Hello everyone welcome to this 10th lecture on this course which is introduction to remote
sensing in this particular lecture we will be discussing about image characteristics
how what basically image meaning or especially I am talking about digital image form computer
point of view or data handling point of view also we will be discussing different types
of resolutions though in previous lectures we have touched but in detail we will see
and how images will look different in different resolutions and of remote sensing images
As you know that old saying is that a picture tells 10000 words we can add one more line
in it that a satellite image can tell 10000 words so instead of just 1000 words a satellite
image can tell 10000 words the reason why we have added this line because not only it
is a saying but if you are having time series data then it can tell you the changes which
has occurred so for change detection studies satellite images are being used extensively
because of availability of archive and other data sets
Basically what is an image and that is basically a satellite image we are talking now days
we use digital cameras and even through our mobile cameras we take a picture that is snapshot
though it also makes a 2 dimensional matrix and a you are having depending on the resolution
of your camera you are having the size of pixels there
But it is a snapshot this this is there is a difference between a snapshot and a scanning
devices which we have also discussed in some other previous lectures so satellite images
are not really a snapshot except for geostationary satellites but in this polar orbiting satellite
these are the images scanned line by line as like in your table or flatbed scanners
it is done that the entire document or map is scanned line by line rather than a snapshot
So when we take a when you take a snapshot we call as a picture but when when we take
a scanner and scan an image or part of the earth using satellite based sensors then we
call an image so thus the one of the major differences between an image and a picture
or a photograph So photograph is just a snapshot whereas this is scanned line by line so an
image by if we look the definition an image is a pictorial representation of an object
or a scene here we are we have to think that image is being taken by the sensor which are
on board of different satellite
So this is line by line data acquisition is done There are various forms of images earlier
we know that there are analog images digital images so all satellite based remote sensing
images are digital images Originally they are acquired through those opto-electro devices
and they are all in digital numbers
Analog images like photographic earlier we used to have photographic film or we now days
print also so that we call as produce by photographic sensors on paper based media or transparent
media that was the analog and various variations in still characteristic are represented as
variations in brightness or great sets if it is black and white image and the objects
reflecting more energy appear brighter on the image and objects reflecting less energy
appear darker so same concept as in case of analog image so in the digital as well
Digital image basically produce by electro optical sensors so these sensors whether it
is a on a satellite or any other devices they are all so there is lot of similarity between
these table top or flatbed scanners and the scanners which are in space they scan line
by line they produce they produce images of any document or map of part of the earth using
this concept of electro optical sensors
Now the unit of an image is a pixel which is an abbreviation which is called picture
element if you recall in previous lecture I mentioned that like in Landsat 1 the MSS
sensor the it was not a perfect square the pixel was not a perfect square and there because
it was one pixel was being overlapped with another so we used to call as mixel but now
days that problem is no more existent all our remote sensing centres sensors so we call
as a pixel and so it is a unit of an image which is always square in shape accept in
case of Landsat MSS
In future or in current satellites this concept is going to remain that unit of an image is
pixel and pixel is always square in shape and this is these pixels are arranged as a
2 dimensional matrix and each pixel is associated with a number and that number is basically
the quantisation number so we will see that how the but what basically they are recording
is the value is brightness value is being recorded if an object is reflecting very high
than higher brightness value will be recorded in terms of digital values it will be higher
value will be recorded if it is reflecting very low then a lower value will be recorded
If we still if we change this value number digital numbers pixel values into the range
of grey values then the dark are showing the less reflecting (val) objects and brighter
are showing high reflecting objects and they because of the variation in the radiant energy
it discreet form And a an object reflecting more energy records a high number of itself
on the digital image and vice versa
And the digital image in in our GIS terminology also otherwise from mathematical domain also
that we call as an raster image it is nothing but a 2 dimensional matrix and it is a each
unit as I have already mention of an digital image is called pixel pixel is always square
in shape However overall size of an digital image can be either square or rectangular
so this is very important to note that the unit of an image is a pixel which is always
