Hello everyone and welcome to the 15th lecture of introduction to Remote Sensing Course In
this course weíre going to discuss three techniques and mainly spatial filtering and
band receiving and principal component analysis techniques We will also try to cover the decorrelation
stretch which is an offset of principal component analysis technique and there are three techniques
mentioned here The first one is on the indusial channel or bands and the second band ratio
and principal component analysis techniques are performed on multiple bands we say there
is a single and this is multivariate analysis so letís see that what basically is spatial
filtering means here basically
The purpose of spatial filtering technique is to improve the again image quality so that
interpretation becomes much easier or even classification becomes much better or accurate
And the concept of special filtering basically came from the electrical or electronics engineering
there and they used to have a kind of single dimensional or a slit kind of filtering to
pass the signal certain type of signals and certain to block certain types of signals
or noises so instead of having just one dimensional here in digital image processing we employ
two dimensional filters and it is also called convolution filtering techniques
Basically theyíre major if we see the types of these two techniques then one is the low
pass filtering technique which emphasizes the regional special trends or deemphasize
the local variability so that is why it is called low pass filter and whereas high pass
filter emphasizes the local special variability and theyíre just opposite in that sense and
then edge enhancement combines both filters to sharp images in that images so edge enhancement
is a kind of high pass filter in that sense
As I was mentioning that we see this way formed and here what we find that here is a combined
factor and the various signals are together but these signals through filtering like in
electronics engineering people do can be can be separated out and different wavelength
signals can be seen but this is single dimensional thing In the spatial filtering what we try
to use the two dimensional metrics a convolutional filter and then try to move throughout the
image and try to improve the image as per our requirements
So if we see this first the low pass filter i.e convolutional metrics over this the size
of this moving filter or moving metrics may vary but it has to be odd number here it is
like in this particular example it is a 3 by 3 metrics we can have even 5 by 5 7 by
7 9 by 9 but the larger the size is going to be this convolution metrics or a special
filter more the time it would take more the regional data means more surrounding or neighbouring
pixel values will come in calculation and therefore that may not be very efficient
So initially we can think as a 3 by 3 filter and as the purpose of low pass filter is to
deemphasize the local variations which are present and in which and in reverse we get
the emphasis on the regional variations so that is why it is called low pass filter Now
we see this is the original image digital representation of this image is also shown
here and when it is subjected to this kind of filtering then this is how the calculation
is done So either we can have this kind of thing or having one integer and this is the
low pass filter what it is doing basically as you can see that it is making the average
of all surrounding 9 pixels and giving the centre value where like here an average value
So instead of like here value 30 in original image that gets 27
Likewise the value here 26 it gets 25 so that as you may realize that the difference here
between 30 and 26 was 4 after a special filtering low pass filtering the difference became only
2 That means the local variations has got deemphasized whereas the regional variations
have become obvious As you can see that the lot of variations which were present in the
image especially in the built up areas and other part of this image they have all disappeared
and now we see a more regional picture Though the sharpness has also this is a kind of diffusion
filter so this is how it can be
Now it is not necessary that all this value should be 1 we can change these values as
per our requirements but that is mainly done in case of directional filters which we will
see later and this kind of a filtering is very helpful in the mapping where we donít
want to see lot of details local details we would like to see the regional details and
therefore we apply a low pass filter
There are several high pass filters can be designed like this as a few examples six examples
are shown here that convolution metrics for edge detection directional particular direction
or high pass filtering can be achieved by subtracting result of a low pass convolution
from original image and this is by devising metrics based on more complex algorithm like
B and C edge announcement metrics are there which can be add edge to the these are the
directional filters you can realize and that in certain directions things will so we will
see some examples
Here a high pass filter in which the convolution metrics is 11 by 11 pixels so as I was mentioning
this odd number 3 by 3 5 by 5 7 by 7 9 by 9 even 11 and see these all local variations
through this high pass filter Itís not directional just simple high pass filter of having a size
of 11 by 11 pixels have completely evaporated the regional variations but however it has
brought the local variations has emphasized the local and then if somebodyís looking
for some linear features maybe linear meant mapping or for road mapping or any other such
features then this kind of filtering can be very helpful
