Remote Sensing Lab 7: Photogrammetry

Introduction: In this lab, we learned how to utilize certain photogrammetry techniques on aerial images. Specifically, we learned how understand the math behind analyzing features on images (i.e. relief displacement). We also learned the basis behind stereoscopy and orthorectification.

Methods:For the first part of the lab, we calculated statistics like Map Scale, measurement of areas on aerial photographs, and relief displacement, to give us a solid background on the math behind analyzing aerial images. For Map Scale, we were given two points on an aerial image, then given the equation s = (pd/gd), where pd is the distance between the two on the photo, and gd is the real world distance. We also utilized a different equation, where focal length of camera lens and flying height was utilized to calculate the scale. That equation is S = (f/(H-h)), where F is the focal lens length, H is the altitude above sea levele, and h is the elevation of terrain.

In measurement of areas and perimeters, we utilized the 'Measure Perimeters and Areas' digitizing tool in Erdas. And for relief displacement, we used the equation d = ((h*r/H), where h is the height of the object in the real world, r is the radical distance, H is the height of the camera above the datum, we were able to calculate relief displacement.

In the second part of the lab, we worked with producing stereoscopic images. Stereoscopic images is another way to say "3D" images, and we created two different 3D images to analyze. One image contained relief displacement, while the other did not. The two images were then used to create respective anaglyph images. The image with relief displacement used a separate DEM to create the image, while one without relief displacement utilized a DSM.

In the last part of the lab, we learned the process of orthorectifiying images. Orthorectification is the process of removing image perspective (i.e. tilt) and terrain for the purpose of creating a "correct" image. For this part, we took two separate images of the same area, one a reference image and the other an image set for orthorectification, then added control points to each image so they matched up. With the two images, we added 12 total control points. We then got rid of the reference image after all the control points were added to the orthorectification image, then added an overlapping third image so we could further enhance our orthorectification. With the two images set for orthorectification, we ran the model in ERDAS after adding all the control points, and produced an image with a high degree of accuracy in its spatial overlap.

Results:

Part 1

For the first  problems, we calculated Map Scale on an image. The image contained an A and a B point. With our measurements, we obtained:

2.7 inches for distance between point A and B on photo.
8822.7 feet for distance between point A and B in the Real World
2.7in / (8822.7ft*12in) 1:39,212.

Rounding up, the answer is 1:40,000.

With the second image, we were given a problem to calculate Map Scale using focal length and flying height. With our measurments, we obtained:

Focal Length = 152 mm
Altitude = 20,000 ft
Elevation = 796

152 mm/(20,000-796 ft) = 152 mm/19,204 ft = 152 mm / 5,853,379.2 mm. Scale = 1:38,509

Rounding up, the answer is 1:40,000

For calculating the area and perimeter of a given feature, we utilized Erdas 'Measure perimeters and areas' tool, and digitized an area around a lake on an image. With the digitizing, we obtained measurements of:

Area: 93.3549 acres, 37.7794 hectares
Perimeter 4071.37 meters, 2.5298 miles

In calculating relief displacement, we calculated the displacement on a smoke stack, and obtained these measurements:

Height of Object (real world) = 100.28 ft
Radical Distance = .479 ft.
Height of aerial camera = 3980 ft.

100.28 ft.  Radical Distance = .479 ft. (100.28-.479/3980) = .300 inches

Part 2

In part 2, we created two stereoscopic images to analyze. With the first image, we created the Anaglyph image using a DEM of Eau Claire and a section of Eau Claire with relief displacement on the image, Figure 1. 

Figure 1: Anagylph Image of Eau Claire

With the second image, we created a Anaglyph image of Eau Claire using a DSM derived from LiDAR, located in Figure 2. 

Figure 2: Anaglyph Image of Eau Claire using LiDAR 

Although it can't be seen on the blog, in ERDAS when comparting the two images, Figure 2 is a more an accurate 3D representation than Figure 1. Figure 1 contains relief displacement, which create inaccuracies within the image (i.e. there's a valley in Eau Claire with a gradual decline, however in Figure 1 it makes it look like a steep cliff).

Part 3

For this part, we orthorectified images to create a planimetrically correct image, as exhibited in Figure 3. 

Figure 3: Orthorectified Images

In analyzing the spatial overlap, the rivers and the road boundaries match up really well on all the boundaries I’ve seen. There are some discrepancies on the southeastern portion of the boundaries, where the roads and rivers don’t quite match up. However, on the western portion of the boundary, the roads and rivers match up quite well.

Conclusion: Photogrammetry and their techniques are important in the realm of remote sensing, because they help correct images into their planimetrically correct form. 

Sources: 
National Agriculture Imagery Program (NAIP) images are from United States Department of Agriculture, 2005.  
Digital Elevation Model (DEM) for Eau Claire, WI is from United States Department of Agriculture Natural Resources Conservation Service, 2010. 
Lidar-derived surface model (DSM) for sections of Eau Claire and Chippewa are from Eau Claire County and Chippewa County governments respectively.
Spot satellite images are from Erdas Imagine, 2009. 
Digital elevation model (DEM) for Palm Spring, CA is from Erdas Imagine, 2009. 
National Aerial Photography Program (NAPP) 2 meter images are from Erdas Imagine, 2009.