It's my first presentation

1. Band Ratioing And Differencing
&
Principal And Canonical Components

2. What is Band
ratio
Some Key Ideas
of Band Ratio
The mathematical
expression of the
ratio
Introduction
Of
Band Ratioing
Band Ratioing

3.  BAND RATIOING
 Band rationing means dividing the pixels in
one band by the corresponding pixels in a
second band.
 Some times differences in brightness values
from identical surface materials are caused
by topographic slope and aspect ,shadows,
or seasonal changes in sunlight illumination
angle and intensity. these condition may
hamper the ability of an Interpreter or
classification and identify correctly surface
materials or land use in a remotely sensed
image.

4. What is Band Ratio?
 A common remote sensing application.
 A comparison of two bands.
 A way to take advantage of the properties of objects.

5. Some key ideas of band ratio -
• Light exists along a spectrum.
• Different sensors capture different
wavelengths of light.
• Object have a spectral signature.
• Key Properties of those spectral signatures
can be used in Remotesensing.

6.  The mathematical expression of the Band
ratio
The mathematical expression of the ratio function is
BVi,j,r = BVi,j,k/BVi,j.l
Where,
BVi,j,r is the output ratio value for the pixel, at row i, column j, and BVi,j,k
and BVi,j,l is the brightness value at the same location in band k and l.

7. Band Math
Band ratioing is accomplished by the simple division of one
band by another.Band ratioing is used to enhance differences
between bands which represent spectral features while
suppressing other features.

8. Uses
Ratio images can also be used to generate
false color composites by combining three
monochromatic ratio data sets.

9. Advantage
• combining data from more than two bands and presenting the
data in color.
• In band ratio we get particular information of separate two
bands.
• Some times differences in brightness values from identical
surface materials are caused by topographic slope and
aspect ,shadows, or seasonal changes in sunlight illumination
angle and intensity. these condition may hamper the ability
of an Interpreter or classification to identify correctly surface
materials or land use in a remotely sensed image.

10.  Image Differencing
Image differencing is an image processing technique used to determine
changes between images. The difference between two images is calculated
by finding the difference between each pixel in each image, and generating
an image based on the result. For this technique to work, the two images
must first be aligned so that corresponding points coincide, and their
photometric values must be made compatible, either by careful calibration,
or by post-processing (using color mapping). The complexity of the
pre-processing needed before differencing varies with the type of image.

11.  Image Differencing

12.  Uses
• Image differencing techniques are
commonly used in astronomy to
locate objects that fluctuate in
brightness or move against the
star field.

13.  Principal And Canonical Components
• Two mathematical transformation techniques are
often used to minimize this spectral redundancy:
 principal component analysis (PCA)
 canonical Component analysis (CCA)

14. Principle Component
• Principle components analysis is
a technique that transforms the
original remotely sensed dataset
into a smaller and easier to
interpreat set of uncorelleted
variables that represents most
of the information present in
original dataset.

15. • Principle components analysis has proven to be of value in the analysis of multispectral
and hyperspectral remotely sensed data.
• Principle components are derived from the original data.
• The ability to reduce the dimensionality from n to just a few bands is an important economic
consideration , especially if the potential information that can be recovered from the
transformed data is just as good as the original remote sensor data.
• A few of PCA may also be useful for reducing the dimensionality of hyperspectral datasets.
• To perform principle component analysis we apply a transformation to a correlated set of
multispectral data.
Principle Component (Cont…)

16. Limitations
 Directions with largest variance are assumed to be the most
important.
 PCA based on mean vector and covariance matrix. Some di
stributions may be characterised by this but not all.
 PCA is not scale invariant.

17. Canonical Components
Principle Component Analysis is appropriate when little prior
information about the scene is available. Canonical component
analysis, also referred to as multiple discriminate analyses,
may be appropriate when information about particular features
of interest is available. Canonical component axes are located
to maximize the reparability of different user- defined feature
types.

18. Canonical Components

19.  Conclusion
The presented paper dealt with possible application
“ Band Ratioing And Differencing , Principal And
Canonical Components” in image processing and
other application is can be an area study as well as.