All these grey shades and everything has almost disappeared and image has become virtually
binary kind of image but your interpretation on linear features or some other features
which are having very contrasting surroundings in original image can be emphasized very clearly
Similarly (uh)if we bring the direction instead of having in old direction the same like in
case of high pass filters here that like if I take this example then minus 1 minus 1 minus
1 so in all four directions the treatment would be same That means the values from the
surrounding pixels will be treated same but in case of directional filters as you can
realize this example is again from 3 by3 convolution metrics is that only on one direction weíre
using minus values whereas in opposite in the northern part weíre using the positive
values so what this do it will produce the image in northward direction which will emphasize
the features
If there are schedules and in northward direction they will be emphasized and B will produce
altogether a different results and it will produce a southward direction So remember
that wherever you are having minus values in opposite direction the emphasis would be
there Here weíre having minus values in southern side so we will have northward direction things
And this is an example that different directional filtering the image was the same filtering
is done earlier it was simple high pass now directional the peers would be different
Linear features or which are perpendicular to this diverse direction can be emphasized
very easily without much human interventions and then mapping of such things can be done
very easily so that is the advantage of having high pass filter especially with the some
emphasis on certain directions Now in this example in the B example weíre having Southward
emphasis we can have just opposite so northward north-westward emphasis we can also change
these values put here so then we will have this north west south east in all directions
and such filtering can be designed and results can be obtained
Now we go for the multivariate image analysis and the special filtering as mentioned earlier
is always done on the single band then later on you can combine even sometimes people also
apply this edge enhancement of filtering techniques on a false colour composite as well so what
it would do if it is not very strong filter what it will do it will produce an image output
which would be much little sharper maybe compared to the original one so in order to sharpen
image we use this high pass filtering in order to deemphasize or make more smooth appearance
defuse kind of image they use the low pass filtering In various image processing softwareís
or even photo editing softwareís you may find a certain options like defusing the image
or sharpening the image so edge enhancement in particular direction can be done and so
on
Now in multivariate image statistics basically as you know that in remote sensing instead
of one band weíre generally having multispectral bands several channels are now available and
how to involve many channels in analysis just depending on three channels for false colour
composite and doing everything on false colour composite we will not be utilizing the entire
number of channels which are available so the best thing is to get involved in this
multivariate images statistics or analysis
As weíre concerned with the measurement how much radiant flux is reflected or emitted
from an object and is more than in one band because in one band we have different reflection
of the same object whereas in another band itís different signatures so we want to exploit
many bands and use in the analysis so that we can produce better results better classification
results as well so in multivariate statistical we measure such as covariance and correlation
among several bands to determine how measures co-vary
Basically the purpose here initially is to see how bands are correlated Let me give you
an example Landsat MSS initially the band 1 and band 2 they were very highly correlated
because the responses of different objects or features which were present on the surface
of the earth were sewing almost same responses in the bands So if the correlation is very
high between two adjacent bands or any two bands then we can choose either one to use
in analysis but if there is a lot of difference then we can use both of them so for that purpose
this multivariate image statistics to analysis is very important
Two popular techniques are there one in band receiving principal component analysis or
there are more complex steps are there instead of just simple receiving and we create some
induces like normalize difference vegetation index or some other indexes to use multiband
data and create some better output So we will see that how these things can be done
Now in the band receiving the ratio of two bands removed much of the effects of illumination
in the analysis of spectral differences If you are having too much shadow areas and bright
areas and shadow areas you involve two bands and create a ratio you would find that the
shadow effect have reduced and this is what it isÖ that solar illumination is here we
donít bother much about the scattering which is happening in this atmosphere
But if the structure is something like this then this part the part of this hint is more
illumination and whereas other part goes in shadow and in three channels are shown here
examples of three channels in this view graph and they here the same same same thing is
there But theyíre also in different signature so if we start looking well use 46 23 23 for
red green and blue whereas here because of more illuminations in this part and present
