Hello everyone welcome to 16th lecture in the this series of introduction to remote
sensing and in this particular lecture we will be discussing image classification techniques
there are various types of image classification techniques exist and some are also evolving
so few of them we will see which are most popular but before that we would like to focus
why would we want to classify
As you know that image is a continuous data it is not discreet data like a simple map
say may be a land use map or a lithological map or soil map where the boundaries are distinct
and one unit is there for one soil one type of soil and another and within in one unit
you do not have the variations but an image is a continuous data not a discreet data so
sometimes it becomes very difficult to use continuous data and therefore from continuous
to discreet one way of doing this thing is to perform image classification
Ultimately what we tied to go for image classification to say for a lithological mapping for maybe
for mineral exploration maybe for alteration mapping to find mineral deposit or very popular
application of image classification that is land use and land cover LULC in brief we mention
Maybe for vegetation types maybe based on density maybe based on the species type similarly
in agricultural fields also we also use image classification to show different types of
crops their condition and so on So basically any image can be classified can be discretised
into certain number of classes as per our requirements and the variability which exists
within an image So this is the not an exhaustive list it is a everyday new applications and
new classification techniques are
So if you look the different types of classification here basically the as I have mentioned that
we would like to covert that continuous remote sensing data into into discreet form and by
(meani) so that we get the some meaningful categories representing surface condition
or classes Also some people call as a instead of image classification they call as feature
restriction also some little different terms are also used like better recognition 6
So there are 3 types of these recognitions 1st is spectral pattern recognition and the
(proce) procedures classify pixel based on its pattern of radiance measurement in the
each band more common and easy to use this spectral pattern recognition Then the 2nd
one is the special pattern recognition in which this under this the classification is
based on those pixel but it build relationship with surrounding pixels In the previous one
each pixel will be treated independently but here the surrounding pixels neighbouring pixels
are also considered So this one is more complex as compared to spectral classification more
complex and difficult to implement
And the 3rd type is the temporal pattern recognition this is becoming more popular now a days because
of lot of data through archives so look at changes in pixel overtime to assistant feature
recognition so if you are having a time series data you want to classify you want to create
some maps which can tell lot many things meaningful or extract meaningful features than temporal
recognitions are possible
Now different types of classification that was based on the pixel neighbouring pixels
or in the chain detection but in the other way most popular one which you would find
in the books of remote sensing or digital image processing is especially this 2 types
1 is unsupervised classification which we will take little later which is very simple
one and popular one as well and supervise class represent which requires certain inputs
by the user so that it involves a lot of work and therefore it is more accurate and it will
use a both types of this one
Now in unsupervised classification basically what we do instead of a continuous data we
aggregate this continuous data to natural spectrum grouping or clusters based on their
spectral radiance and no knowledge of about thematic cover class then we perform supervise
classification we do not have basically the ground information much available Whatever
we are seeing on that images based on the spectral radiance based on the spectral districts
one can classify under this unsupervised classification
Not much not many inputs are required except that if suppose I am having an image I want
to instruct the computer or the software that a please classify in it classes so based on
their spectral radiance it classes will be generated but and these maps though it is
a one of the very popular way of doing classification but still it is having lot of limitations
and they are not that accurate either But in supervised classification which is like
a we identify certain areas that this is per my interpretation skill this is the forest
area so consider the spectral characteristics of this area and implement this throughout
he image wherever the similar characteristics spectral characteristics exist then consider
this as a forest cover
And if I choose another area like bear soil or bear rocks or sand bodies water bodies
so similarly I train the software or train the system to classify images according to
the spectral characteristics available there So we will see here and that pixel look like
most based on that trading set pixel categorisation and because this is being supervised by an
user who is going to do classification that is why it is called supervised classification
training set of areas are used as a numerical interpretation keys
So this is how that if I am having a say false composite of an area and I have I am having
some skill of identifying different ground features then I will train the system that
as per my understanding this is water body this is land you know bear soil rock forest
agriculture land and sand part and other things Like here in this example that water sand
forest are one and corn field hay field and all these things are there as per their you
know pixel values in different bands these these training sets will instructs to classify
this image this color composite or different bands
