Photogrammetry

Lab 7

 

 

 

Introduction

 

Photogrammetry is the science of making accurate measurements from aerial photographs and satellite images and in the process obtain geometrically reliable products. The goal of this lab was to develop skills in performing photogrammetric tasks on aerial photographs and satellite images. What will be keyed on in this lab is the mathematics behind the calculation of photographic scales, measurement of areas and perimeters of features, and calculating relief displacement.

 

This lab deals with the introduction of stereoscopy and performing orthorectification on satellite images. All in all this lab is really meant to put us in the position to perform diverse photogrammetric tasks.

 

 

Methods

 

 

Part 1

 

There are two tasks located in this first section. The first deals with calculating scale of nearly vertical aerial photographs. In this section we were to open up the image Eau Claire_West-se.jpg. In this image there are two points, labeled A and B. The task was to calculate the scale of this aerial photograph by using a ruler and measuring the distance on the computer screen. The only information that was given was the actual ground distance between the two points is 8822.47 ft. The image that was used for this section can be seen below in Figure 1.

 

 

Figure 1: This is the image that was used to complete section 1. The points A and B are located in the Northwestern portion of the image.
Image created by: Cyril Wilson

 

 

The second task in this section also deals with scale. Now the image ec_east-sw.img will be used. This image was taken by the National Agriculture Imagery Program (NAIP) in 2005. This aircraft acquired the photograph at an altitude of 20,000 ft. above sea level with a camera that had a focal length lens of 152 mm. One other bit of information given was that Eau Claire County is 796 ft. in elevation. The task is to use this information to figure out the scale. Looking at the image for this task isn't really necessary, it is more mathematical based.

 

 

 

 

Section 2

 

This section deals with measurement of areas of features on aerial photographs. This begins by displaying the image ec_west-se.img in an ERDAS viewer. The idea behind this section is to trace the feature located in Figure 2 and then look at the measurement window to obtain the area and perimeter of the feature.

 

 

Figure 2: This is the image that contains the feature that is to be traced for this section. The red X in the western portion of the image is the lagoon that will be measured.
Image created by: Cyril Wilson

 

 

These are the instructions as to obtaining the measurements.

• Click the 'Measure' icon. This will change the tool panel, shown below.
• On the tool panel, click on show Panel (arrow A). This will open a View Measurements table at the bottom of the image.
• Click a Point drop down arrow (arrow B) to show various measurement tools.
• Click on the Polygon tool (arrow C) to measure the area.
• Use that tool carefully to digitize the outline of the lagoon in the photo above. The area can be read directly from the Measurement window. Click the drop down default hectares to change to other units of measuring area and perimeter.
• Click on the Polyline tool (arrow D) to measure perimeter. Follow the same process as measuring the area.

 

Figure 3: This is an image of the tools that are needed to complete this section
Image created by: Cyril Wilson

 

 

 

Section 3

 

 

This section deals with calculating relief displacement from object height. The image used for this portion is ec_west-se.img, which can be seen below. The information given for this section is as follows; the height of the aerial camera above datum is 3,980 ft., the scale of the image is 1:3,209. Then a hint was given (hint: using a ruler to measure the height of the smoke stack and find its real world height; then measure the radial distance between the principal point and the top of the smoke stack). With data the relief displacement could then be calculated.

 

 

Figure 4: This is the image that was used to complete this section. The principal point can be seen in the top, left-hand portion of the image, while the smoke stack is marked by the red X.
Image created by: Cyril Wilson

 

 

Part 2

 

This part of the lab deals with stereoscopy. To begin, the image ec_city.img needs to be brought into a viewer in ERDAS. Then open a second viewer and bring in the image ec_dem2.img. Then click on Terrain - Anaglyph to open the Anaglyph Generation window. In the Input DEM, input ec_dem2.img. For the Input image, input ec_city.img. In the Output image, insert the image ec_anaglyph.img located in the Steroscopy_output folder. Increase the vertical exaggeration to 2. Then click OK to run the model.

 

 

Figure 5: This is the Anaglyph Generation window where the above operations are completed.
Image created by: Cyril Wilson

 

 

The final step is to use polaroid glasses to observe the image.

 

 

 

Part 3

 

This part deals with orthorectification. Erdas Imagine Lecia Photogrammetric Suite (LPS) will now be introduced. It is used in digital photogrammetry for triangulation, orthorectification of images collected by various sensors, extraction of digital surface and elevation models.

