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EXTRACTION OF URBAN FOOTPRINT OF BENGALURU CITY USING
MICROWAVE REMOTE SENSING
Deepthi R1, *, Sudha Ravindranath2, K Ganesha Raj3
1Bharati Vidyapeeth Institute of Environment Education and Research, Pune, India - deepthi1911@gmail.com
2,3Regional Remote Sensing Centre – South, National Remote Sensing Centre,
Indian Space Research Organisation, Bengaluru
Commission V, SS: Infrastructure and Development Planning
KEYWORDS: SAR (Synthetic Aperture Radar), Polarization, Urban, eCognition, SNAP
ABSTRACT
Monitoring the urban development/change is of critical importance in planning the future infrastructure of a city. The use of
satellite images in urban related studies has yielded in exemplary results. The city of Bengaluru, with high variation in urban
landscape is most suited for this study. In this paper, the potential of the SAR imagery in understanding and characterizing
the urban features is studied. The SAR images have unique characteristics such as double bounce and corner reflectors which
are prominent in an urban landscape. The diverse urban features are characterised by comparing the graphs derived from the
image statistics of temporal Sentinel -1 dual polarized data. For the generation of the urban footprint a rule based approach
and an object oriented approach has been implemented in this study. The stack of coherence image and synthetic bands
derived from image statistics of the VV polarization is used as the input image for the same. The final urban footprint is
derived by the comparison of the output from both the methods. The results are authenticated with the urban footprint
obtained by optical imagery of the same area for better understanding and improvement of the algorithm. The observations
are made regarding the contribution of SAR in the study of urban features and the feasibility of implementation in the
mainstream analysis.
1. INTRODUCTION
The urban footprint is defined as the overall extent of a
city comprising of urban man made features including
buildings and transportation networks. The urbanization
is happening at a very fast pace in recent times and the
growth of the city has to be monitored for better
planning. The acquisition of the urban footprint from the
satellite image is a well-established approach. With the
advent of the SAR imaging techniques the utility of using
the same has been explored and exemplary results have
been obtained. Detection and analysis of settlement
patterns, estimating population, and monitoring
population migration are vital to accurately assess the
impact of human activities on the environment. The SAR
images can be used in the applications of settlement
detection, population estimation, and assessment of the
impact of human activities on the physical environment.
The SAR image is very useful in distinguishing the
buildings from the other features such as transportation
networks (roads, railway) and barren land such as
grounds in a city.*
The property of corner reflectors and double bounce
effects are very prominent in the SAR image of the urban
*Corresponding author
landscape. The corner and the dihedral reflectors are a
characteristic features which depict the buildings in the
SAR image. All these factors are main contributors in
understanding and analysis of urban features and
buildings in SAR imagery.
1.1 STUDY AREA
Bengaluru city is located in the state of Karnataka in the
southern part of India.
Figure 1: Study area map of Bengaluru city
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-5-735-2018 | © Authors 2018. CC BY 4.0 License.
735
It is situated at an elevation of 914.4m. The city spans
about 712 Km2in area. The population census of the city
in 2018 was found to be around 10 million .Bengaluru
city is famously known as the silicon valley of India and
has employsaround 35% of India’s 2.5 million IT
professionals. It is also fondly known as the garden city
due to the greenery and well known parks such as
Lalbagh and Cubbon Park.
1.2 RESEARCH QUESTIONS
Floyd M Henderson (1997) et.al[3]this paper focuses in
understanding the factors influencing the SAR imagery
of the urban features. The important categories that
influence the settlement visibility in the SAR image are
category or the type of building function, construction
material, surrounding land cover, wavelength
polarization look direction and incident angle. This paper
explains the practical utility of using SAR images in the
extraction of urban features i.e. the urban and socio-
economic indicators can be derived from the SAR images
which are used by planning commission.
The aim of this study is to understand the potential of
microwave images to study the urban features. The
properties of the urban features in the VV and VH
polarization are studied by statistical analysis. The SAR
image is very useful in distinguishing the buildings from
the other features such as transportation networks (roads,
railway) and barren land such as grounds in a city. The
property of corner reflectors and double bounce effects
are very prominent in the SAR image of the urban
landscape.The following research questions are
considered by the above stated understanding:
1. What is the statistical analysis and characterization of
responses of urban features in SAR?
2. What is the accuracy of an urban footprint generated
from SAR data and the optical data of Bengaluru city
and comparison of the same?
1.3 LITERATURE REVIEW
Anthony Lewis (1968) et.al [1] this paper briefly
discusses about the various polarization of RADAR
imaging and the influence of the same on land use
features. The paper gives a clear cut idea on which
polarization should be used for the studying different
landscapes like urban, rural, transportation networks etc.
