Lab 1 Image quality assessment and statistical analysis

Goal and Background

The goal of this lab was to use different methods to view correlations and associations of bands of Landsat. Correlation and association analyses help to eliminate data redundancies. There were two main tools that were used for this lab. The first was feature space plot which showed association between Landsat bands. Feature space plots are images that show ellipses that are either have high association which would show a narrow ellipse or low association, which would have a wide ellipse. The next tool used was a model maker. The model maker is a tool for correlation and creates a correlation table.

Methods

The first part of the lab was to look at associations of bands using feature space plots. As stated earlier feature space plots are ellipse images that measure association. Feature space plots are raster based image analyses. If there is a narrow ellipse then there is a large similarity between bands. If there is a wide ellipse then there is a low association between bands. To avoid data redundancy a low association is needed.

To view a feature space plot ERDAS Image was used. In ERDAS, the route to feature space plots is Raster>Supervised>Feature Space Image. An input image is used and in this case it was the eau_claire_2007 image. The output was saved in the student personal folder. After these simple steps 15 feature space plot images were shown. The images were used to answer questions and view the associations between bands.

The second part of the lab was to create correlation tables using a model maker. Model Maker is a tool found under the Toolbox tab in ERDAS. For this lab there were 3 main objects that were needed. These were a raster object, a function, and a matrix object. The raster object was simply a raster image. For the model there were 3 images. These were an image of Eau Claire, Key West, Florida, and the Bengal Province in Bangladesh. The raster image is needed for the bands within the image. To input a raster image it needed to be dragged and dropped into the model window. These images were found in the Q-drive. 3 models needed to be run separately for 3 separate raster images.

The second object was the function. The function used here was a correlation. To use the function it is a similar process to input the raster image. Once in the model the function was selected and the specific function “CORRELATION [ <raster>, IGNORE <value>] was chosen. Replace the “<raster>” with the raster image “$n1_eau_claire_2007. Also, <value> must be substituted with the number 0. This allows for the correlation of this image and all bands.

The final object was the matrix object. The matrix object is the output of the Model Maker. The matrix object was saved in the Q-drive and was saved as .mtx file. For the Model Maker to work the 3 objects must be connected so that they know to interact with one another.

The 3 image correlation tables were opened with Notepad and then transferred into Excel to allow for correlation tables.

Results

A narrow ellipse showing a high association between bands

A wide ellipse showing a low association