 

Tasks

• Create a new project.
• Select a horizontal reference source.
• Collect GCPs.
• Ass a second image to the block file.
• Collect GCPs in the second image.
• Perform sutomatic tie point collection.
• Triangulate the images.
• Orthorectify the images.
• View the orthoimages.
• Save the block file.

 

 

Section 1

 

The task of creating a new project is what section 1 deals with. Open up ERDAS and open the LPS Project Manager tool. Click the Create New Block File to open the Create New Block File dialog. Then navigate to the subfolder that was created for this lab and name the block file Sat_ortho, then click OK. The Model Setup dialog is now open. This is shown below in Figure 6.

 

Figure 6: This is a screenshot of the window that was opened in the previous steps.
Image created by: Cyril Wilson

 

Click on the Geometric Model Category and choose Polynomial-based Pushbroom. In the Geometric Model Category select SPOT Pushbroom, then click OK.

 

The Block Property Setup dialog is now open. Click Set in the Horizontal Reference Coordinate System. The Projection Chooser dialog is now open. In the Custom tab click Projection Type and choose UTM. Click the Spheroid Name and select Clarke 1866. Click the Datum Name and select NAD27(CONUS). Click UTM Zone field and type 11. Then click OK. Click OK on the Block Setup dialog to close it.

 

 

Section 2

 

This section deals with adding imagery to the Block and define the sensor model. In the Block Project Tree View click the Images folder. Then click the Add Frame icon, the Image File Name dialog is now open. Add the image Spot_pan.img, then click OK.

 

Next you will verify the parameters of the SPOT pushbroom sensor. Click the Show and Edit Frame Properties icon. Click Edit, the sensor information dialog is now displayed. Click OK and then click OK again.

 

 

 

Section 3

 

This section deals with activating the Point Measurement tool and collecting GCPs. Start by clicking the Start Point Measurement Tool and select the Classic Point Measurement Tool, then click OK. The Point Measurement tool opens displaying three views, a Main View, and two Cell Arrays.

Arrow A is the Main view, arrow B is the Tool Palette, arrow C is the Detail View, arrow D is Reference Cell Array, and arrow E is the File Cell Array. These are all shown in Figure 7 below.

 

Figure 7: This is a screenshot of the different views, tools, and arrays that will be used to complete this section of the lab.
Image created by: Cyril Wilson

 

 

In the Point Measurement tool palette, click the Reset horizontal reference source icon (F). The GCP Reference Source dialog is now open. In this check the Image Layer radio button. After clicking OK on the GCP Reference Source dialog, the Reference Image Layer dialog will now be open. Input the image xs_ortho.img. Click OK.

In the Point Measurement tool, click the checkbox next to Use Viewer As Reference (G). The Point Measurement tool automatically changes to display the reference image xs_ortho, in the left view of the point measurement tool, and the original image, spot_pan, in the right view. This can be seen below in Figure 8.

Figure 8: This is a screenshot of the two views that contain the images used in this part of the lab. The xs_ortho.img is on the left and the spot_pan.img is on the right.
Image created by: Cyril Wilson

You will collect reference coordinates by selecting points in xs_ortho, the reference image that correspond to points in the block image, spot_pan. The following is the process of collecting these GCPs with helpful images.

Move the inquire box in the top left viewer showing the full extent of the xs_ortho image to where point ID 1 is (Shown in the Figure below). Click the Add button (A) in the Point Measurement tool. A row for Point ID 1 is added to the reference Cell Array. Move the inquire box in the lower left viewer containing the second view of the xs_ortho image, the detailed view also moves. Do this until you find the road intersectionin the detailed view, shown below.

Figure 9: This is a screenshot for the completion of the directions that are listed for this section.
Image created by: Cyril Wilson

Click the Create Point icon (B). Go to the Detail view on the xs_ortho image and click the road intersection, shown in green. Paint ID 1 is added to all three viewers containing the xs_ortho image. Check the reference control point coordinated; they should almost match those in the table below.

If the coordinates do not approximate (within 10 meters) of those listed in the table above, type in the correct coordinates from the table in the reference cell array, then press enter on the keyboard.

You will now collect the corresponding point on the block image, spot_pan.img. Check figure 1 for the location of Point ID 1 on the spot_pan image display in the right view of the point measurement tool. Move the inquire box in the views containing the spot_pan image until you see the road intersection. Click the Create Point icon and then click on the intersection. Check your spot_pan control point coordinates; they should approximate those in the table below.