So likewise ultimately I will had so first first step in this basic basic step in supervise
classification is to train that is training stage collecting numerical data that is means
pixel values from different bands from training areas where I am sure that this is dense forest
so I will select from there collect the training area and then depending on the spectral response
patterns of land cover categories and the second stage is classification stage compare
each unknown pixel to spectral patterns assigned to the most similar categories So wherever
the computer will find the similar spectral characteristics that area will get forest
and so on so forth Better the training we give to the system better the classification
we can achieve
So here the input from user are very very important and then here once a I once these
classes have been compared the training sets have been compared with the remaining spectral
patterns than the classification is achieved and then in output stage you have a map like
if I have classify they say band and then image a color composite image using these
number of classes I will have a map maybe a land use map forest density map lithological
map maybe soil map and so on So likewise I will have a map I will have table of areas
which will tell you tell us the statistics that how much different categories are covering
what are their percentage and we create the digital files so that we can use elsewhere
So training stage is very important and it is that is the determines the success of classification
if training sets are very reliable and if I user is having prior information about that
area then it becomes very good if it is not then if I have to do a very accurate classification
then one has to go in the field really check whether I the area which I am identifying
as a dense forest is really exist in the field or not So likewise if I have collected field
data or training sets from the field and then I input in the image then my classification
is going to be very accurate So classification is still which is the heart of the supervise
classification and the output three stages are there
Now the spectral characteristics which we have seen some spectral curves for different
types of ground features like vegetation water bear soil and all those things So now there
that understanding will really play very important role like here one way of looking is through
this you know this graph like I am having different bands here or plotting against this
EM spectrum number of pixels so what I find that for water there is a one curve for land
there is a another curve vegetation and other thing so I can distinguish this
The other way instead of doing this thing I can go for a 2D scatter plot and a I may
get clustering of pixels like this so then I can train after looking such scatter plots
I can train my image for classification in a much better way But if I am having the facilities
to go for 3 dimensional plotting then my like here the red areas which is vegetation in
this schismatic and the bear soils so there is some overlap in 2D in 2 dimensional space
I am seeing but in fact they are altogether different they there is no overlap so here
in the 2D there is a overlap but in 3D I can discollate very easily
So I can train to to my software that this area where in this these 2 bands or in 3 bands
this is the characteristics of water body these are the spectral characteristics of
vegetation there is the characteristic of urban area and or built up area this the characteristics
of water and likewise then I can classify more reliably so instead of 1 dimensional
image histogram we generally we prefer 3 dimensional histogram or even a in sometimes 2 dimensional
histogram for scatter plot can serve proper The best thing whatever is available if we
plot here then we can discriminate features based on 3 bands characteristics and they
are generally like we perform on a color composite so there 3 bands will have their plottings
spectral characteristics and then classification can be done
So better as we mentioned that better we train the system better for the cases we are validity
of classification depends on a primary knowledge of the technician or a whoever the user who
is going to classify the image So if we are having some ground information about that
area then it is going to be much better way and conventional cover classes are recognised
in the scene from prior knowledge after certain experience one
One becomes a expert on interpretations image interpretations and therefore one can train
the system very easily about different classes and training sets for each class are chosen
then each training site that is class results in a cloud of points in a end dimensional
as I have shown in a 3 dimensional space And then you assign different colors just to represent
different classes maybe like for vegetation you may assign green color water body you
may assign blue color and so on so forth
Like here this is an real example that the in main aim is to classify this image as per
the land use or land cover type these 2 terms are used land use and land cover land use
when we use land use word when human interventions are prevailing in that area or prevalent in
that area but land cover we use the word when a when we feel it is a natural territory not
much human interventions are there in that area then we use the word land cover instead
of land use
So in this one in the land cover analysis these are the different training sites have
been identified how these have been done as explained like if there is a forest or different
types of even green patches which you are seeing these are the training sites so like
like this like green color which is this area so based on image interpretation or might
be priory knowledge about the area I am sure that this area is having trees big trees
So this area is been this much spectral characteristic have been collected and assigned a color and
declaring to the system that wherever you find a similar characteristics the similar