in square but overall size of an image can be either square or rectangle no other than
these 2 shape an image can have
But when we go for when we want want to show only a image of an arbitrary area or a circular
area then what would happen so there is a concept which has been introduced and that
no data so the areas which are beyond this circle but within the rectangle or square
are assigned a value which is no date value and generally it if we are displaying on the
screen then it is mixed with the (orbi) the same value as background value is given and
therefore we don’t see anything there So we feel that image is circular but in fact
in the computer the image can only have 2 shapes either rectangular or square however
unit of an image is always a square in shape
And when though there are images if we zoom a small part of a digital image this is what
we see expect this sharp the grid which you are seeing here this grid is being just to
use to demonstration but in real zooming of an image you will not see these lines this
the grid of the pixel and because these are having some values this is an example of 8
bit image So we are having value 0 as well as 255 total range of pixels values are available
of total 256
So we in digital image processing or in our 2 dimensional matrix concept we call these
are the columns and these are the rows or also we call as scanned line or these call
pixel and this is another very important thing to note that origin of a image an image a
digital image is always considered from the top left this concept will we use later when
we go for geo referencing and there we have to change this one because in in geo referencing
that means when we willing the geographic coordinate systems and to correct an image
geometrically then our coordinate system will start from bottom left corner instead of top
left corner so that that thing will be so therefore it is important to note here that
the origin of an image will start from top left corner
Pixel values as I have said is the magnitude of the electromagnetic energy captured in
a digital image is represented by a positive numbers Now this is very very important that
an (ima) the value of a pixel will always be a positive integer value I am going to
repeat it again it is very important that a value of a pixel of a digital image is always
is going to be a positive integer value but in like in GIS or for other raster types like
for digital elevation models there the cell value can be positive integer negative integer
even real value negative or positive but in case of a digital image the pixel value is
always going to be the positive integer value so this one has to remember
And they are basically in the binary digits or bits so when say this binary mage that
means we are having pixel value I within an image either 0 or 1 that means either black
or white no other shades of grey in between these 2 extreme shades means black and white
can happen then it is image is binary or single bit image but when a we increase this quantisation
or number of this then we get more number of values or a if we think in terms of shades
of grey than we can have more shades of grey between 2 these extreme values black and white
that we will see little later
So each but records and exponents of power of 2 that is a it is one bit that is 2 power
1 equal to 2 so either 0 or 1 total number 2 When it we improve on this the then this
the number available will improve very significantly we will see that example as well so the maximum
number of brightness level available depending on the number of bits used in representing
energy recorded Of course no satellite records at a bit level of course now they are either
7 bit 8 bit 9 bit 10 bits 11 bits like NOAA AVHRR records data at 11 bits
So if but very standard one especially because many of the digital image processing software’s
are capable of handling 8 bits data therefore 8 bits digital satellite images are the most
common one or they are restrained so that they are become much earlier to use with the
digital imaging processing software so 8 bit satellite images are very popular 8 bit means
true true power 8 that means the total number of values available for each pixel to choose
is 256 and the values will vary between 0 to 255 total number would be 256 including
0 and 8 bit as I mentioned is a very common one
So just to for completeness if we see that 1 bit image that true power 1 equal to 2 only
2 sets are available or 2 colors can be assigned values are just between 0 and 1 total 2 if
it is 6 bits like in case of IRS 1C pan camera we had a 6 bit images that means the total
number of pixel values could vary between 0 to 63 total number where 64 in LISS 3 we
had a like a 7 but images and that means the values were ranging between 10 range within
an image and depending on the ground conditions between 0 to 127 total number 128
If it is 8 bit as said the most common one then the values of pixels within an image
can vary between 0 to 255 total 256 that means 2 power 8 16 bit mages with the satellite
based remote sensing are not common yet but definitely because things are improving so
the readymade acquisition is improving with future satellite So hopefully we are going
to have even the 16 bits and you can see that the range is 0 to 65535 total number 65536
and here 20 bit images 2 power 24 that means 1.67 million number of pixel or values or
range is available for each pixel And this 24 bit I want to emphasis here because when
we create a false color composite or color composite using 3 different bands of 8 bits