 

 

 If your coordinates do not approximate (within two pixels) those listed in the table above, then type in the correct coordinates in the file cell array, then press enter in your keyboard.

 

This process will be the same process that will be followed for GCPs 2-9. The last two control points, Point ID 11 and Point ID 12 will be collected from a different horizontal reference source than the previous one. For the last two GCPs, you will use NAAP_2m-ortho.img. This is an orthoimage at a scale of 1:40,000 with a spatial resolution of 2 meters.

Click the Reset Horizontal Reference Source icon in the Point Measurement tool palette. This opens the GCP Reference Source dialog. Then check the Image Layer radio button. Click OK, opening the Reference Image Layer dialog. Input the image NAAP_2m-ortho.img. Click OK and the NAAP_2m-ortho.img is automatically loaded to the left viewer, shown below in Figure 10. To make the distinction between two different horizontal surfaces (xs_ortho and NAAP_2m-ortho) clearer, skip ID Point 10, and name the next control point Point ID 11.

Figure 10: This is a screenshot of the above process, showing the new images in the left-hand viewer.
Image created by: Cyril Wilson

The process for collecting the ID Points for 11 and 12 will be the same process that was used for points 1-9. When done, click Save and deselect the Use Viewer As Reference checkbox in the Point Measurement tool palette. Now only the spot_pan image will be shown.

You will not proceed to set the Vertical Reference Source and collect elevation information for all the horizontal reference GCPs you obtained from both images. The image palm_springs_dem.img will be used to collect the elevation information.

In the Point Measurement tool palette, click the Reset Vertical Reference Source icon. The Vertical Reference Source dialog then opens. Click the DEM radio button. Click the dropdown arrow and select Find DEM from the list. In the Add DEM File Name select the DEM palm_springs_dem.img., then click OK in the File Selector.

The DEM file selected displays in the DEM section of the Vertical Reference Source dialog. Click OK. Now right-click on the Point # column and Select All. Click the Update Z Values on Selected Points icon. The Z values of all reference points are updated in the reference cell array based on the values in the DEM selected. This can be seen in Figure 11 below.

Figure 11: This is a screenshot showing the newly acquired Z-values to display elevation for each of the control points.
Image created by: Cyril Wilson

Section 4

This section deals with set type and usage, with also adding a 2nd image to the block and collecting its GCPs. Left-click the title bar of the column titled Type to select the entire column. Right-click on Type to access the Column Options menu, then select Formula. The Formula dialog is not opened, shown below.

Figure 12: This is a screenshot of the Formula dialog that will be used for this assignment.
Image created by: Cyril Wilson

In the Formula section type Full, then click Apply. Each Type entry now changes to indicate Full (X,Y, and Z coordinates). Repeat the above steps to update the Usage column to Control. The cell array should look exactly like the one below.

Figure 13: This is a screenshot that shows the updated columns in the cell array.
Image created by: Cyril Wilson

In the Point Measurement tool, click Save. Then close the Point Management tool, you will be returned to LPS Project Manager.

 

Now that you have successfully finished the collection of reference coordinates from the xs_ortho and NAPP_2m-ortho, and file coordinated in the first block file, spot_pan, you can more on to the second image in the block, spot_panb.img.

 

Click the Add Frame icon and add the image spot_panb.img. Now click Row #2 to highlight the row corresponding to Image ID 2, spot_panb. Click Frame Properties icon. Click OK to accept original perimeters.

 

Click the Point measurement icon, select the Classic Point Measurement Tool, and click OK. Spot_panb will be displayed on the left while the initial image in the block spot_pan on the right. Points will be collected for Point ID #1, 2, 6, 8, 9, 12. This is done the exact same way that it was done in section 2. The reason some of the numbers are missing is because they are not located on spot_panb. When you're done click Save. The block interface should look like the image below in Figure 14.

 

 

Figure 14: This is a screenshot of what your Block Interface should look like after completing the steps above.
Image created by: Cyril Wilson

 

 

 

Section 5

 

This section deals with automatic tie points collection, triangulation, and ortho resampling. Begin by clicking the Automatic Tie Point Generation Properties. Image used radio button should be set to All Available. Initial Type radio button should be set to Exterior/Header/GCP. Check and confirm that the Image Layer Used for Computation is set to 1. Click the Distribution tab in the Automatic Tie Point Generation Properties dialog. In the Intended Number of Points/Image field, type in 40. Then click Run. Then click Save and close the Point Measurement tool.