In rural environments cross-polarized imagery (HV, VH)
is generally better for defining cultural objects such as
farmsteads and transportation networks. Whereas in
urban the buildings are more clearly delineated in the like
polarization than otherwise. The orientation of the
building, roof material and height of the building are
contributing factors in the image. For the detection of the
roads and railway both cross and like polarization have
given good results however this varies with the
morphology of every study area.
Fabio Dell’Aqua and Paolo Gamba( 2003) et.al [2] In
this paper different textural parameters were analyzed for
a single date SAR image. The texture parameters such as
contrast, correlation, dissimilarity, entropy, mean,
homogeneity and second moment. The various texture
parameters values were analyzed for different urban
features. The urban density maps were derived after
characterizing the urban features by texture. Multi-
temporal SAR data was analyzed for the same and the
density maps were derived. The consistency was
observed in the density maps. This paper gives a clear
understanding about the influence of the texture
parameters in the SAR image and implementing it in the
classification algorithms.
ThomasEsch (2010) et.al [4] this paper presents an
approach toward semi-automated detection of built-up
areas based on single polarized TSX images. The method
includes specific pre-processing of the SAR data and an
automated image analysis procedure. The pre-processing
focuses on analysis of local speckle characteristics in
order to provide a texture to highlight built-up areas.
Object – Oriented image analysis is used to automatically
extract the built-up areas using the texture and the
intensity layers.
D G Corr (2003) et.al [5] this paper discusses the
approach of object oriented image analysis and
segmentation techniques on SAR images. The
methodology utilized is the through the generation of
coherence images from temporal complex SAR images.
This was complemented by the information from
polarimetric decomposition of the data into entropy and
anisotropy. To perform further classification of features
as urban fuzzy membership classes were defined within
the eCognition software. The prominence of the
relationship between the building orientation and radar
look direction is highlighted with substantial ground
data. Considering the above stated factors a hierarchical
approach has been implemented in this paper for the
extraction of urban area.
Junichi Susaki (2015) et.al [6] this paper compares the
potential of extracting the urban areas from L band and X
band fully polarimetric SAR images. For the analysis of
L band images the POA (Polarization Orientation Angle)
volume scattering (Pv) and total power (Tp) data is used
for classification. For the analysis of X band images a
thresholding approach of the total power and volume
scattering values is used. It was found that in
mountainous region the X band had poorer returns than
the L band. It was concluded that theusage of Tp and Pv
for the classification of urban features from SAR data.
Andy Reiu (2017) et.al [7]the aim of this study was to
see how precisely the urban areas can be mapped using
interferometric coherence on Sentinel -1 satellite imagery
with different conditions. The conditions are the time
separation of 3 months and 1 year, ascending and
descending orbits, winter and summer data. This study
has used the image sets off Sentinel -1 SLC data.
Another important aspect of this study is the
classification of urban and non-urban areas that would
lead to time-saving improvements when using manually
derived rules for building detection. The major
conclusion was the accuracy of the urban maps extracted
from the coherence image; it showed that a time
separation of 3months has higher accuracy than 1 year.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-5-735-2018 | © Authors 2018. CC BY 4.0 License.
736
2. MATERIALS AND METHODS
2.1 DATA COLLECTION
2.1.1 Satellite Images
Sentinel – 1SLC SAR data and Sentinel – 2 optical data
both are acquired with a spatial resolution of 10m was
used in the course of this study which includes the
following three datasets:
a) Sentinel – 1A SLC SAR Image dated 13th October
2017.
b) Sentinel -1A SLC SAR Image dated 5th January 2018.
c) Sentinel – 2AOptical Image dated 12th January 2018.
13th Oct 2017 5th Jan 2018
Figure 2: Sigma Naught Sentinel -1A SAR Images
Sentinel -2A Optical Image
Figure 3: Satellite Image and GIS generated ward boundary
referred in the Study
2.1.2 Ancillary Data
1. Ward Boundary in the form of attribute table and
vector layer generated using GIS tools.
2. Vector layers of transportation networks (roads and
railway lines) of the city.
2.2 METHODOLOGY
2.2.1 Characterization of responses of urban features
in SAR Data
The statistical approach is taken to understand the
backscatter response from the urban features. The
Backscatter is dependent on the dielectric constant and
the surface roughness of the feature at interest. The
mean, max, min and standard deviation are considered in
the statistical analysis. The features considered in this
methodology are barren, parks, lakes, lakes with weed,
agricultural patch, transportation networks (roads and
railway) and types of built-up. For each of the feature the
statistics are analysed and conclusions are drawn on as to
why the values of the feature are so. A graph is derived
from the statistical analysis for each of the polarization
(VV/VH for Sentinel – 1). The graph is a clear
representation of the interaction between the features of
interest and the polarization. The values derived from the
above analysis are used in the next objective to extract
the urban footprint and also in the final objective in the
feasibility in delineating individual buildings.
