Today we will learn how to employ PCA in ILWIS opensource software environment, OK let’s begin:
1. Our study area is around Kurdistan, Iran. We will use Landsat 5TM image from USGS, the acquisition date is July 5th, 2011. Firstly, open your ILWIS software and import all your tiff images into ILWIS format using the following algorithm:
2. Double click “Principal Components” in the Operation-List window (left side), the following window will appear, if you don’t have MapList, create MapList first with click the yellow button, and input from band B1-B5 and B7
3. The following window (Principal Component Coefficients) will appear when you click “Show”.
4. Write down in your paper the eigenvalues from b1-b7 (right window), and calculate the PC1-PC3 using the algorithm in the left window
The first two or three components will carry most of the real information of the original data set, while the later components describe only the minor variations (sometimes only noise). Therefore, only by keeping the first few components most of the information is kept. These components can be used to generate an RGB color composite, in which component 1 is displayed in red and component 2 and 3 in green and blue respectively. Such an image contains more information than any combination of the three original spectral bands.
Through this type of image transformation the relationship with raw image data is lost. The basis is the covariance matrix from which the eigenvectors and eigenvalues are mathematically derived. It should be noted that the covariance values computed are strongly depending on the actual data set or subset used, therefore the atmospheric correction is mandatory for images with too much noise.
5. Create a color composite of the PC1 (Red), PC2 (Green), PC3 (Blue)
6. Filter your PC-RGB data using “Majority” model to reduce spatial frequency of your data
7. Create; a. RGB from the original spectral bands (742 band false color); b. 432 false color. Just double click “Color Composite” in the Operation List window on the left side of the software, and input your data.
8. Compare the result with composite of the PC1 (Red), PC2 (Green), PC3 (Blue)
A. What are the differences between three different color composite maps?
B. Can you define the top soil classification from the map?
C. Explain the contribution of the TM bands in PC2 and PC3?
One of the many applications of this method is mapping geological structures or top soil in Arid areas.