 

 

In LPS Project Manager click on Edit-Triangulation Properties. Change the Iterations With Relaxation value to 3. Verify that the Image Coordinate Units for Report is set to Pixels. Click on the Point tab and in the Ground Point Type and Standard Deviations section click the Type dropdown and select Same Weighted Values. Change the values for the X, Y, and Z to 15. Then click Run. This will open a Triangulation Summary report, Figure 15.

 

 

Figure 15: This is a screenshot of what the Triangulation Summary report will look like. This is something that should be looked at and analyzed.
Image created by: Cyril Wilson

 

 

Click Accept and then OK. After returning to the LPS Project Manager click File-Save for the image that is created.

 

 

Figure 16: This is a screenshot of what is produced through completing the above instructions.
Image created by: Cyril Wilson

 

 

Now click the Start Ortho Resampling Process. In the General Tab, click the DTM Source dropdown and select DEM. Click the DEM File Name dropdown and select Find DEM. Input the image palm_springs_dem.img. Click the Output Cell Sizes and change to 10.0. Click the Add button and make sure the second image in the block file spot_panb is in the Input File Name. Check the box to Use Current Cell Sizes, then click OK. Click OK in the Ortho Resampling dialog to start the process. After the model has run click File and Save.

 

 

 

Section 6

 

This last section just deals with viewing the Orthorectified Images. Just bring both the images into the frame and save the results.

 

 

 

Results

 

 

Section 1

 

1.

2 7/8 inches

1 inch = 3,000 ft

2.875” x 3,000’ = 8,625’

8,625’ x 12” = 103,500”

Scale = 1:103,500

2.

Altitude = 20,000 ft.        elevation = 796 ft.            focal length lens = 152 mm. or 0.5 ft.

Scale = f / H – h

Scale = 0.5 / (20,000 – 796)

Scale = (0.5 / 19,204) x 2 =

Scale = 1:38,400

 

Section 2

 

3(a).

37.69 ha, 93.13 acres

3(b).

4175.79 m, 2.59 miles

 

Section 3

 

4.

D = h x r / H

Smoke stack = 0.5 inches              scale = 1:3,209                   H = 3,980 ft.

3,209” x 0.5” = 1,604.5” / 12 = 133.7 ft.

Real world height = 133.7 ft.

Radial distance = 9.5” / 12 = 0.79’

D = (133.7 x 0.79) / 3,980

D = 0.027 x 12 = 0.32 inches

The feature must be plotted inwards 0.32 inches

 

 

Section 4

 

5.

From observing the anaglyph, it is apparent that there are massive flood plains that stretch landward from the river. The elevation for these land areas is fairly similar to that of the river. The area of lower campus is located on that flood plain, and is the student housing area. The student housing area, by Water Street, has a more decreasing elevation as it reaches the river than that of the lower campus area. The difference between the elevations of upper and lower campus are very apparent. The area where Carson Park is located, being surrounded by Half Moon Lake, is at a higher elevation than I would have expected. It is more elevated than both of the previous areas that were talked about. The land located near the southern part of the river in this image is at an extremely low elevation, almost matching that of the river itself.

6.

I believe what I have described is very accurate to that of reality. I’m not really sure about the southern portion of the river, but I do know that the area of land located near the river on the student housing side near Water Street experiences flooding every year in the spring. The sidewalks are closed off because of this flooding. This is explained by the intense decrease in elevation as you get closer to the river. There are some differences, but they aren’t very noticeable because of the lack of flooding in this part of the state.

7.

Like I said in the paragraph prior, this portion of the state doesn’t exactly see much flooding. So in the anaglyph, it might show lower elevations near the river that represent an outwash plain. But in reality we never get that much flooding, so these elevations patterns can be seen as a misconception. In the anaglyph you may think an area would receive a lot of flooding because of the low elevation, but because of the portion of the state we live in this is untrue.

It’s amazing how in the original city image of Eau Claire the elevation differences are almost non-apparent, whereas the anaglyph image the elevation differences jump off the page at you, literally.

 

 

Section 6

 

8.

I’m actually astounded at the accuracy of the spatial overlap between these two images. I zoomed way in to the overlap area and there is seemingly no difference; roads and rivers connect perfectly from one image to another. When you zoom way in the obvious overlap of pixels is apparent, but at a further distance out the overlap is unnoticeable. When you Fit To Frame the overlap area is featured as a black line though, and when you are zoomed in there is a slight difference in shading between the two images. But everything matches up otherwise.