each than your output image becomes 24 bits instead of having just 256 colors available
for all 3 then it becomes 1.67 million colors
And see this the difference what the difference it creates if we increase just 1 bit instead
of 1 bit if we go for 2 bit see this is just binary image black and white so pixel values
either are white just for representation the the one has been assigned one one has been
assigned white and 0 has been assigned black so you are seeing either white or black pixels
no other in between
But if you go for 2 bit then we are having black we are having white and in between as
well when we go for 4 again more number of values are increasing when we go for 8 so
the 0 value is assigned the back color the 255 value has been assigned your white color
and rest of the values are in between total range is available here 256 to choose for
each pixel and as if you compare the 1 bit image and just see the quality of image so
as the bits have improved that means the radiometric resolution has improved and say the quality
of image has improved your interpretation can become much more reliable you can distinguish
different objects much more easily as compared to in a binary image So that is the advantage
of having higher radiometric resolution images which are becoming now possible
I was mentioning about the 24 bit images so when you use the 8 bit data 3 channel so 3
bands data create a color composite in this example a false color composite assigning
3 different channels using this concept of additive color screen and primary colors red
green blue then this image will become 24 bits image and that means the (nu) each pixel
can have values of the spectrum which is available is 1.67 million color
So any value between those 0 to 1.67 million colors can come to represent that is why generally
colored images are much more useful as compared to black and white images of any channel or
any band which we are seeing That is why it is a very useful though in terms of quantisation
it has improved but the basic thing limitation if we wish to call with each band will remain
same so this output image because 3 channels have been combined therefore instead of 8
now we are having 24 bits image
In case of this example about a particular sensor that is a IRS 1 les 2 image of Paonta
Saheb of Himachal Pradesh area so if we see the digital numbers this is how it looks remember
that these lines these boxes or grid which are just seen here for our own convenience
but image itself will never have it is having just raise in columns and in this you are
having values in continuation
So this is how the values will vary between different channels and the once the values
varies the when we assign the shade grey shades then the image itself also varies so from
computer point of view or from digital image point of view this is how the image is there
in 2 dimensional matrix each cell is a pixel which is unit pixel is a square over all shape
of an image can be square or rectangular
Now we come to different types of resolutions and first we take the spatial resolution which
we have been mentioning few times which is basically the if we look the definition it
is the ability to distinguish close space object on an image that means the resolving
power How nicely you can see the boundary between 2 adjacent objects available within
2 pixels adjacent pixels So if a if you are able to resolve that thing much easier than
we say higher spatial resolution images and when we are unable to do that then we call
a coarser relatively coarser so this is related term There is a no highest spatial resolution
images in terms of in the field of remote sensing and neither there is a coarsest in
that sense
So it is the ability to distinguish close space objects on an image higher the value
for a pixel that it covers the area because it is square shape so if I say 10 meter resolution
image I mean it is representing 10 meter of the ground in width and height wise or length
wise so 10 is square that means 100 square meter of area is being represented by 1 pixel
of a satellite image when I say a 10 meter resolution image But when I say 1 meter resolution
image that means I am representing now only 1 square kilometre of the area width and height
is 1 meter so a small area is now being represented
So once you are having a very small value in case of a spatial resolution we call as
a higher spatial resolution so it is inverse kind of relation The larger the spatial (resolute)
value of spatial resolution we say a fine resolution whereas if we are having coarser
that me and we say larger the value then we are saying it is a coarser spatial resolution
when the value is small like instead of 10 meter when you are having 1 meter then we
say relatively a higher spatial resolution
So one has to understand this the spatial resolution is very important as we are seeing
we go through the history of a present day remote sensing and satellites then what we
find that spatial resolution in Landsat MSS was available about 80 metre now it has improved
even to 30 centimetre even in some panchromatic or multi-spectre data sets like in world view
or global view and kind of satellites Second type of resolution which we will see further
details of this is spectral resolution that is the basically how many channels within
available EM spectrum are there what is the best of this channels
So the location of the channels or bands width narrower the width more number of channels
higher the spectral resolution broader the bands broader the bands means covering the
