Photogrammetry

Laboratory Exercise 7

Goals and Objectives

            This lab exercise opens students to a variety of photogrammetric techniques, and introduces the mathematics behind relief displacement, and the measurement of features within the image. These skills can be harnessed to perform more advanced techniques like the stereoscopic technique of orthorectification of images. These types of skills and background knowledge are becoming increasingly more desirable in a professional setting, so understanding the basics behind the mathematics of relief displacement create a solid foundation on which to build more advanced skills required in today’s job market.

Methods

The beginning section of the laboratory exercise asks students to calculate the amount of relief displacement required for an image of the city of Eau Claire. The relief displacement is calculated in relation to the principle point of the image. Depending on whether the object is positioned above (in elevation), or below the principle point, the object will be displaced in an upwards or downwards fashion. Combing knowledge learned in lecture, along a few mathematical formulas, students manually calculate how much the specific object needed to be rectified.

The next portion of the lab exercise introduces students to the stereoscopic technique of making an anaglyph, and how to appropriately use elevation models to make a pseudo-3D image. Students produced two different anaglyphs of the same image and compared the output for effectiveness. The first anaglyph was created using a DEM (Digital Surface Model), and an anaglyph function within ERDAS imaging. The output of this function was barely noticeable 3D effect to the human eye, but did add some distinction to the elevation change of the image. The second anaglyph made, used a 2m LiDAR DSM (digital surface model). This output was considerable better than the previous anaglyph, and added a noticeable 3D effect when visualizing the elevation using 3d glasses. The 2m resolution provided much more information for the function to work more effectively, but added the computation time of the function. The DSM anaglyph output product is an effective way to visualized and gain a better aspect of the elevation in the image, and where the most elevation change is located.

The Last portion of this lab deals with orthorectifing an image with a reference image. The process of orthorectifing produces an image which has the image perspective effects removed. Processing images by orthorectification removes the tilt and terrain relief to produce a planimetrically corrected image. The process of orthorectification is quite extensive, and involves many different types of connecting points between the image being rectified and the reference image. Essentially, an image photogrammetry program is used in ERDAS to collect ground control points (GCP’s) , both control and tie points on the images. Most importantly, is the collection of points between the two images being rectified. Connecting the images with multiples of points, allows the computer software to produce an output with very limited perspectival anomalies, which produces a seamless and functionally usable image.

Conclusions

Remote sensing is an ever evolving, complex subject, which requires a multitude of skills. Gaining knowledge into the specifics of important techniques like creating an anaglyph, and orthorectified images further separates successful remote sensing professionals from unsuccessful. This lab provided the background information, for students to apply the material learned in lecture, and applied it to very real, and important techniques in the production of usable and applicable images. 

Multipoint Geometric Correction

          Goals and Learning Objectives

This particular lab was designed to introduce students to an important aspect of preprocessing remotely sensed images. The technique of geometric correction is commonly used before any information is extracted from the image to ensure the quality and accuracy of the image obtained.

            Methods

The preprocessing technique is multipoint geometric correction is used to resample an image which contains geometric errors. To correct for these errors, a reference image is with a known scale, coordinate system, and units. In ERDAS, the Geometric Correction tool is used, and for the first portion of the lab an image-to-map rectification process is conducted. A USGS DRG (digital raster graphic) is used to Collect GCP (ground control points) to rectify the TM image. Ground control points link one spatial location with the same area on the image that needs correcting. Once the reference image and the distorted images are imported into ERDAS Geometric correcting window, a polynomial function is used to correct the image. For the image of Chicago, a first order polynomial is used. This means we must only collect 3 pairs of GCP for an accurate rectification, a fourth point is added to complete the formula and the display resample image tool used to perform the task. Before the image can be fully corrected, a quality check of the RMSE is performed to ensure the correction is accurate. For each application there is an acceptable value for rmse, in this case the RMSE is less then 2 (total).

 

          Image to Image Rectification

This second portion of the lab uses a very similar process to correct the distorted image. Excapt this time a 3^rd order polynomial function is used, which means that (9-12) pairs of points much be collected. Once the collection of the GCP’s is completed, and the RMSE is <1 total, then the Display Resample Image tool is used. This tool uses bilinear interpolation to execute the geometric output. Bilinear interpolation is a more computationally expensive process, but leads to a much more cohesive, smooth output.

Figure 2: Displaying parts of Eastern Sierra Leone, left image contains a variety of geometric distortions. The right image is the reference image used.

Conclusions

This lab introduced students to two very important preprocessing techniques. While collecting GCP can be very time consuming and tedious, the results speak for themselves.  A quality, geometrically proportional image is produced which can be used for a multitude of projects. Often times, images have some level of distortion, and it is the mappers job to correct for these errors so the output map is of the highest production value possible.