Remote Sensing Lab 6

Goal
One form of image preprocessing is geometric correction. This technique is performed to correct any distortions present in a satellite image so extraction of biophysical and sociocultural information can be extracted.

Methodology
There are two types of geometric correction: Image-to-Map rectification and Image-to-Image rectification.

Image-to-Map Rectification
To correct a Landsat TM image of the Chicago Metropolitan Statistical Area, a USGS 7.5 minute digital raster graphic was used to collect ground control points (GCPs).  These GCPs were used to rectify the Landsat TM image.  This was done using the Set Geometric Model tool in ERDAS Imagine 2013.  A first order polynomial equation was used to correct the distorted image.  This requires a minimum of 3 GCPs, but best practice calls for at least 4 GCPs.

The Set Geometric Model tool opens the Multipoint Geometric Correction window.  In this window, the GCPs are collected and evaluated.  When the window opens, the GCP Tool Reference Setup requires the user to input which image to collect GCPs from.  For this technique, Image Layer (New Viewer) was chosen.  The GCPs will be collected from USGS 7.5 minute digital raster graphic.

As shown in the figure below, four GCPs were collected for image rectification (Figure 1).  The quality of rectificiation is measured by RMS Error (root mean square error).  It is the difference between the distance of the input location of a GCP and the retransformed location for the same GCP in the output rectified image.  The closer the RMS error is to 0, the more accurate.  The RMS of this rectificationwas 1.6 which can be seen in the lower right hand corner of figure 1.

Figure 1: 4 GCPs collected for geometric correction

Image-to-Image Rectification
The second type of image rectification is Image-to-Image rectification.  The same process is followed as the previous technique, but this time, GCPs are collected from a reference image, not a map.  In this lab, a third order polynomial was used for the image-to-image rectification.  This changed the minimum number of control points from 3 to 10.  Just like the previous process, GCPs were collected for the reference an input image; 12 total GCP's were collected for this rectification technique (Figure 2).

Figure 2: 12 GCPs collected for Image-to-Image rectification

Once the RMS error is at an acceptable level, the image can be geometrically corrected.  This is done using the Display Resample Image Dialog tool.  For the purposes of this lab, all parameters of the tool were left as the default.  The second rectified image was resampled using bilinear interpolation.  Bilinear interpolation was used because it is a more spatially accurate technique that uses brightness values of the 4 closest input values in a 2 by 2 window to calculate the output value of the pixels.  When performing geometric correction it is best to use the most spatially accurate technique because that is the goal of the transformation.