of a different maybe rock type all three channels are showing the same signatures whereas due
to the shadow here it has got 46 it is just half say reflection and this is here also
roughly half of on this side
Now ultimately it is getting recorded only 5 14 and 32 so if we perform if we perform
band wise in involving two bands what we would find that this affect of shadow one surface
illuminated another one is not illuminated or in shadow this weíll remove and this is
possible because of composition difference between geologic units in this example here
limestone and red band and which is grey white and in the RGB i.e red green blue in the combination
of 3 2 1 and red band which is in RGB 3 2 1 determines the ratio of any two bands whereas
the illumination determines the magnitude of and digital numbers received by the satellite
So this is the advantage of having bands
Now we see the output here a blue radiation means band 1 and since the strong topography
while the strong topography is visible because of differences in illumination itís a hilly
terrain rugged terrain and when illumination is there there will be some shadow in the
hilly terrain and that will show the depth perception as well so if we want to deemphasize
the depth perception then we need to involve some band rationing
So here in this example band rationing of band 3 oblique 2 was done and which removed
the effects of topography and as you can see that this unit a litho unit which was basically
getting the same signatures in band 1 but in issue of band 3 into which has removed
the topographic effects or different illuminations because of shadow and in the rugged terrain
and we can now identify that it is a completely different rock unit
So that is the advantage of having band with the single channel no matter how many types
of main enhancement of filtering we perform but still we may not be able to discriminate
between two different lithologies which we have present in this particular example but
if we involve the band rationing then because of we could remove shadow effects we could
discriminate two major lithological units quite easily
Same might be in case of forest cover or in two different land uses or maybe in tool soil
types So the similar techniques can be implied but there are not really very sort of that
there is no straight forward rules are written that if weíre in this problem you can take
these two channels make the band ratio and everything we need to not at all so it has
been it has to perform itís a kind of hit and try so you perform you take two bands
make the bands ratio and see whether results are satisfying serving you a purpose or not
thus that is one limitation of these techniques
Similarly if youíre having too many bands you can create the band ratio of several bands
at least maybe 6 use them and then later on you can make also colour composites Those
not be a standard false colour composite but anyway colour composites can be used so composites
of band ratios like example here that these two colour band receives this is the band
of 5 by 2 and this is the band 7 by 4 an example of a Landsat TM and what weíre finding that
once when we combine in RGB fashion the ratio of 3 by 2 ratio of 5 by 2 ratio of 7 by 4
in red green blue screen then just compare with indusial ratio and just final colour
composite
It is really giving very good results even the variations within 1 litho units which
were seeing in single band ratio example now have been emphasized here and then you can
imagine that what kind of better interpretations of classification can then be performed on
such colour composites Though these are not standard colour composites but when somebody
is looking for mineral exploration or looking for different types of vegetation different
types of sides different types of lithologies then we need to try to involve as many as
bands
Here band 2 band 3 band 5 band 4 and band 7 so many bands have got involved otherwise
in the standard colour composite you would be able to involve only three bands and whereas
here weíre involving at least 5 to 6 bands So 5 bands have been involved to create a
colour composite otherwise only 3 can be used So this example is from to find out the hydrothermally
altered rocks and finally it was observed that band 5 is good for iron oxide detections
But again if I apply the same band ratio in technique instead of in this area to another
area then the terrain conditions are going to be different soil conditions vegetation
conditions and I may not get the same results so again we have to see that which band will
emphasize which kind of rocks or materials or alterations according to it we have to
adopt these things so there is no straight forward written rules that if you find this
follow this no you have to try different band combinations
Now offsete of a simple band ratio is this normalized difference vegetation index because
in normal band rationing thereís no normalization and therefore you get a very high value or
very low value but in this one you can normalize like in this example near infrared channel-the
red band divide near infrared channel plus red band and this I used to basically for
mapping or seeing the changes in the vegetation i.e normalized difference vegetation index
this is that we want to see the health of a vegetation
So by applying this kind of arithmetic on each pixel involving two bands one near infrared
because near infrared band as you know the healthy vegetation will have the highest reflection
and the red band which is if it is not that healthy then it will have so this will create
a difference and then we can normalize it and so whereas increases in red so stress