Figure 4: Methodology Flowchart of first objective
2.2.2 Generation of urban footprint using inputs from
SAR and Optical data.
For the extraction of the urban footprint from SAR data
dual date Sentinel – 1 SLC (Single Look Complex) is
used. The SLC data is the complex data which contains
the phase and amplitude intensity. Single Look Complex
(SLC) products are images in the slant range by azimuth
imaging plane, in the image plane of satellite data
acquisition. Each image pixel is represented by a
complex (I and Q) magnitude value and therefore
contains both amplitude and phase information. The
phase information in the SLC images is used to generate
the coherence image. The prerequisite of the creating a
coherence image is a set of Sentinel – 1 SLC products
with any measurable time difference. Generating the
coherence image is process intensive the methodology
for the same is written the annexure. The synthetic bands
are generated using the understanding and the conclusion
derived from the first objective of the statistical analysis.
The synthetic bands generated are of the mean and the
difference statistics. The three images of coherence and
the two synthetic bands are layer stacked to make a
combined image. To generate the urban footprint a rule
based approach and an object oriented approach is
considered. The rule based approach uses a conditional
algorithm considering the values of coherence and mean.
It is found that in urban areas with features such as
buildings the coherence values are generally high or
nearing to 1, since the features rarely change with time.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-5-735-2018 | © Authors 2018. CC BY 4.0 License.
737
The mean values of Sigma naught considered for urban
features are shown to be mostly ranging in positive
values. The footprint using the rule based approach is
derived from the SNAP software. For the object oriented
approach a segmentation algorithm is run with a nominal
shape and compactness value on the layer stacked image.
A thresholding algorithm is run with conditions on
coherence and mean image. The footprint is derived as a
vector. The final urban footprint is derived by taking
inputs from both the procedure.
The urban footprint is generated using the Sentinel – 2
optical data for comparison and improvement of the
results derived from SAR data. The Sentinel – 2 optical
data has the SWIR band which is very useful in the
monitoring of urban features. An urban index is used
using the SWIR and NIR band to delineate the urban
features. The urban footprint derived from all the three
methods are compared and the justified.
Figure 5: Methodology Flowchart of second objective
3. RESULTS AND DISCUSSIONS
3.1 Characterization of responses of urban features in
SAR Data
The below are the graphs derived from the analysis done
from the first objective. The graph derived from the
statistical analysis that is implemented in this study for
the characterization of the urban features gives a clear
idea about the interaction of SAR wavelength and
polarization of land features.
Figure 6: Sigma Naught values of various features
The graph derived from the statistical analysis of urban
features except buildings using the 10m Sentinel -1 dual
pol data. The features considered here are barren, parks,
lakes, lakes with weed, agricultural patch, transportation
networks (road and railway). The sigma naught values
are all negative for these features as they do not reflect
the incident energy more. The above features mostly act
as smooth surfaces to the incoming incident energy and
hence the specular reflection occurs. Since the
backscatter values for all the above mentioned features
are negative there is a chance in overlap of classification
of features. Hence other properties such as surface
roughness should be considered to distinguish between
the features.
Figure 7: Sigma Naught Values of Built-up
The primary observation in this graph is the positive
values for the VV polarization data of different built-up
areas (The backscatter values are most likely to be high
in like polarized return than cross polarized return; this is
due to the fact that when the transmit polarization is
vertical/horizontal then generally the land features return
the incident energy in the same polarization as received,
there has to be a depolarizer as the target to give a bright
return in the cross polarization which is a rare
phenomenon in land use features, hence the negative
values of cross polarized data). This is the graph derived
from the statistical analysis of different types of built-up.
The type of built up considered here are industries, high
rise apartments, tech parks, dense residential complexes
with have a North-South orientation and dense residential
complexes having an east west orientation. In the
analysis of interaction of SAR incident energy with the
urban features the major characteristics are the double
bounce effects and corner reflections. The RCS of the
double bounce is dependent on the area of the building
wall and its height. The higher the building, the stronger
the double bounce. Hence the sigma naught values for
Tech Park, high rise apartments and industries have very
high positive values ranging from 5.32 – 7.68db. Since
the industries represent a sparser distribution and with
metallic roofs the double bounce scattering are generally
less for this feature. However, since the data considered
here is of 10m resolution the industries appear to be in a
cluster giving a combined high backscatter. The
orientation of the building with respect to the direction of
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-5-735-2018 | © Authors 2018. CC BY 4.0 License.
738
illumination is very important in understanding the
backscatter sigma naught values. In the above graph it is
clear that the dense residential area with EW orientation
have higher sigma naught values than the NS oriented
buildings. The EW oriented buildings are in the direction
(parallel) of the radar illumination and the NS oriented
buildings are perpendicular to the direction of
illumination. The RCS of the buildings parallel to the
direction of illumination is small and thus act as sharp
corner reflectors resulting in a positive backscatter value.
