Lab 4: Miscellaneous Image Functions 1

Goal and Background
The goals of this lab were to gain an understanding of the following: subsetting an image, optimizing image resolution for the purpose of visual interpretation, radiometric enhancement, linking satellite images to Google Earth to assist in image interpretation, and resampling of satellite images. The lab was broken up into several sections, each focusing on different image functions.

Part 1: Image Subsetting

Methods
There are two different ways to subset an image: By using a rectangular box with the Inquire Box, or by creating an AOI (area of interest) using a shapefile of the area. We learned the box method first.






Figure 1 - Original satellite image

The image I used was false color satellite image of Western Wisconsin, titled eau_claire_2011.img (figure 1). The first step was to open the image up in ERDAS IMAGINE 2013. By right clicking on the image, I was able to choose 'Inquire Box' and use this box to outline the area I wanted to subset: Eau Claire and Chippewa counties.

After getting the box the appropriate size, I clicked apply in the Inquire Box Viewer to set the extents. Then, under the raster tool bar, I clicked Subset & Chip, Create Subset Image. From this menu, I was able to specify that I wanted the subset image to come from the box I created by clicking From Inquire Box. I chose to save the new image in my lab file and hit OK.

Next, we learned the alternate way to subset: creating an AOI using a shapefile. This is the strategy used most when the subset image will be used in ArcMap, as it ensures that the image is the same size as the shapefiles being used.

I loaded a shapefile of Chippewa and Eau Claire counties that our professor had prepared for us, which was overlaid onto the original satellite image that we used in the last step. I selected both counties and clicked on 'paste from selected object' on the Home tool bar. This created an 'area of interest' around my counties, and from there I went to 'Save As' - AOI Layer As, and saved it in my lab folder.

 

With the AOI saved, I could then use it as the basis for a subset image. I went to Subset & Chip on the raster tool bar as I did before, but set the AOI for use instead of the Inquire Box.

Results




Figure 2 - Subset image using Inquire Box

Figure 2 shows the image that resulted from the Inquire Box subset technique. Not only is the image cropped down to show only the area I want to use, but the image is sharper and free of haze.







Figure 3 - Subset image using AOI

Figure 3 shows the image that resulted from using an AOI for the subset range. Notice that this image is more exact because it matches the county shape files. The haze has not been reduced in this image, however, and so the first image shows better contrast.

Figure 4 - Images to be fused

Part 2: Image Fusion

Methods

In this section, we learned how to increase the spatial resolution of an image by fusing it with a higher resolution panchromatic image.

First I opened both images to be fused. Figure 4 shows the 30m spatial resolution image on the left, and the 15m spatial resolution panchromatic image on the right.

On the Raster tool bar, I clicked on the Pan Sharpen icon and selected Resolution Merge in the drop-down menu. In the Resolution Merge window, I set the panchromatic image as the High Resolution Input File and 30m resolution reflective image as the Multispectral Input File. For output file, I set it to save to my lab folder. I chose 'Multiplicative' for the Method, and 'Nearest Neighbor' for the Resampling Technique. All other options I left at default settings and ran the tool.

Results



Figure 5 - Original image (left), pan-sharpened image (right)

As you can see in figure 5, the pan-sharpened image (on the right) retains the colors of the original image, but has the higher spatial resolution of the panchromatic image. This creates an image with "the best of both worlds," with high resolution and greater contrast and allows for easier image interpretation.

 

Part 3: Simple Radiometric Enhancement Techniques

Methods

For this portion of the lab exercise, we learned to use a simple haze filter to increase radiometric resolution. We started once again with a false color satellite image of Western Wisconsin.

On the Raster tool bar, I clicked on the 'Haze Reduction' under the Radiometric drop-down menu. For input file, I set the Wisconsin image I had opened, and the output file I saved to my lab folder. All other options I left at their default settings.

Results




Figure 5 - Original image on left, haze reduction on right

The results were drastic. The enhanced image looks much more clear than the original image on the right. The colors are more pronounced, the contrast is higher, and it is much easier to decipher details.

Part 4: Linking Image Viewer to Google Earth

Method

This feature is relatively new to Erdas, added in the 2011 update. It allows for a satellite image with a defined coordinate system to be linked to Google Earth to quickly and easily see differences, and to use Google Earth as an interpretation key.

Again, we loaded a false color image of Western Wisconsin. Erdas has a dedicated Google Earth tool bar. I clicked on the Connect to Google Earth button, and the program opened up. From the same tool bar, I can link and sync the programs together, allowing me to see the same spatial extent on Google Earth as I pan and zoom in Erdas.

Results



Figure 6 - Google Earth linked to Erdas Imagine

Being linked to Google Earth can be extremely helpful for image interpretation. Google Earth utilizes the high resolution GeoEye satellite, and these images are usually pretty recent. When the two programs are linked, Google Earth can be used as a guide for picking out features. Some things may be difficult to identify in a 30m resolution false color image, but with Google Earth displaying a higher resolution image on a second monitor, it can make it much easier. An example is shown in figure 6.

Part 5: Resampling

Methods

Resampling is used to change the size of pixels. Resampling up will reduce the size of pixels, while resampling down will increase the size of pixels. Each is equally easy to employ, depending on what the job requires.

The 30m spatial resolution Western Wisconsin image was loaded and fit to frame. Using the Raster tool bar, I selected the Spatial icon and clicked 'Resample Pixel Size.' The open image defaulted to the input image and I set the output image to save in my lab folder. I chose 'Nearest Neighbor' as my method of resampling and changed the cell size from 30m to 20m. All other options were left at default.



Figure 7 - Resampling by Nearest Neighbor (left)
Resampling by Bilinear Interpolation (right)

I did the same process once again, but this time I selected 'Bilinear Interpolation' as my resampling method. This too was saved to my lab folder.

Results

In figure 7, you can see both resampling methods. On the left is an image resampled using the Nearest Neighbor method, while on the right is the Bilinear Interpolation method. Both have had a reduction in pixel size from 30m - 20m spatial resolution (resampling up), but because of the method used, they look slightly different.