 

 

Geometric Correction

Lab 6

 

 

 

Introduction

 

This week in lab we focused on the idea of Geometric Correction. Geometric correction is the removal of geometric distortion in an imagery so that individual pixels can be in their proper planimetric (x,y) position. This lab was structured to develop your skills on the two major types of geometric correction that are normally performed on satellite images as part of the preprocessing activities prior to the extraction of biophysical and sociocultural information from satellite images.

 

 

 

Methods

 

 

Part 1

 

First open up ERDAS Imagine with a viewer. In this viewer bring in the image Chicago_drg.img. Then open a second viewer and bring in the image Chicago_2000.img. Click Multispectral and then on Control Points. This process can be seen below in Figure 1.

 

 

Figure 1: This is a screenshot of the process of selecting the Control Points tab.
Image created by: Cyril Wilson

 

 

 

Now the Set Geometric Model window will be open, under Select Geometric Model select Polynomial and then hit Ok. Another window will pop up, accept the default. Then navigate to the Lab 6 folder and add your reference DRG image - Chicago_drg.img. Then click Ok on the Reference Map Information.

 

The Multipoint Geometric Correction window will open up two panes. On the left is the input image (Chicago_2000.img), while the reference image is on the right pane (Chicago_drg.img). Each of these panes contains three windows. The top left and top right panes show the entire input and reference images respectively. The other two central top panes shows the areas that are zoomed into on the input image and also that zoomed into the reference image.

 

Now starts the process of adding GCPs to the images. But first the default GCPs need to be deleted. Once this is done the task is to add four pairs of GCPs on the images. Even though only three are needed, extra can't hurt and will give a better fit.

 

Begin by clicking on the Create GCP tool at the top of the page. Then add the four GCP points in the locations exactly like shown in Figure 2.

 

 

Figure 2: This is a screenshot of the instructed areas as to where we were supposed to place our sets of GCP points.
Image created by: Cyril Wilson

 

 

Now is the process of making sure you're doing a good job by evaluating your GCPs. This is done by looking at the Root Mean Square (RMS) error. This is shown at the bottom of the page. For this lab the goal is to reach a RMS of below 2.0. This is done by repositioning the GCPs in their correct placed on both images. After all of the RMS values are below 2.0 now comes the process of creating the corrected image begins.

 

 

Begin this process by clicking on the Display Resample Image Dialog. Name the output file Chicago_2000gcr.img and place it into your Lab 6 folder. click ok and then bring the resampled image into your ERDAS viewer.

 

 

 

Part 2

 

This section is essential going to be the same as the first part, with a few changes. The image that will be located in the first viewer will be sierra_leone_east1991.img and Sierra_Leone_east1991grf.img in the second viewer. Click on Multispectral and Control Points again. Click on Polynomial under Select Geometric Model, then click Ok. Set your reference image as Sierra_Leone_east1991grf.img, and click Ok. On Polynomial Model Properties change the polynomial order to 3 and click close.

 

Now begins the process of adding the GCPs again, but this time 12 are going to be added, even though only ten are needed. The GCPs will be located in the exact same location as of those seem in Figure 3.

 

 

 

Figure 3: This is a screenshot of where the GCPs should be located for this section of the lab.
Image created by: Cyril Wilson

 

 

Next click on the Display Resample Image Dialog on the Multipoint Geometric Correction toolbar. Name the output file sl_east_gcc.img. Change the resampling method to bilinear interpolation and click Ok. Now bring the resampled image into the ERDAS viewer.

 

 

 

Results

 

For the results for each section I either can to upload an image or answer a question or two. I will show you my created images and answered questions for each section below.

 

 

Part 1

 

 

 

 

 

 

 

Q1: The Chicago_drg.img image was the image used in an image-to-map rectification. The image data pixel coordinates were rectified/transformed using the map coordinate counterparts. The rectification process involves converting a data file coordinate to some other grid and coordinate system known as a reference system. So this particular image was used as a reference to transform the map.

Q2: The resampling method used above is called spatial interpolation. Spatial interpolation is the use of GCP pairs to establish a geometric coordinate transformation that is applied to rectify the location of pixels in the output image with a value from a pixel in the unrectified input image.

Q3: GCPs must be dispersed throughout the image to achieve a reliable geometric correction.

Q4: A first-order polynomial equation/model is used to fit a plane to the data. This is the fitting of polynomial equations to the GCP data using least-squares criteria to model the corrections directly in the same image domain.

Q5: The minimum number of GCPs needed for a first order polynomial would be 3.