characteristics within that image classify it as a trees similarly for water bodies or
sea water like for sea water so this area it is found that ok this is good at training
set for the water or sea water so wherever the spectral characteristics will match within
this image that entire area will be classified as sea water
Maybe for sediments different type of concentration of sediments which is visible can be classified
urban areas and so on so forth And once these training sets have been collected then rest
of the thing has to be expended in the entire image by the computer or by the software that
wherever this similar characteristics for each classes will be match those areas will
be assigned the same color and then you get the result which we will see little later
So the after the training sets the next step is for the computer to assign each class to
spectral class is appears to belong to these digital number of pixel values of the bands
which are being used and clustering aluminium looks at clouds or pixels in spectral measure
measurement space that is in 3D space from the training to determine the cloud or the
clustering for a given training samples Likewise here of this is schematic not these are the
like this is a the this color is indicating that these areas have been cleared off so
they are having different spectral characteristics these area waves (())(17:18) which are having
like this one having different characteristics and these clusters in 3D space can be seen
So once it is done then algorithm is used different algorithms are there which we will
discuss one by one
The first one the very popular one is the minimum distance to means classification also
called the chain method another ne is the Gaussian maximum likelihood classification
or classifier early piped classification and each will give a slightly different results
and we will see some results and we compare how they create different outputs The simplest
method is as I have mentioned is the minimum distance in which a theoretical centre point
for each cluster is plotted based on the mean values and an unknown point is assigned to
the nearest of these and the point is then assigned to that cloud class so all these
here first minimum distance to mean
So for each class as mentioned for each cluster there will be a mean and for entire there
will be a mean so this is minimum distance to mean that is the area and the distances
are measured and then clusters are decided that these pixels belong to a particular class
Here in this schematic these like these plus symbols are belonging to the urban class and
so on so forth So this way if we go for classification use that algorithm after training sets that
is the minimum distance to mean
Then in there is a parallely piped classification or a where instead of determining the mean
a box are drawing drawn and wherever these pixels are falling having the same spectral
characteristics according the their they are assigned As you can realise that here the
plotting is same in 2D space plotting is same like here but I can draw a box so that some
more pixels will be included of some other areas but if I draw a circle or a maximum
likelihood then I will have a altogether different and therefore there will be some difference
in the results
More precise instead of simple parallely piped classification where only the rectangles or
squares are drawn I can draw some other types of boundaries and can have more precise parallely
piped based algorithm based classification So different methods of spectral classification
can be represented diagrammatically as here I have just shown
And this is another is maximum likelihood classification the third type that the contours
here the contuse are drawn and these are based on the probability that the contours express
the probability that any point belongs to a particular class which is bases of maximum
likelihood method classification and likewise that the centre of this cluster or having
plus symbol is having the maximum likelihood as you go away away then that likelihood intensity
decreases and there might be some mixed pixels scenario as well that whether those pixels
can be classified at one type of land cover or another type so these mixed scenarios might
be there in certain areas
So if a like 2 examples are given 1 is minimum distance classification based on the training
set which we have discussed earlier Then this is how the output map that is land cover map
will look but instead of using a different algorithm rather than minimum distance classification
a maximum likelihood which is based on the probability then this is how the map will
look so at several places there are some changes one can notice specially like a this set of
areas in this one are much more in maximum likelihood which have been classified here
less
And maybe about this areas this is the Mercyland which is less classified here but more uniform
classification has been done Now how to how to judge that which one is the better so again
one is one wants to really check that which one out of these four three four (ba) algorithm
based classification is giving better results than it is always good to go again in the
field and check whether the it is the same way it has been classified or there are some
spectral or there are some mismatches and then one can come back again and correct it
so these in this way the classification accuracies can be improved
Now initially if we put in 2 categories 1 is unsupervised classification and supervise
supervise we have discussed now come to the unsupervised classification which is completely
a software based or computer based classification Our input is required whether we want to classify
that map or that satellite image in 8 classes 7 classes 5 classes
Now this input will come through our image interpretation skill that we interpret the
image and find that there are major 8 classes suppose so then we will assign to the computer
that classify this image based on the 8 categories of different land cover so but the concept