large part of EM spectrum and less number of bands are there within that part of EM
spectrum we say coarser spectrum resolution So with the time this spectrum resolution
is also improving and slowly slowly we are going towards hyper spectral there we will
have spectral bands for each point 1 micro metre as well and this basically sensitivity
of chosen lambda bands means at the different wave lengths that is another very important
thing
The spectral resolution will also depend in which part of EM spectrum one is looking for
because that amount of sufficient amount of energy has to reach to the sensor to get recorded
so that is another important thing The third out of 4 types of resolution the third one
is the temporal resolution temporal resolution means the time between observations how frequently
a satellite is being revisited or is visiting the same part of the land again and again
If a like a let me give you an example of NOAA AVHRR is NOAA satellites it is covering
the same parts of the earth at least twice in a day in 24 hours so repeativity here is
2 times in a day whereas a very high coarse resolution satellites might be revisiting
that area again after 22 days
So A is 2 times in a day and once in a 22 days so this when this the length time gap
or the time between observations becomes a very small we call a higher temporal resolution
so in this front NOAA provides a higher temporal resolution say as compared to our Resourcesat
or other IRS satellites or even Landsat so on that front because the swath date is much
larger during a coarser resolution NOAA provide higher temporal resolution so it is kind of
a trade of and the last one among the resolutions is the radiometric resolution that the precession
of observations showed accuracy
Precession basically depends on the sensitivity of sensor whereas a accuracy is a statistical
terms So these are 2 different terms which are there one has to remember so 4 resolutions
spatial resolution spectral resolution temporal resolution and radiometric resolution these
4 type of resolution are known in remote sensing we will see one by one details about this
In remote sensing the resolution means resolving power and the capability to identify the presence
of 2 objects how nicely we can resolve these 2 objects capability to identify the properties
of 2 objects and how these properties of 2 objects are recorded in terms of different
pixel values if same pixel values are recorded for 2 adjacent objects that means we cannot
resolve we cannot distinguish between these 2 objects and therefore we will say a poor
might be a poor spatial resolution So an image that shows finer details is said to be a finer
resolution compared to the image that shows the coarser details
And we will see the example here Here what we are seeing that it is 8 metre resolution
the pixels are looking very coarse and you cannot resolve anything here Basically you
cannot make what different objects are there but if we see relatively instead of 8 meter
we go for 1 meter now we can see different objects in a much easier So the ability to
resolve or distinguish between 2 adjacent object is much much better relatively in case
of 1 meter spatial resolution satellite image compared to 8 meter
Another example instead of panchromatic another example is given in the this false color composite
that this is 10 metre example we are able to resolve things very easily we are able
to distinguish but if we compare this image with 80 meter resolution then things are not
easily identifiable so thus this is how the spatial resolution makes the difference
So higher spatial resolution means a smaller value here digital value and more number of
pixels will be there in an image more number of rows and more number of columns would be
there but a compare by here 8 time in case of 1 meter here you will have 8 times more
number of rows and columns but the quality will improve significantly So size of smallest
dimension on earth surface over which an independent measure can be made by the sensor that is
what in sense the spatial resolution and this is expressed by the size of pixel on the ground
in meters controlled by the instantaneous field of view
So we will bring this one as well this concept of instantaneous field of view or IOV but
basically when when the satellite is a looking completely downward then we say the Nadir
IFOB and when because if the swath is much larger like in case of NOAA AVHRR the swath
is 2800 kilometre then we will have Nadir IFOB as well as we will have for an image
Oblique IFOB and these the size of pixels are changing here whereas IFOB is a solid
angle so we say basically you can say it is a 3 dimensional angle whereas you you are
projecting on a 2D surface so that also makes the difference
So the IFOB is a ambler cone of visibility of a sensor with sole here this cool is A
determines area seen from a given altitude at a given B this is the area which is been
covered area viewed is IFOB ad that is altitude the sea in depending on if it is very flying
at very high height then IFOB is the size of pixel the coverage it will have much larger
of the earth with the same sensor so that is the known as the ground resolution cell
GRC or element or pixel
So it depends on as I mentioned that this is how the in area is recorded an image and
this is a solid angle then is converted to this one
Now the second one is the spectral resolution of a sensor refers to the number of location
of band sensors collected in and how wide those bands are So initially Landsat MSS was
