaussain and apply was clicked to changed the image.  

Next, adding the second image eau_claire1991b5 was added.  Under Panchromatic>General Contrast>Piecewise Contrast was chosen.  The window and the range specifications were changed exactly to figure 7 below,

Figure 7: Piecewise Contrast Window
Image from Dr. Wilson 

This was then applied to the image and screenshot was captured, seen below in the results section.

Next a histogram equalization was performed on the image 15026029_0292011b30, the red band of Landsat TM of Eau Claire.   Raster>Radiometric>Histogram Equalization was clicked to open up a new window.  The input and output were set correctly and default values were accepted.  The function was run and a screenshot of the new image was captured to be seen in the results section.  

Part 4: Binary change detection

In part 4 calculations  were made to estimate the brightness values of pixels that changed from Eau Claire County from 1991 to 2011.  

2 images were brought in, ec_envs1991 and ec_envs2011.   Raster>Functions>Two Image functions was clicked to access two input operators window.  In input one image 1991 was entered and input 2 2011 was entered.  A new output name was created.  Under operator the + was changed to - and the layer file under both inputs was changed from All to Layer 4.   OK was clicked and the new image was screenshoted for results. 

Section 2: Mapping change pixels in difference image using spatial modeler.  

In this section a map was created that showed the differences between 1991 and 2011.  

The model maker toolbox was opened under Toolbox>Model Maker> Model Maker.

A model was created using the functions shown by figure 8 below. 

Figure 8: Arrows showing the different functions of model maker.

A= Selection tool, B= raster object C=function D=arrow E=where to place the items F=run the model

Figure 9: The model was set up like this, 2 rasters on top to
a function and then the output raster. 

ec_envs_2011_b4 was placed in the first raster and ec_envs_1991b4 was placed in the second.

In the function the first raster was subtracted by the second and 127 was added at the end.  A new output file was created for the final raster and the model was run.

 Then new image was then brought into ERDAS IMAGINE and calculations were made.  The standard deviation was times by 3 and added to the mean to give us the threshold number for a number of 202.18.  

Model maker was opened again with just two rasters and one function.  The new image that was just created was entered into the first raster.  In the function window analysis was changed to conditional and Either IF Or was chosen.   The equation- EITHER 1 IF ($n1_ec_91>202.18) OR 0 OTHERIWISE  as entered into the box.  An new output name was created and the image was opened.  

The two images, ec_envs_1991b4 and ec_91-11bvis were brought into ArcMap to give us a result, seen in the results tab below. 

Results

Part 1: Image Mosaicking

Figure 10: eau_claire1995msx

Describe the nature of color in your output image, in other words, is there a smooth color 

transition between one image and the other especially at the boundaries? 

1.      No, there is not a clean/ smooth color transition between the images.  The image on top is much darker in terms of red than the image below, it is easy to see the difference between the two.  

Section 2: Image mosaic with the use of MosaicPro

Figure 11: Eau Claire 1995msp

Compare the output mosaic image using the MosaicPro and that obtained earlier using the 

Mosaic Express. In your discussion, state the reason(s) for the differences in the image quality 

[hint: open two viewers and display each of the output images in a separate window] 

 The MosiacPro does a much better job of smoothing the colors together.  The edges of the pictures go together much smoother than the Mosaic Express technique.  If I were combing images I would use the MosaicPro technique.  This is because MosaicPro combines the two histograms to match the brightness of the images making it much harder to tell the difference in colors.  

Part 2: Band Rationing

Figure 12: Eau Claie 2000NDVI

What will you expect to find in areas that are very white in the NDVI image?

-Healthy Vegetation

Comment on the presence or absence of vegetation in areas that are medium gray and black. 

-It seems there is not much vegetation in areas around big cities, which makes sense since there are not very many agricultural fields near cites.   For example areas around the Twin Cities and Eau Claire are very dark compared to the rest of the image.  

Part 3: Spatial and spectral Image enhancement

Figure 13: Chicago 1995 5x5 Low

Outline the differences between the original image and the 5x5 Low Pass filtered image you 

just created.

-    The new image created is less bright, however, the new image has a lower spatial resolution and appears very blurry when zooming in.   Although, the high frequency of the image has improved making the image easier to interpret. 

Figure 14: Sierra 2002 5x5 High

Outline the differences between the original image and the 5x5 High Pass filtered image 

you just created. 

Although, the new image at first appeared very dark when zooming in there was a difference in brightness with the old image.  The old image is very gray all over, while the new image is black and white in places of rivers and city lights. 

Figure 15: Sierra 1991 Edge

Outline the differences between the original image and the Laplacian edge detection image 

you just created.

The old image is contrasted between to colors, red and green.  The old image appears not to use “real colors”.  The new image is much darker than the old and sort of blended the red and green together to make one color.  When zooming in the image cannot be interpreted at all making it not very useful because each pixel is a different color. 

Part 4: Binary change detection

Figure 16: EC 91_11

Section 2: Mapping change pixels in difference image using spatial modeler.  

Figure 17: Final product in Arc Map

Describe the spatial distribution of areas that changed over the 20 year period. Are these 

areas close to urban centers or not? 

The changes do not appear close to urban centers.  However, it depends on how you define ‘close’.  There does not appear to be any change close to Eau Claire, the biggest city in the area.  The changes to me seem to be in agricultural fields and close to a lot of water.  The changes do not have any pattern and are scattered throughout the area. 

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.