 

 

Part 2

 

 

Q6: UTM (Zone 29)

Q7: 10 GCPs

Q8: Because in part 1 only 3 GCPs were necessary, in this section 10 GCPs are necessary. Currently there are only 9 GCPs, so it will display Model has no solution

 

 

 

 

 

 

 

 

 

Q9: The new image has a greater geometrical correctness. Even though there is still cloud cover in the image it is still easier to identify features on the new image. The old image was much more distorted and had a lot more cloud cover.

Q10: Bilinear interpolation is more spatially accurate than nearest neighbor. This image was in more need of greater spatial accuracy than the image in part one, that is why bilinear was used instead of nearest neighbor.

 

 

 

 

 

 

 

 



Image Mosaic and Miscellaneous Image Functions 2

Lab 5

 

 

 

Introduction

 

The idea behind this lab is the introduction of some important analytical processes in the remote sensing field. This lab explores image mosaic, spatial and spectral image enhancement, band ratio, and binary change detection. The image mosaic part of the lab is structured to teach how to process individual scenes of satellite images to create one seamless scene. This is needed when faced with a project that covers a large geographic area that exceeds the spatial extent of one satellite image scene, or the study area astride portions of two satellite scenes.

 

By the end of this lab everyone will be in a position to apply all the analytical processes introduced in this lab to real life projects.

 

 

Methods

 

Part 1 has all to do with Image Mosaicking. Image mosaicking is done when the study area is larger than the spatial extent of one satellite image scene, or the area of interest is relatively small but traverses the portion intersected by two adjacent satellite scenes.

For the first part of this section the image eau_claire_1995p26r28.img was brought into the ERDAS viewer. But before the image could be added there were some specific functions that needed to be performed. First by clicking on the Multiple tab and then on Select Layers To Add, and then final on the Multiple Images in Virtual Mosaic button. Next click on Raster Options and click on the Select Layers To Add. In this window make sure that Background Transparent in checked along with Fit To Frame, then hit OK.

This process was then to be repeated for the second image, eau_claire_1995p25r29.img. Then both images would be brought into the same viewer, one overlapping the other.

Section 1

Finally after all this prep work Section one can really begin. Section 1 deals with Image Mosaic with the use of Mosaic Express. First click on the Raster tab to activate the tools. Then click on the Mosaic tab and then on Mosaic Express. This Process can be seen below in Figure 1.

Figure 1: This is a screenshot of the process of selecting the Mosaic Express tool.
Image created by: Cyril Wilson

Next, in the Mosaic Express Window, click on the folder icon and add the images to be mosaicked. This process can be seen below in Figure 2.

Figure 2: This is a screenshot of the process of adding the images that are to be mosaicked.
Image created by: Cyril Wilson

Adding the eau_claire_1995p25r29.img image before the eau_claire_1995p26r29.img image will put it on top, which is what is desired for this lab. Navigate to the Output Dialog and create a folder called Mosaic_Output, this is where the image will be saved. Click on the folder next to Root Name and name the output file eau_claire1995msx.img. Then click Finish to run the model.

Section 2

This section has to deal with Image Mosaic with the use of MosaicPro. After opening the two images that were to be mosaicked before, the parameters in the Select Layers To Add tool will be the same as the section prior. First click on Mosaic and MosaicPro to open the MosaicPro window. Click on the Add Images icon. Highlight the image eau_claire_1995p25r29.img and click on the Image Area Options, then clicking on the Compute Active Area button. Click Ok. This process can be seen in Figure 3 below.

Figure 3: This is a screenshot of the process on selecting the Compute Active Area option.
Image created by: Cyril Wilson

The above steps are then repeated in adding the image eau_claire_1995p26r29.img onto the MosaicPro window.

The next task is to synchronize the radiometric properties at the area of intersection of both images so that there will be a smooth color transition from one image to the next. Click on Color Corrections, and then click on the Use Histogram Matching to activate the Set button. Select Overlap Areas, then click Ok. This process is shown below in Figure 4.

Figure 4: This is a screenshot of the above process to selecting the Overlap Areas button.
Image created by: Cyril Wilson

Initializing the image mosaic may now commence. Click on Process in the window and then Run Mosaic. Navigate to the Mosaic_Output folder and name the output image eau_claire1995msp.img.

