Lab 4: Miscellaneous Image Functions 1

Goals and Background

This laboratory exercise is designed around the following: (1) delineating a study area from a larger satellite image scene, (2) demonstrate how spatial resolution of images can be optimized for visual interpretation purposes, (3) introduce some radiometric enhancement techniques in optical images, (4) linking a satellite image to Google Earth which can be a source of ancillary information, and, (5) introduce students to various methods of re-sampling satellite images. At the end of this lab exercise we will have gained some skills in image pre-processing, enhancing images for visual interpretation, and be in a position to delineate any study area (area of interest) from a larger satellite image scene. 

Methods

Figure 1: Create Subset&Clip

In part 1 of this 5 part lab, we looked at image sub-setting.  There are two methods of image sub-setting: an inquire box and creating an area of interest or AOI.  When creating a sub-set using the inquire box, you will make a square or rectangle around the part of the image that you want delineate from the rest of the image.  To insert the inquire box into your image right-click on your image and hit "Inquire Box".  The inquire box can be re-positioned and re-sized to the specifications that you need.  Once the inquire box is around the area of the image you desire to sub-set, you will then need to create the sub-set.  Under raster, click on subset&clip, choose create subset image, as seen in Figure 1. In output file, go to your folder and save it.  When the sub-set is created, the end result will be an image of where the inquire box was around.

Figure 2: Shape file Area of Interest

The next way to sub-set an area is by creating an area of interest or AOI.  In order to create an AOI, you will have to have a shape file of your AOI.  The one used in part 1 was of Eau Claire and Chippewa Counties, as seen in Figure 2.  Once the AOI is selected, you can clip the AOI and save it by using the sub-set tool.  A new image of the AOI is created. 

Figure 3: Pan Sharpen/Resolution Merge

In part 2, we looked at image fusion.  Image fusion is when a higher spatial resolution image is made from a coarser resolution image.  In order to create a higher spatial resolution image, a regular image and a panchromatic image are fused together.  This is called pan sharpening. We merge the two images together.  With both images open in Erdas, click on Raster, then pan sharpen, and choose resolution merge from the drop down box, as shown in Figure 3. 

Figure 4: How to save image

A window will open like in Figure 4. In the high resolution input the panchromatic image, in the multi-spectral input the original image, and in the output choose a file name.  In the method box check multiplicative and in the re-sampling techniques check nearest neighbor.  With two viewers open in ERDAS, sync the views and compare them.  When the two images are merged together you will get a pan sharpened image that is easier to depict features from, better color, and more depth of detail.

 
In part 3, we looked at enhancing the image by clearing the haze from an image.  A image with haze makes it harder to read and see detail.  With an image open in ERDAS, click on raster, then radiometric, and in the drop down choose haze reduction, as seen in figure 5.

Figure 5: Haze Reduction

  You will get a window to rename your output/new image.  Once the image is done processing and the haze is cleared up, the image will be sharper and easier to interpret.  
















In part 4 we linked an image with a Google Earth image.  This is a neat feature of ERDAS. With an image open in ERDAS you will connect to Google Earth, as shown in Figure 6.  

Figure 6: Linking to Google Earth

When the images are side by side you can sync them together and scroll up and down and zoom in and out.  Since the images are synced both images will move the same.  Google Earth is a great tool to interpret aerial photos with.  You can zoom in to a very near extent and use the image interpretation keys to help identify objects in the frame. 





In part 5, we took a look at re-sampling.  Re-sampling is changing the size of pixels.  With an image open in ERDAS. click on raster, spatial, and re-sample pixel size, as shown in figure 7.

Figure 7: Re-sampling

 When the pixels are made smaller it is called re-sample up and when the pixels are made larger it is called re-sample down.

Results

This is the result of sub-setting with an inquire box.  We started out with and image of Eau Claire County and some outside areas.  With the inquire box you can zoom in to where you want to be able to observe.  This can be a larger area or a smaller area.  For this we choose to zoom in on the city of Eau Claire.  Image sub-setting is a great tool to have in order to pick an area out of a the big image and you are able to only concentrate on the area that you need.

Inquire Box

In this image we created an area of interest.  This is another form of sub-setting.  Here our area of interest was Eau Claire and Chippewa Counties.  A shapefile was overlaid on the image and we were able to select the two counties and make that our area of interest.  

Area of Interest