Image characteristics and different resolutions in Remote Sensing


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.

3 Replies to “Image characteristics and different resolutions in Remote Sensing

Leave a Reply

Your email address will not be published. Required fields are marked *