there it covered a same part of EM spectrum that it has the same width for at least first
3 bands and these were quite broad wide or broad Later on like in Landsat OLI series
that is Landsat 8 they are these bands are covering very small (num) very small width
of EM spectrum focusing more on the way the threshold is available in the atmospheric
windows for different objects in part of EM spectrum 1 and number of bands have also improved
So the location and number that makes the spectral resolution so a good analogy maybe
how many acrons are in your coloring box you have 8 or 64 colors that makes the difference
here
Here is the example now in this one MSS TM ETM plus and OLI so far whatever the Landsat
series of satellite all 4 types of sensors have been covered in this they are in the
background the grey shades are showing the areas where we are having atmospheric windows
the white areas in the background are the opaque areas but we see here the Landsat MSS
continuous data one band was ending another band was starting without having much inputs
from the threshold of these atmospheric windows and that is why these were continued slice
down in a small part later on in Landsat TM things improved number of bands increased
and the width of band have reduced
So if width is narrow higher the spectral resolution if width is large poorer the spectral
resolution If number of bands are more higher the spectral resolution if less number of
bands are there in sensor poorer the resolution If we compare the Landsat MSS to the Landsat
OLI that is Landsat 8 see the difference see though the numbering has changed but if we
say second here Landsat MSS band 2 and this red continues See it has become further narrow
So it provides the better (distinc) distinguibility of different objects which are present on
the surface of the earth as compared to Landsat MSS data
And in the other sensors are other bands are also shown you can see we compare Landsat
TM EDM both were almost same expect we introduce one more band in EDM plus which was the panchromatic
band though this panchromatic band was made further narrow in case of Landsat 8 OLI series
and all these bands 6 and 7 also made narrower and instead of having 16 1 ETM plus the 6th
band which is thermal channel instead of thermal band which was a very broad it has now have
been proven
So we if we want to say about the spectral resolution in relative sense we would say
that the Landsat 8 OLI series is having higher spectral resolution as compared to Landsat
1 MSS because the number of bands have increased second the width of these bands have also
gone to very narrow in size
What we see that in black and white and in panchromatic generally the bands are quite
wider but when we go for multi spectral like RBB imagery this colored image example is
giving or a word view then these bands are many more and a narrower bands are there So
we instead of like multi spectral sensors we instead of having just 1 or 2 now good
people are going for 15 channels hyper spectral and no discontinuity between 1 band to another
It is completely continuous example is Everest sensor and a there are 256 bands and 100’s
of channels 100’s of bands providing near continuous reading of obstacle spectrum
So entire part of EM spectrum between that range point 4 micro meter to 2.5 micro meter
is covered so any object which is present on the surface of the earth can be distinguished
by channels which are in large in number so we don’t have any gap left in this number
of channels has increased the thickness of these channels or the bandwidth has decreased
very significantly and that will ultimately providing higher spectral resolution So in
in this case of spectral resolution the number of bands increases we say higher spectral
resolution when bands or channels becomes narrower we again say higher spectral resolution
Now broadband is mainly common now a days in panchromatic but they even in Landsat 8
OLI series this has further been reduced because people realise that there is a always advantage
of having narrower bands rather than broadband So the what basically if we are having broadband
data what is the advantage collective radiation across border range of lambda per band or
other EM spectrum so more protons and so more energy
Because why this this question can come that why panchromatic channels are having relatively
broader band as compared to multi spectral channels the reason basically is that in that
part of EM spectrum the energy which is required to be recorded why a sensor on board of a
satellite is much more So if you are in very narrow band the you might not have a that
part of EM spectrum sufficient energy to be recorded by a satellite so therefore in earlier
stages when our sensors capabilities were not up to as in that quality as today we used
to have broader band or channels now the electronics part is improving so we are now going narrower
channels in point one micro meter channels and more number of channels are there
So broadband advantage is that it provides the sufficient energy so that it can be recorded
by sensor which is about 850 kilometre in space Narrower bands whereas gives the more
spectral detail but less energy so the lower signal or lower signal to noise ratio so there
as I have just mentioned that if band is very less there are chances the sufficient amount
of energy may not reach and this ratio is going to be much higher so as electronics
is improving sensors are improving and therefore it is becoming now possible to have more number
of channels within the given EM part of spectrum 1 and a narrower channels are becoming possible
