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.