Section 3

This section has to do with Band Ratioing. Bring in the image eau_claire_200.img. Click Raster-Unsupervised and then NDVI. The Indices interface is now open. In the input put the image eau_claire_2000.img. Name the NDVI image eau_claire2000ndvi.img. Make sure the Sensor reads "Landsat TM". Under Select Function,, make sure NDVI is highlighted. Below in Figure 5 is what the window should look like. Click Ok to create the NDVI image.

Figure 5: This is a screenshot of how the window from the above processes should look like.
Image created by: Cyril Wilson

Section 4

This next section deals with Spatial Enhancement. Bring the image Chicago_tm1995_b3.img into the ERDAS viewer. Click on Raster, Spatial, and Convolution. This will open a convolution window. This is displayed in Figure 6 below.

Figure 6: This is a screenshot of the convolution window and what it should look like.
Image created by: Cyril Wilson

Scroll down the kernel types and highlight 5x5 Low Pass. Input the image Chicago_tm1995_b3.img as the input file. Name the output file chicago_tm1995b3low.img. Then hit Ok.

Next, using the same process as above, apply a 5x5 High Pass convolution filter and name the output sierra_leone2002high.img.

The next task is going to focus on edge enhancement. Access the Convolution window and input the image sierra_leone1991.img. Under kernel type select 3x3 Laplacian Edge Detection. Check Fill under Handle Edges and uncheck Normalize The Kernel. Name the outpue sierra_leone1991edge.img and click Ok.

Section 5

This section deals with Spatial Enhancement. Bring the image eau_claire1991b3.img into the ERDAS viewer. Click on Panchromatic to activate those tools. Click on General Contrast and General Contrast again. After the Contrast Adjust interface opens click on Method and change it to Gaussian. Click apply.

This process is going to be repeated, but except for selecting General Contrast, the Piecewise Contrast option is going to be selected. Change the values to what they are in Figure 7 below.

Figure 7: This is a screenshot of the values that are to be used for the Piecewise Contrast.
Image created by: Cyril Wilson

Next a Histogram Equalization is going to be performed. Open the image I5026029_0292011b30.img. Click on Radiometric and then Histogram Equalization. Input the above image and name the output ec_2001_b3_he.img. Then press Ok.

Section 6

This section deals with Image Differencing. The first step is to display the images ec_envs1991.img and ec_envs2001.img. Click on Functions and then click on Two Image Functions to access the Two Input Operators. Input the images above in the input file. For the output name it ec_envrns_91_11.img. Click Layer and change from All to layer 4, do the same for both images. Click Ok.

Now open the Image Metadata and click the Histogram. Estimate a point by using a rule of thumb threshold of Mean + 1.5 x Standard Deviation. Write this value down. Now put the curser over the center and write that value down. Add those two values together to find the upper limit of the change/no change threshold. Do this same procedure for the lower value.

Section 7

This section deals with Mapping Change Pixels in Difference Image Using Spatial Modeler. Open Model Maker by clicking on Toolbox, and Model Maker. Create a model that looks like the one in Figure 8.

Figure 8: This is a screenshot of what the model should look like for the completion of this next section.
Image created by: Cyril Wilson

For the first raster object input the image ec_envs_2001_b4.img and for the second raster object ec_envs_1991b4.img. For the function write $n1_ec_envs_2001_b4 - $n2_ec_envs_1991_b4 + 127. Name the output ec_91-11chg_b.img. Then run the model. Bring this created image into the viewer and complete the process for finding the change/no change threshold value just like what was done above. Except multiply the standard deviation by 3 this time.

Open a new Model Maker and create a model that looks like Figure 9.

Figure 9: This is a screenshot of what the next model should look like.
Image created by: Cyril Wilson

Insert image ec_91-11chg_b.img in the input raster object. Open the function object. Change to Conditional and click on Either IF OR. The function script should look like this: EITHER 1 IF ( $n1_ec_91 > change/no change threshold value ) OR 0 OTHERWISE. Label the output image ec_91-11bvis.img. run the model and bring the image into the viewer. It should look like Figure 10.

Figure 10: This is a screenshot of what the model should have created in the process above.
Image created by: Cyril Wilson

This will then be brought into ArcMap and be set over the image ec_91-11bvis.img to show the spatial distribution of this area over the last 20 years.

Results

 

 

For the results for each section I either can to upload an image or answer a question or two. I will show you my created images and answered questions for each section below.

 

Section 1

 

 

Q1: As far as color transition goes, the eau_claire_1995p2529.img image seems to have a much more rugged color transition than that of the eau_claire_1995p26r29.img image. The second image is much smoother than the second image. Besides the second image being a smoother color transition, the color for this image is also much more vibrant. The color is much brighter, but I believe this makes viewing the image more difficult.

