Lab 4 Miscellaneous image functions

GEOG 338
Charlie Krueger
Lab 4 Miscellaneous image functions

 Goals/Background:

There are multiple goals for this lab exercise and the first is to be able to select a study area from an image that is much larger that it. This is because a lot of the time researcher will only want to focus on a smaller area of a larger satellite image. Another goal of this lab is to use tools on computer programs to help better the image of the map. This process is done by removing things like course resolution images and changing them to better visual purpose of the image. Taking an image and then recreating a higher resolution image. One goal of this lab was to enhance images in different ways like preforming radiometric enhancement techniques. One of these techniques is called haze reduction and this helps the resolution of the image become clearer by removing the haze.

This goal of the lab was recently new to the software that was being used for the lab and that was linking image viewer to Google Earth. Re-sampling was another goal of this lab where a researcher would change the size of a pixel either up or down to get a better view of the research area. The next goal of the lab was very interesting to learn about and this was image mosaicking. This is when a pair of images are intersected by two adjacent satellites and the images do not fit together well but then through mosaicking they do. The last goal of the lab was to detect changes on images that would change in brightness values.

Methods: 

The first section of the lab was creating an area of interest on a map which would help researchers focus on a study area. This area of interest was created using an inquire box from the raster tool box. After the tool was ran the selected area was save in a personal folder to use later. Next in this section the area of interest was cut from the selected map and then made into a personal file. The next tool that was used was the Subset and Chip tool which was also under the Raster tab in the program. This let the area of interest be placed onto another map and it stood out because of the different zone of the images.

            The next section use the pan sharpen tool and under it the resolution merge was selected. All the necessary images were selected for input and then output was creating a new image from the tool. Nearest Neighbor was the re-sampling technique that was selected for use. This tool created an image that was darker in color and was higher in resolution.

            This next part was Haze reduction which used the Haze Reduction tool under the Radiometric. After the original file has been run through this tool the image that was created was much more vibrant in color and the outlines of the objects became easier to view when surrounded.

            Google Earth was the star of this section of the lab when the viewer was link with Google Earth. This was completed by hitting the Connect to Google Earth button from the tool bar. Then the match to GE to view was selected so then the image viewer was looking at the same spot as Google Earth. Sync GE to View was then selected and then every move that was made on the image viewer Google Earth replicated.

            On part five resampling was the goal of this section and is the reduction or increase of the size of pixels. First the Raster tool bar was selected then spatial was chosen followed by resample pixel size tool. Then two different methods where applied to the same image and these were nearest neighbor and bilinear interpolation. The image from nearest neighbor showed no real results while B.I. made the image smoother around edges.

            Mosaicking was the next task that was given in the lab and this was the process of intersecting two adjacent satellite scenes. The two images were added and then Mosaic Express was used first and then followed by a more advance method called MosaicPro. The files were added into Mosaic Express and not much else was changed in the process. The final image did not turn out that well and had very different colors on them. Next MosaicPro was used and this was defiantly a more technical method of mosaicking. Pro was opened and the images were added making sure to specify image area options by clicking the compute active area and hitting set. Once both images where in the program a histogram matching tool was use making sure the colors would match. Then hitting process, the program created a much cleaner version of the images with the same type of colors.

            Last was the binary change detection using image differencing. Using the same image but taken from two very different years the program that was used to pick up on the changes in the brightness of the pixels. The raster tool was activated and the functions tab was selected and the two input operators. The program was ran giving the output of a change in the fourth layer of the images. Next in this section a simple model was created for use to find the change in the two images. This model did skew the histogram of the images so a correction had to be made and another simple model was created from the first by trying to correct it. Finally, a product that resulted in the change of the images came out and was brought over to ArcMap another program and then the info was used to create a final map. This map showed the counties behind the change and outlined them in red to easy identification.

Results

Results for Section 1. This was the area of interest after it was selected

 

Area of interest over the original map

The first attempt using Mosaicking Express

Using MosaicPro this is no longer a different in the images and they connect 

Final Map after the image differences were taken out and made its own layer 

Sources: 

Satellite images are from Earth Resources Observation and Science Center, United States Geological Survey. Shapefile is from Mastering ArcGIS 6th edition Dataset by Maribeth Price, McGraw Hill. 2014.