The buildings with perpendicular orientation to the
direction of illumination have more building area
exposed towards the incoming radiation. Since the
buildings are all aligned close to each other this will
result in the NS oriented building to behave as a specular
reflector thus dissipating all the energy.
3.2 Generation of Urban Footprint with inputs from
SAR and optical data
The below is the description of images and the urban
footprint derived from the analysis of the second
objective. The first approach used for deriving the urban
footprint using the Sentinel – 1 SAR data is obtained by
the generating coherence maps using two date Sentinel -1
single look complex data and synthetic bands using
statistical analysis. The phase information contained in
the complex data is used to generate the coherence
image. The statistical information of mean and difference
is used to generate the synthetic bands of mean and
difference image. A layer stack of the three bands are
used and a rule based approach is implemented using
conditional algorithm to extract the urban footprint. The
same is performed using OBIA.
The accuracy assessment is a very important part of the
image classification process. It is a statistical measure of
the exactness of the classified features. Accuracy
assessment is performed using various methods based on
the results obtained and the objective of the study. The
method of calculating the accuracy assessment in this
study is done using a grid method. The output is divided
into grids and random numbers are generated as per the
number of grids. The grids are chosen as per the random
number. The ground truth points (GT’s) are taken within
the grid created on the output derived from the first two
objectives. The result from the first objective is the urban
footprint of the Bengaluru city derived from two different
methodologies using SAR image. The derived footprint
is first checked with the same generated using the optical
Sentinel -2 data. The accuracy is checked for the three
urban footprints using the above mentioned grid method.
The accuracy of the footprint derived from the object
based approach is approximately 68.31% while the rule
based approach gave an accuracy approximated to 71%.
This lead to the conclusion that the OBIA procedure has
a satisfactory outcome on medium resolution SAR data.
The rule based approach gave a higher accuracy as it was
a pixel based approach and the land use features are less
confused in a SAR image in this approach. The accuracy
of the urban footprint derived from the optical data is
approximately 72.37%. The optical data proved to have
higher accuracy due the availability of the SWIR band in
the Sentinel – 2 data. The analysis of urban in SWIR
band has always given exemplary results.
Figure 8:Mean Image Figure 9: Urban Footprint
Figure 10: Layer Stack Image Figure 11: Coherence Image
Figure12:Urban Footprint from Figure 13:Accuracy Assessment
Optical dataset using grid method
4. CONCLUSIONS
This project gives a detailed understanding that the like
polarization (VV/HH) gives the optimum results in
analysing urban features. In this study since the data was
of coarse resolution the rule based approach gave better
results as compared to the object based approach. The
graphs explain the influence of dielectric constant and
surface roughness on the backscatter co-efficient sigma
naught. The values from the statistical analysis are used
to extract the urban footprint with inputs from the
coherence image. The urban footprint is generated using
the optical data for comparison and improvement of the
algorithm.The urban footprint derived from the SAR
coherence image is a very robust method to understand
the urban sprawl. It is a very fast process that clearly
delineates the built – up area of the city. The accuracy of
the pixel based approach is approximately 71% and the
OBIA was 68.31%. This method is not algorithm or
process intensive as it is in the processes using optical
data. The area of the urban footprint extracted from SAR
dataset is 762 km2 and that from optical is 810 km2.The
area obtained in optical is more due to discrepancy
caused in distinguishing barren and built-up. This
method can further be used in urban studies in creating
urban growth models.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-5-735-2018 | © Authors 2018. CC BY 4.0 License.
739
5. FUTURE SCOPE OF STUDY
The SAR images have great potential in urban studies
and can give exemplary and quick results. This study can
be extended for full polarization and hybrid polarization
SAR datasets. By utilising the VV, VH, HH and HV
bands novel urban extraction indices can be generated
that would give results with higher accuracy. Also, the
dual aspect images can be considered as it would
enhance the understanding of impact of radar
illumination direction on building orientation.
Interferometric analysis of the complex SAR images will
also provide highly valuable information through which
the 3D rendering of the buildings can be made possible.
ACKNOWLEGDEMENT
The authors thank Director, NRSC and CGM RRSCs for
encouragement. We thank Bangalore Municipal
Corporation for providing necessary data for carrying out
our research. Authors are thankful to Dr. V Poompavai,
Scientist/Engineer, RRSC-S Bengaluru
andBharatiVidyapeeth Institute of Environment Research
and Educaation for providing the necessary guidance.
Weextend our sincere thanks to the technical as well as
non-technical staff of RRSC-S for providing moral
support during the course of our work.
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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018
ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India
This contribution has been peer-reviewed.
https://doi.org/10.5194/isprs-archives-XLII-5-735-2018 | © Authors 2018. CC BY 4.0 License.
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