 

 

 

Section 2

 

Q2: Using MosaicPro, both of the images seem to have a fairly similar brightness. The deference is in the smoothness once again. The second image is again smoother and more difficult to interpret features. The differences between Mosaic Express and MosaicPro are due to user input. The MosaicPro image is much more extensive and in depth with the overall quality than that of the Mosaic Express image. This is because MosaicPro demands extensive user input to be able to smooth images, perform multiple radiometric synchronization processes in order to produce a visually pleasing and spectrally seamless image. Mosaic Express only demands the bare minimum of inputs by the user.

 

 

Section 3

Q3: In the areas of the NDVI image that are very white I would expect to find vegetation. As stated in the notes, several ratios to help discriminate against objects and features have been developed. One of those would be normalized difference vegetation index (NDVI). Because the white is so vivid and stands out I would assume that in a NDVI image that those white areas would in fact be vegetation.

Q4: While the white area does take up a lot of the image, there are in fact areas of medium gray or black. These are areas that lack vegetation. In this image there are two types of areas that lack vegetation, the first being water. The rivers in this area are black. The second would be the residential areas on the map. These residential areas are the medium gray color. This would be home to features such as buildings and roads.

Section 4

Q5: A high frequency image has significant changes in brightness values over a short distance.

Q6: The 5x5 Low Pass filtered image does in fact decrease the changes in brightness over short distance. The image looks smoother and not as choppy at the original image. The one this I would have to say seemed to be a disadvantage of the 5x5 image would be that the original was much easier to see. I synched the views and zoomed in to the Chicago area. With the original image it was much easier to view specific features. Unlike the 5x5 image where it was blurry and features were not as distinguishable.

Q7: A low frequency image has few changes in brightness values over a given area.

Q8: The 5x5 High Pass filter seems to increase the changes in brightness values over a short distance. This time the original image seems to be smoother while the 5x5 image is much more coarse and bright. I would now say the 5x5 image looks more suitable for the use of feature identification. I synched the views again the 5x5 image was much easier to look at to see what the white areas of the image were. Whether they were a river or some other feature.

Q9: A Laplacian Convolution filter approximates a second derivative between two adjacent pixels. It also sharpens the image by locally increasing the contrast at discontinuities. Finally it gives an image a natural look compared with other methods.

Q10: I’m not sure if I would consider the laplacian image as looking more “natural”. It definitely looks more rustic with its dull colors and more visible features. The image does look sharper than the original image. There are certain features that do look less visible in the enhanced image, such as crevasses and features of that nature. It is also apparent that the image was enhanced in some way, shape, or form. There is a patterned scaring that is shown on the image.

 

 

Section 5

 

 

Q11: The histogram for this image would seem to be near-Gausian because of its distribution and high peak. Mode = 0.

Q12: A minimum-maximum contrast stretch on this image is appropriate because of the low contrast of the image itself. This type of linear enhancement is also best used in Gausian or near-Gausian histograms.

 

Q13: The historgram for this image would seem to be non-Gausian because of its lack of distribution. Mode = 0.

Q14: It’s appropriate to perform a piecewise contrast stretch on this image because it is non-Gausian. This makes it perfect for a piecewise contrast stretch.

 

 

 

Q15: The Piecewise image is much coarser and shows off the features within the image better than that of the original image. I feel like if I had to choose one of the images for feature identification I would most certainly choose the piecewise image over the original image. The piecewise image is just that much more defined.

Q16: I would say it is apparent that this image is not enhanced in any way that would make feature identification more efficient. The contrast if fairly low just by looking at the image, the short, high peak on the histogram also tells me that the contrast on this image is fairly low.

Q17: It is immediately apparent that there is a far greater contrast in the second image than there is in the original image. Features are clearer and more defined. The histogram for the second image is also more spread out which hints to an increase in contrast from that of the original image that has a short, high peaked histogram.

 

 

Section 6

 

 

 

 

Section 7

 

 

 

 

Q18: The spatial distribution of areas that have changed over this 20 year period are fairly clustered together. That is because these areas are located around urban areas and near water. I believe that the reason these areas are the ones with change is because urban areas are always changing, mostly due to the great number of people that are located in these areas. If you think about it a rural area with not a lot of people isn’t really going to change that much over 20 years. But an area with lots of people is changing constantly, with new buildings and roads and whatnot.