Lab 7: Photogrammetry and Orthorectification

                         

Goal:

  This lab was an introduction to photogrammetry skills that can aid in the analysis of aerial photographs and satellite images. Techniques used to complete this lab were calculating scales from various aerial photographs, calculating relief displacement, and digitizing features within the Erdas viewer in order to calculate their areas. Also, Lecia Photogrammetric Suite was used in order to create a planimetrically-true orhtoimage.

Part 1: Scales, Measurement, and Relief Displacement:

Section 1: Calculating Scale of Nearly Vertical Aerial Photographs:

The following equation was used to calculate the scale of Eau Claire_West-se.jpg, shown in figure 1.

 

S = [pd/gd]   

Where:

S = Scale

Pd = Photo Distance = 3”

Gd = Ground Distance= 8822.47’ = 105869.64”                       [1]

Using equation 1 and multiplying the denominator by 3, the calculations should be as follows:

 

     9.44564E-6 ≈ 1/105869.64  

So the scale for this map after rounding is 1:110000.

Next, focal length and altitude was used to calculate the scale of ec_east-sw.img:

                     

                         S = [f/(H-h)]                                                                                                 

                         Where:

                         f= focal length of lens = 152mm 5.9843”

                         H= altitude above sea level = 20000’ = 240000”

                          h= elevation of terrain=796’ = 9552”                                 [2]

                   Using equation 2 and multiplying the denominator by 5.9843, the calculation will

                   be as follows:

                    4.33937E-6 ≈ 1/230448

          So the scale of this map after rounding is 1: 230000.

 

Figure 1

Figure 1 shows an example of how Eau Claire_West-se.jpg should look.

 

 Section 2: Measurement of Areas of Features on Aerial Photographs:

The lagoon in ec_west-se.img (figure 2; marked with an ‘x’) was digitized and measured using the polygon tool in Erdas Imagine to calculate its area. Following is a list of the results.

Hectares ≈37.99 hectares

Acres ≈ 93.87 acres

Meters ≈ 379888m²

Miles ≈ 0.1467 miles²

 

Figure 2

Figure 2 shows the lagoon that is to be measured in section 2 of this lab.

 

Section 3: Calculating Relief Displacement From Object Height:

 

The relief displacement of the smokestack in relief_displacement_1.jpg  was calculated using the following equation (map scale is 1: 3209):
 

d=[hr/H]

 
Where:

d= relief displacement ≈ 0.2457

h= height of object (real world) = (0.375”)x(3209”)=1203.375”

r= radial distance from the top of the displaced object from principal point (photo)=9.75”

H= height of camera above local datum = 47760”                                                                               [3]

Figure 3 shows what Relief displacement_1.jpg should look like once it is opened.

Figure 3

Figure 3 shows relief_displacement_1.jpg. However, the calculations using equation three must be performed when the above file is displayed in an appropriate photo viewer and not in this figure.

 

Part 2: Stereoscopy:

In this section of the lab, a digital elevation map (DEM) was used to create an anaglyph. This was done in order to show elevation changes in the Eau Claire area. The results had a stair-stepped appearance where elevation change was abrupt. This effect likely had to do with the differences in spatial resolution between the two images (i.e. 10m on the DEM and 1m on the city image it was merged with).

Also, a second image was created where the only change was made to the vertical exaggeration to see how this would affect the SS effect. When this was done the SS effect occurred in different locations on the second image as compared to the first.

Part 3: Orthorectification:

The goal of this section of the lab was to:

1)      Create a project using Erdas Imagine Lecia Photogrammetric Suite.

2)      Select a horizontal reference source.

3)      Collect ground control points (GCP) in the first image.

4)      Add a second image to the block-file.

5)      Collect GCPs in the second image.

6)      Perform automatic tie-point collection.

7)      Triangulate the images.

8)      Orthorectify the images

9)      View the orhtoimages.

10)  Save the block-file.

Methods:

1: Create a new project:

a)      Two images, spot_pan.img and xs_ortho.img, were used as the initial input images.

b)      A model was set up using Polynomial-based Pushbroom as its Geometric Model Category.

c)      The UTM (Zone 11, North) projection was used in the Block-Property set up, with Spheroid: Clarke 1886 and Datum: NAD27 (CONUS).

2: Add imagery to the block and define sensor model:

This section of the lab determines what the internal coordinate system will be. That is how the camera was oriented when it captured the image. Nothing was changed in this lab.

3: Collect GCPs (x, y, and z) of the first block file spot_pan.img:

a)      xs_ortho.img was the image that

b)      Measurement tool was Classic Point.

c)      A total of 11 GCPs (x and y) were spatially synchronized on the spot_pan.img and spot_panb.img.

d)      The DEM image palm_springs_dem.img was used to obtain z-coordinates for the orthorectified image. This was done to provide a z axis (elevation) on the final image.

 

4: Set type and usage: These were set to full and control, respectively. This concluded the collection process for spot_pan.img.

5: Add a 2^nd (spot_panb.img) image to the block (spot_panb.img) & collect its GCPs:

Collection of GCPs in this section was identical to those collected in the previous steps.

 

6: Automatic tie-point collection:

Tie-points will be collected in this section of the lab in order measure image coordinate system at overlapping regions of the spot_pan.img and spot_panb.imgs.

a)      General tie-point properties were set as follows: Images: All; Initial Type: Exterior/Header/GCP.

b)      Distribution: Intended Number of Points per Image was set at 40.

7: Triangulation:

Triangulation was performed using the following parameters:

a)      Iterations With Relaxation: 3.

b)      Image Coordinate Units for Report: Pixels.

c)      Type of Point: Same Weighted Value.

d)      x,y, and z: 15.00000.

e)      Simple Gross Error Check Using: 3 times of unit weight.

8: Ortho-resampling:

a)      DEM Source: palm_springs_dem.img

b)      Output Cell Sizes (x and y): 10.

c)      Resample Method: Bilinear Interpolation.

 

Output Images:

Shown below in figure 4a, the overall spatial overlap at the boundaries of the orthorectified images (orthospot_panb.img overlaying orthospot_pan.img) is accurate. However, when zooming in close some sections of the boundaries are not matched perfectly between the images. For instance, figure 4b shows the ‘Swipe’ view of panb overlaying pan at an extent of about 1:11000 in the Erdas viewer. Notice that at the boundary between the two images the river features do not match up.

  Since this effect does not occur throughout the image, it may have to do with the accuracy of individual reference points. For instance, if a reference point was slightly inaccurate on either input image or the DEM, then when resampling occurred that inaccuracy might be transferred to the output image(s) once Ortho-resampling was applied.

 

Figure 4

a)

b)

Figure 4a shows the two orthorectified images, orthospot_pan and orthospot_panb in the Erdas viewer; panb is overlaying pan in this case. Figure 4b details the square in 1a and shows some inaccuracy of the spatial overlap between the two images (circle).