vegetation and very near infrared shows the chlorophyll
So this way we can really map the health of vegetation even not global scale if weíre
using relatively low resolution low space of resolution data sets like NOAA AVHRR which
is extensively used with of using the time series data to see that how with global climate
change how the vegetation is changing and where it is changing maximum where it is changing
minimum so that kind of analysis is possible involving two channels only but normalizing
it so that we can emphasize that where what is happening to the health of vegetation
How this works is very simple as I explained that in near infrared and the healthy vegetation
will say roughly reflect in this particular example about 50 percent radiation whereas
in visible part of EM Spectrum it might reflect only 8 percent so if I use the same values
and I convert them to percentage then this is what the NDVI is going to be 0.72 in this
particular case where vegetation is healthy
Whereas in case of a vegetation which is see this is example if I take a example of a deciduous
trees then before the fall of these leaves when they become yellow the infrared because
of chlorophyll content has reduced and the reflection radiation back radiation or solar
radiation may get reduced by 10 percent whereas in visible part this may increase very significantly
So instead of in a healthy vegetation earlier say for example it was 8percent now only now
30 percent is getting back to the satellite and whereas in case of infrared it was 50
percent now only 40 percent is getting reflect and therefore you see that the the normalized
difference vegetation index i.e NDVI has reduced to 1.14 so from 0.72 it has reduced to 0.14
that means just looking the values or assigning some colours or if palette to these values
on a map we can see that where the vegetation is healthy where is the vegetation is not
healthy
The same technique is also used in agriculture and practices are to assess the health of
a crop in a region in an area if youíre using high resolution images then even at a level
of a firm it can be assessed so this is how estimations are done about the production
of certain crops in different countries including in India Very extensively remote sensing is
being used in India to assess that what kind of crop production is going to be in the Kharif
season or Rabi season so the technique is very simple you involve a normalize difference
vegetation index NDVI you use two channels infrared channels and visible channel and
create this normalized index and then you can assess this very nicely
This is the example of giving of some parts of you and UK United Kingdom an island and
as you can see that in the month of June in 2003 the NDVI was something like this Then
the London is here which is a completely built up area less vegetation that is why it is
sowing in really in zero or in minus values Whereas average NDVI for here in October 2003
because this is when youíre having maximum greenness maximum good growth of vegetation
and therefore youíre seeing like this
But when it comes to October just a fall period then what youíre saying a large area has
become red that means here the vegetation has changed or so in this way we can assess
that where the impact of things are this is of the same year but if suppose these are
going June 2072 and June 2016 then we can know that how things have changed so if month
is same years are different then we can see the changes which has occurred we can involve
them since how things have changed where things have changed and how the things have changed
can also be estimated so this is a very good technique involving minimum two channels and
creating indexes this is simply offsete of band rationing
Now the another very popular technique in digital image processing and that is principal
component analysis or in short we call as a PCA ant this is in if you think that there
are you have only two bands here I am taking say example of band 1 and band 2 then I create
a scatter plot then what I see that how when in this plot I can assess that how these two
bands are correlated so what I find that these are highly correlated in this one but I donít
want to use for these two bands for further because if two bands are highly correlated
then no use of using them even in false colour composite or any other class
Weíre looking the bands which are less correlated or not correlated at all so if this is the
situation then I want to use the maximum variations which is available along this direction so
this is what it is done here that these axis are rotated so this X and Y axis are rotated
and they this becomes basically the origin also shifted so the maximum variants which
are available are will go as a principal component one and then next variations will go in PC
2
Think that if I involve three bands then I can have a 3D plot so there will be another
one which will be perpendicular to this screen and there will be also having variations maybe
little less so that might become my principal component 3 and the likewise then I can emphasize
or use these variations and can create again colour composites as well so because of band
correlations one weíd see in band 1 is not so much different what weíll see in the band
2
As per linear algebra weíre making Eigenvector weíre Eigen values containing the contribution
of each band to principal components As I have explained that origin is shifted the
axis are shifted and you get principal component 1 2 and 3 It is a statistical technique which
we can perform
So 6 principal components for band 1 1 to 5 and 7 for Landsat images shown here and
this is 1 2 3 4 5 and 6 and 6 is having a lot of noise and 4 is also having some stripping