wise rim that you can think that a in 3D these are the these are the these are the plots
then initially we assign spectral class 1 spectral class 2 that means the clustering
of the pixel values in a 3D space and based on that then later on you can assign different
clusters of different colors ultimately which will come to different category of land cover
So here the the basic assumption that we don’t have any prior knowledge about the area so
local information is not available so computer what the software will do it will group all
pixels according to their spectral relationships and look for natural clustering so wherever
it will find the cluster it may assign 1 class Now you have to identify whether the class
it has assigned is a water body or not and in (convence) in natural convention we will
assign blue color to the water body so in this way we achieve unsupervised classification
So here assumes that the date data in different cover class will not belong to the same grouping
and once created once we have given this to the computer the analyst later on assesses
the utility and can adjust a clustering parameter so later on once we realise that water body
is being or bear soil is being classified along with the sand bodies but then some adjustment
or accuracy can be introduced if I am share that computer is wrongly classified so based
on their spectral characteristics these things can be adjusted later
So after a when this comparison is done the reclassified image based on the spectral classes
to ground reference data the analyst can determine which land cover type the spectral class correspond
to and has the advantage over this technique and supervise classification is having advantage
over supervise because here it is done by the computer so sometimes we might be in supervise
classification we might be choosing a wrong area as a training site
Training site has to be a very exclusive area when are sure that this is definitely is dense
forest or bare soil or you know bare rocks or water body sand body then only it would
assign so we there we are introducing a bias to the system but in case of unsupervised
classification we are at initial stage no bias being introduced except that we are giving
number of classes so that image can be classified But both are both types of classifications
are having their advantages and disadvantages and this clustering algorithms in unsupervised
classification includes like K means textural classification and other thing
This is the same image was subjected to the same number of 50 number of classes these
are the clusters Now like if I realise that this is the sea water so instead of mentioning
cluster I will just put a remove this and I will put a sea water and similarly if I
find this color is showing the high sediment concentration then I will say the you know
turbidity or high sediment concentration
If I find that this is a water body then I will assign easily water or whatever if I
find that the green color is the forest area then I will and how I will find very simple
you you will have one classified image and same time you will have a color composite
so by comparing that one you can assign a to this different clusters different classes
you may merge you might realise So initially what we do in case of unsupervised classification
the best practice is to classify in low number of classes than you think so I realised that
probably there might be only 8 classes So first I will instructs the system to classify
in 12 classes then I can do the grouping as well and that will make sometimes this iteration
makes the classification results much better
There is another simple unsupervised classification technique on a single band if we perform or
maybe at a color composite can be performed which is called slicing or density slicing
Generally it is performed on a single band that is why grey levels or digital numbers
distributed along X axis of an image histogram are divided into series of analyst specified
interval of slices so it is something like like you are having loaf of bread
Now you want to classify on equal thickness of slices of the bread or maybe depending
on the distribution that means the histogram based on the frequency you want to classify
or make the different thickness of the bread as per the requirement So all digital numbers
pixel values falling within a given interval are displayed as a single DN that means a
different color in the output image and the process of converting the continuous grey
tone to an image into a series of density intervals or slices which is correspond to
a specified in specific digital image
Like for example here that pixel values within 1 band band 6 example here or that pixel values
within 0 to 15 should be assigned red color 16 to 32 should be assigned 32 and green color
and so on so forth
So again this is a simple discretisation instead of having a continuous image the same image
is being classified based on so this this distribution of or the thickness of the width
maybe different depending on depending on your requirements or you may involve some
statistical techniques like equal interval or natural breaks or a quintile there are
many such methods are available which can be used to classify a single band image
There are some other classification techniques are evolving recent times they have become
popular which are pixel based so we compare with these object oriented classification
versus pixel based classification so far what we have been discussing is pixel based
Now so we can improve on this in the past most digital image classification were based
on the processing the entire scene pixel by pixel So instead of now we may thing about
identifying the objects and then going for classification so object oriented classification
techniques allow the analyst to decompose the scene into many relatively homogenous
image objects revert as a patches or segments using a multi resolution image segmentation
process So this brings to the end of this presentation thank you very much