Principal components analysis is a orthogonal transformational technique (preserving the symmetry between vectors and angles) to reveal new set of data arguably better from the original data set and better capture the essential information as well. It happens often that some variables are highly correlated with a lot of duplication. Instead of discarding the redundant data, principal components analysis condenses the info. in inter-correlated variables into a few variables, called principal components. The main idea of Principal Component Analysis (PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent. THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.

1. PRINCIPAL COMPONENT
ANALYSIS
KAMLESH KUMAR

2. Principal components analysis is a orthogonal transformational technique (preserving the
symmetry between vectors and angles) to reveal new set of data arguably better from the
original data set and better capture the essential information as well. It happens often that
some variables are highly correlated with a lot of duplication. Instead of discarding the
redundant data, principal components analysis condenses the info. in inter-correlated
variables into a few variables, called principal components.
The main idea of Principal Component Analysis (PCA) is to reduce the
dimensionality of a data set consisting of many variables correlated with each other, either
heavily or lightly, while retaining the variation present in the dataset, up to the maximum
extent.

3. 1. Run the app. Right click
on the image list on the
content tab to add an
image. Go to Open
Raster Layer.
2. Browse the file and
load it.
STEPS

4. 3. Go to Raster after the
image has been loaded
and change the layer
arrangements to Standard
false colour.
4. Go to Home and correct the
view of the image by changing
the alignment to upside down
as the river valleys and ridges
appear inverted.

5. 5. Go to Raster tab and select Principal
Component under Spectral tab.
6. Set the output file destination and
select the number of components
desired to 7 the select OKAY.

6. 7. Add another window beside the
image and load the image recently
saved.
8. Go to Raster Options, change the Display as
to Gray Scale (Panchromatic) and layer to 1 and
consecutively up to 7 to observe the results. As
well as check the result of False colour IR as well.

7. Layer names as
mentioned (1-7).
Observe the
variation in shade
as we change each
panchromatic layer.
Last one is False
colour IR.

8. PRINCIPAL COMPONENT ANALYSIS
IMAGE SAMPLES

9. Notice how the
image displays varied
shades, somewhat
losing detail revealing
a new aspect,
becoming grainier
gradually as we move
towards the 7th layer.

16. Signing off..