issues none the less if I involve these principal components then what you will realize that
principal component 1 is having the maximum variations and a lot of information can be
tried further can tried from principal component 1 and whereas this principal component 6th
which is having only mainly noise so up to 3 principal component 1 2 and 3 can further
use to create a colour composite and that will give you a really good information out
of all 6 bands which were involved to create all this
So 6 bands through principal component analysis have been reduced to 3 and all the variants
which were available in different bands among different bands have come into the 3 components
the component 1 2 and 3 and then in later on as you go further and further you mainly
get the noise so noise has also been reduced or removed roughly and then you get 3 components
all 3 components are completely different although same area
So likewise if we see here in a statistical means for all bands 6 are here and then there
is correlation and covariance metrics the Eigenvectors can also be assessed here and
when you compare band 1 band 1 of course it is going to be band and then you see that
band 1 and band 2 are highly correlated but so likewise you get a statistics as well through
which also you can assess about indusial bands among bands and their statistics as well so
similar kind of test can be created
Now there is one more step in this one instead of just creating a colour composites if it
performs one more thing like making a stretching oval indusial components and if there is this
if we do it then this technique is called Decorrelation stretch so see the example is
that two bands are plotted here through a scattered plot band 1 and band 2 it seems
theyíre highly correlated whereas compared to this band 4 and band 7 if we have plotted
completely different bands theyíre showing a much more variation as compared to band
1 and 2 and in between band 3 and 5 are also showing some variation
So what we can do when we shift the origin and axis same time we can stretch these reasons
and we can create more contrast in the images so in most of the images the radiation what
you receive in one wavelength is a strongly correlated variation among in most other bands
the best example here band 1 and 2 theyíre which are highly correlated
The bright is bright across the spectrum and end is dark so we see colour or spectral differentiation
In infrared when radiance varies from correlation line so when correlation produces images without
much contrast to illuminate a paisley colour image we remove the correlation and then we
play in this colour space and this is huge saturation intensity or new intensity saturation
of cyan magenta yellow colour space and we remove this correlation by stretching indusial
components and by which ultimately in a colour composite of destretched or stretched principal
component we get a altogether different output and which technique is called decorrelation
stretch
So this is how it is done and then in the original RGB space different bands are having
correlated things but when after decorrelation stretch that the DCS in the same colour space
and it is now occupying a complete queue and if we play further in colour space we can
send and one can create more decorrelation among these So this is how the colour space
can be exploited to create much better results
And this is the example here in the real image This is the original false colour composite
which was subjected to decorrelation and so first the principal components that means
they were input bands were only three and the correlation among these three bands were
determined and then principal component analysis were performed all 3 components came and then
using this principal component they were two stretched indusially and you go back again
and put them in sameÖ maybe in same colour skin so what do you find that now you can
differentiate this red and that red in RGB in simple false colour composite both are
looking red but here theyíre looking completely different
So decorrelation stretch will bring more colours basically in clear image that means completely
less correlation or roughly no correlation and therefore more colours are there and you
may get an image which is easy to interpret easy to discriminate between two edges and
objects as in this example and can become very very useful
So likewise multivariate single channel or single variant or multivariate analysis can
be performed using one band in case of simple spatial filtering or in involving multiple
brands like in brand receiving NDVI and principal component analysis and decorrelation stretch
so NDVI and decorrelation analysis are basically offsets of two basic techniques one is the
band rationing and another one is principal component
The main purpose of digital image processing is to make image more clear than the original
one to improve the image quality why to improve image quality So that my image interpretations
becomes much easier and more accurate If I go and do perform classification then my classification
accuracy should also improve so thatís the purpose of digital image processing to improve
the original image to an extent that image interpretations becomes much easier two adjacent
features or objects which are present normally theyíre not distinguishable and through normal
just normal visualization
But if I subject these channels bands to like decorrelation stretch then I should be able
to see very clearly that how these two features are present so I should be able to mark them
make them and make my interpretation more reliable So thank you very much