Goals and Background
The goal of this laboratory exercise is to help the image analyst to extract basic statistical information from satellite images, develop a model to calculate image correlation analysis and interpret the results of correlation analysis for image classification. With this we will equip the analyst with the knowledge of how to identify and remove data redundancy in satellite images.
Methods
In this lab we will be using ERDAS Imagine 2013 to explore data quality through feature space imaging. There are multiple ways to determine the quality of an image, throughout this lab it will be by feature space plot and correlation analysis.
Feature Space Plots
Feature space plots can display data values of two bands at a time in a single image. These plots are basically two or three dimensional graphs that can be used to classify images based on the type of information the analyst desires.
In order to produce feature space plots in ERDAS Imagine 2013 we must first select raster, then superised and then feature space image to create the feature space image window. Then the image must be inputed. Next, all 15 of the band combinations (for the image used particularly in this lab exercise) will then appear as a result of running this model (Fig. 1).
Correlation Analysis
Using correlation analysis can be one of the most effective methods for assessing image quality. This procedure compares each band with the other bands within the image. Based on the values received after the model is run we can determine how related the bands are to one another. If the values are greater than 0.96 then it can be decided by the analyst whether or not both bands should be used because they offer no unique information. However, if the values are low or negative then the two bands which are being combined offer distinct information so they should be included in the analysis.
To go through this process, a model must be run using the correlation function (Fig. 2). Once the model is run a matrix will be produced in notepad. Then you can copy these values into Microsoft Excel to produce a matrix (Fig. 3). Based on these values the analyst can then determine which bands can be removed due to high relatedness. More examples of how to perform this process can be seen in figures 4-7.
Methods
In this lab we will be using ERDAS Imagine 2013 to explore data quality through feature space imaging. There are multiple ways to determine the quality of an image, throughout this lab it will be by feature space plot and correlation analysis.
Feature Space Plots
Feature space plots can display data values of two bands at a time in a single image. These plots are basically two or three dimensional graphs that can be used to classify images based on the type of information the analyst desires.
In order to produce feature space plots in ERDAS Imagine 2013 we must first select raster, then superised and then feature space image to create the feature space image window. Then the image must be inputed. Next, all 15 of the band combinations (for the image used particularly in this lab exercise) will then appear as a result of running this model (Fig. 1).
(Fig. 1) This image shows the feature space plots produced from the eau_claire_2007.img used in this lab.
Using correlation analysis can be one of the most effective methods for assessing image quality. This procedure compares each band with the other bands within the image. Based on the values received after the model is run we can determine how related the bands are to one another. If the values are greater than 0.96 then it can be decided by the analyst whether or not both bands should be used because they offer no unique information. However, if the values are low or negative then the two bands which are being combined offer distinct information so they should be included in the analysis.
To go through this process, a model must be run using the correlation function (Fig. 2). Once the model is run a matrix will be produced in notepad. Then you can copy these values into Microsoft Excel to produce a matrix (Fig. 3). Based on these values the analyst can then determine which bands can be removed due to high relatedness. More examples of how to perform this process can be seen in figures 4-7.
(Fig. 2) This image shows the model maker and formula used to produce the matrix used in the correlation analysis.
(Fig. 3) The matrix produced from the model maker once it has been put into excel format. This data is then used to make the final matrix.
(Fig. 4) This image show the model maker used to do a correlation analysis of the Florida Keys.
(Fig. 5) The matrix produced from the model in (Fig. 4) is shown above.
(Fig. 6) This image shows the model maker used to produce a correlation analysis over parts of Bengal Province of Bangladesh.
(Fig. 7) The matrix produced from the model in (Fig. 6) is shown above.
Results
The results of this laboratory exercise can be seen in the images presented throughout the methods process. In completing this lab, the image analyst developed skills on how to use feature space plots and correlation analysis as well as the differences between using each.
Sources
The data used in this lab was provided by Dr. Cyril Wilson of the University of Wisconsin Eau Claire.
No comments:
Post a Comment