Aaron Schroeder Geog 338 Remote Sensing: Remote Sensing Lab 4: Miscellaneous image functions

Goal
This lab had five goals and techniques that would help us become familiar with image functions in Remote Sensing using Erdas 2011. The first goal is to understand how to take a study area (AOI) from a larger satellite image scene. The second goal is creating a high spatial resolution image from a low spatial resolution image to aid in visual interpretation. The third goal involves the use of radiometric enhancement techniques to improve image spectral and radiometric quality. The fourth goal involves linking Google Earth to images we have open in Erdas 2011 and use the classification data from Google Earth to aid in interpreting the image.
The last goal teaches us to re-size the pixels of an image to increase or decrease the file size.

Methods
Lets start off by selecting our study area (AOI) from a larger satellite image. We are going to be doing this using a Inquire Box which is located under the Raster tools. Then just move and re-size the Inquire Box to the AOI and apply the change. Now that we have our location set, we have to separate it from the larger image. To do that, use the tool Create Subset Image, insert the input/output options, and hit From Inquire Box. That will make sure the new image we removed from the large satellite image is the one we had highlighted with the inquire box. The original satellite image and the inquire box image can be seen below in Figure 1. You can also get an AOI out of a larger image by using a shape file. We used a shape file of counties and selected two that were on the satellite image. You then save the area as an .aoi file. This result can be seen below as Figure 2.

If you have a coarse resolution image and need it to have higher spatial resolution, you can Pan sharpen it. Pan sharpening optimizes image spatial resolutions to aid in visual interpretation. In our example, we used a 15 meter panchromatic image and a 30 meter reflective image to create a high resolution image. By using the Resolution Merge tool resulting image has 15x15 meter spatial resolution and the reflective properties of the second image. The results can be seen in Figure 3 below.

Radiometric enhancement can be very useful when looking at a low quality satellite images that have been affected by distortion due to water vapor or other interference's . To correct that distortion, we can perform a Haze Reduction technique. In Figure 4 below, we can see the image on the left has haze distortion. To remove that, we can use a Radiometric tool called Haze Reduction. Once it is applied, the resulting image is haze free, which can be seen on the right image in Figure 4.

The next technique that we used in this lab was linking an image viewer to Google Earth. To get started, we had to open an image file in Erdas 2011 and use the Connect to Google Earth tool. That opens up a new viewer in which Google Earth uses to display the image. Then by clicking the Match GE to View tool, Google Earth matches the image you have displayed with the same spatial extent. This is helpful in the case you do not have much spatial information on your AOI and need to use Google Earths data to increase your knowledge base on the location. The synced up views can be seen in Figure 5 below.

The last technique used in this lab is Re-sampling. Re-sampling is the process of changing the pixel sizes in an image to reduce/increase file size and smoothness of images. Before we can re-size them, we need to know what the current pixel sizes are. To do that, to go the Image Metadata and look under the map information section. Once you have the pixel size, use the Raster tool: Spatial and Re-sample Pixel Size. We are using the Nearest Neighbor method and Bi-linear Interpolation methods to modify the new pixels, and changing the size from 30x30 meters, to 20x20. If you go back into the metadata for the image, you can see that the pixel size has been changed and the overall image is smoother. The only downside to having smaller pixels is that the file size is going to be larger. So make sure you have plenty of storage space. The re-sampled images can be seen below in Figure 6. The original image with 30x30 meter pixels is on the left, the 20x20 meter Nearest Neighbor re-sampled image in is the middle, and the 20x20 meter Bi-linear Interpolation image is on the right.

Results