Lab 7: Photogrammetry

Goals and Background

The main goal of this laboratory exercise is to practice key photogrammetric tasks on aerial photographs and satellite images. Specifically the lab is designed to better understanding of the math behind the calculation of photographic scales, the measurement of areas and perimeters of features, and calculating relief displacement. Furthermore, we were introduced to the important processes of stereoscopy and orthorectification on satellite images. The output of this lab was to be a 3D image using an elevation model. 

Methods

Scales, measurements and relief displacement

For an exercise on Stereoscopy, I calculated the scale of an aerial image using the photo distance. I also compared two images and found examples of relief displacement in one of them and took measurements of areas of features on aerial photographs.

Stereoscopy

In this part of the lab, I used one form of ground control points (GCP) to show a 3D perspective of the City of Eau Claire.  I viewed two images: a digital elevation model (DEM) of the City of Eau Claire at 10 meter spatial resolution and an image of the City of Eau Claire at 1 meter spatial resolution. Using Anaglyph Generation under the Terrain tab in ERDAS, I generated an anaglyph from the two images and then viewed it in ERDAS wearing polaroid glasses provided in the campus geography lab.

Next I opened an image of the City of Eau Claire and adjacent jurisdictions at 1 meter spatial resolution called “eau_claire_quad.img” and also a LiDAR-derived digital surface model (DSM) of the City of Eau Claire at 2 meter spatial resolution. I made an anaglyph image from them using the Anaglyph Generation like in the previous step.  Comparing the two revealed that the latter method, using an anaglyph image made with LiDAR DSM instead of a DEM, provides much better 3D results. 

Orthorectification

This section of the lab was designed to introduce us to Erdas Imagine Lecia Photogrammetric Suite (LPS), which is used in digital photogrammetry to complete many functions, such as triangulation, orthorectification, extraction of digital surface and elevation models, and much more. We uses LPS to orthorectify images and in the process create a planimetrically true orthoimage.  

We used data that covered part of Palm Springs, California.

We used already orthorectified images as a source for ground control measurements. The first, XS_ortho.img is a SPOT image, and the second, NAPP_2m-ortho.img is an aerial photo. Using these images I built a block consisting of two images with 10-meter resolution.

Tasks For Orthorectification: 

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

Earlier in the process I created a new project using SPOT satellite images of Palm

Springs, California.

Figure 1 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.

I then collected reference coordinates by selecting points in the reference image that correspond to points in the block image, spot_pan. I collected GCPs and identify their X and Y coordinates. 

The reference coordinates were selected in xs_ortho, the reference image that corresponds to points in the block image, spot_pan.

 I added two points manually, then used the the Automatic (x,y) Drive tool to allow LPS Project Manager to approximate the position of a GCP in the block image file, spot_pan based on the position in the reference image, xs_ortho, which made the process much more efficient.

I set the elevation information using data from the digital elevation model (DEM) file labeled palm_springs_dem.img using the Vertical Reference Source. I then set the Type and Usage for each of the control points, added a second image to the block file and collected tie points. 

Figure 2 Using the formula option in the Column Options menu, I changed the Type on all twelve points to “Full” and the Usage to “Control.”

I then added SPOT_panb as a frame and added GCPs based on those I had already collected in

spot_pan.

Figure All of the GCPs from SPOT_pan have been added to SPOT_panb.

Automatic tie point collection, triangulation and ortho resample

Now I completed the orthorectification process of the two images in the block, spot_pan and spot_panb through several processes. I collected tie points to measure the image coordinate positions of GCPs appearing on the overlapping area of the two SPOT images. I checked a sample of three of the tie points and found them sufficiently accurate.

Figure This is the photogrammetry manager now shows with the GCPs being present on both pan images.

With all the control and tie points, I initiated triangulation to establish the mathematical relationship between the images that make the block file, the sensor model, and the ground.

Figure This report summarizes the results of the triangulation function. 

Figure Notice that the Ext. columns in the cell array in the IMAGINE Photogrammetry Project Manager are now green. This indicates that the exterior orientation information has been supplied.

Results

In my newly orthorectified images, relief displacement and other geometric errors were sufficiently removed and accuracy was significantly improved. The orthorectified images display the photographed objects in their real-world X and Y positions and their coordinates are reliable for navigation and mapping.

Figure The final image shows the finished product; the orthorectified image is seamless.

Data Sources

Orthorectification lab was modified from Erdas Imagine LPS user guide developed for version 8.7 (2005).

The data used in this lab was provided by Dr. Cyril Wilson and collected from the following sources: NAIP in 2005 (image of Eau Claire county), aerial images of Palm Springs, California.