because we are improving on the sensors this SNR or signal to noise ratio We are making
further lower and lower and therefore can increase the number of bands
More bands also more information to store transmitter in process this is a is kind of
trade of everything more is not good in case of remote sensor because then the application
will change like for example of NOAA AVHRR it coves a large swath of the earth 2 times
in a day but at the same time it is having relatively coarser spatial resolution So there
is a trade off but the data storage requirement relatively is very less but if I compare with
say 65 centimetre spatial resolution data then data requirement is very huge and therefore
processing time transmission time everything will increase exponentially or where is highly
significantly
So always it is not good so it is it because now days the choice is available so we are
having now different types of bands are available different type of sensors are available so
first we have to decide for what we are going to apply remote sensing data and accordingly
choose that particular sensor which is most appropriate For example if you are going to
cover a small part of a city at a very high resolution no problem the choice is there
you can go for 65 centimetre even 30 centimetre resolution data because your aim is to cover
a very small part of of a city
But if I am going to cover the entire India say for example then that high resolution
remote sensing data is not going to be useful I will I will require too much of data to
handle to store and process so I will approach and go for a relatively coarser spatial resolution
data so for every application every type of data is not appropriate depending on the application
one should choose the appropriate type of data for in case of a spatial resolution as
well as for a spectral resolution and other temporal and radiometric resolution which
we will see
So if we continue with this broad and narrow band but more bands enables discrimination
of more spectral details but to certain applications if I am working for say for example for mineral
exploration I want to see the slight change in the any mineral concentration say in case
of copper then I am I am looking those slight changes of the ground so therefore for me
broadband channels or broadband Datasat is not going to be useful I am looking for very
narrow band data more bands data
So immediately I should be able to pick that ok at this location I am getting the signals
of the presence of the copper on the surface So there is a always trade off higher bands
means higher storage requirement transmission requirements processing requirements higher
spatial resolution again storage transmission and higher processing requirement lower spatial
resolution large area coverage but less thing but you don’t get those detail so depending
on the application one has to choose the data
Now the last in this one is the temporal resolution that the time between observations how repeatedly
the data is the satellite is covering that area some satellites covers like a radar satellite
generally will revisit the area after 35 days and whereas I have mentioned earlier also
that like no IVHRR will cover and two times in a day so relatively no IVHRR is having
much higher temporal resolution as compared to other many satellites
The revisit time over the same area by a satellite we put as a temporal resolution and the last
one in this one is the radiometric resolution that is the number of digital image used to
express the data collected by the sensor so again if we go for higher radiometric resolutions
we are going an higher in quantisation more data handling more data storage requirements
but off course it is a trader so we are getting better higher quality images
So depends on the application and a and the cost of project as well because some projects
maybe very low cost project you just require to prepare a land use map say at 10000 scale
so you need not to go for a 65 centimetre or 30 centimetre resolution data you may still
prepare a 10000 scale map for land use using sale is three data of 23.5 metre so spatial
resolution radiometric resolution all will depend that for what you are going to use
the Datasat As a in case of radiometric resolution higher you will go more sats or more number
of values to choose for each pixel are available as you can see in 6 bit 64 7 bits 128 8 256
very standard one and then 24 16.7 million colors
Now just to that is a repetition of this image that 1 bit how is binary image how it looks
there
Now if I compare just a these resolutions between 2 sensors which we are on 2 Cartosat’s
1 and Cartosat 1 and 2 the spatial resolution in Cartosat 1 was 2.5 stereo so it has stereo
capability but relatively resolution was coarser as compared to land Cartosat 2 where is it
is a point 8 and more not stereo monoscopic Sensor type of course both are panchromatic
collection time where different swath see the swath as the spatial resolution has improved
the swath has reduced here the space and resolution is relatively poor swath is and this 4A and
after is a because it is a stereo data collection so when it is looking forward it was collecting
data at this swat was 30 meter when it was looking backward the swath was 25 kilometre
Revisit time here in cartosat it was improved instead of 5 days in cartosat 1 improve to
4 days and orbits per day of course this revisit times are interlinked and altitude were also
change in this case So this brings to end of this presentation thank you very much.