Characterization of homesteads in India using C band Synthetic Aperture
Radar data
Varunika Jain, C Patnaik and S Panigrahy
EPSA
Space Applications Centre, Ahmedabad, INDIA
Contact details: cpatnaik@sac.isro.gov.in
Phone: 91 79 26914037
Fax: +91 79 26915846
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Characterization of homesteads in India using C band Synthetic Aperture
Radar data
ABSTRACT
The rural settlements in India are complex in their composition consisting of built-up area,
gardens around homes with miscellaneous trees. The homesteads which are basically
settlements have an impact on the agrarian set-up. The homestead pattern and composition in
India is undergoing rapid changes due to the population pressure. With expanding population,
the agricultural lands are used up for these settlements. In order to map these homesteads a
methodology has been suggested in this paper. Attempt has been made to characterize them
based on the vegetation density as a function of the backscatter. Multi temporal Radarsat-2
SAR Wide 2 beam data has been used and the role of dual polarization SAR data has been
explored. The cross polarization ratio has been used along with temporal HH data. Results are
encouraging and show potential to map and characterize homesteads in India.
Keywords: Homesteads, settlements characterisation, backscatter, cross polarization,
Vegetation,
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1. Introduction:
In the Indian landscape, one encounters two distinct types of rural settlement pattern. In many
parts, the rural life is the nucleated village, a compact collection of dwellings. In other parts,
the so called “village” area may contain separate clusters of homesteads. The term
“homestead” has been variously defined. Homestead means a dwelling house and land on
which it stands, together with any attached garden, orchard or and out buildings used for
purpose of horticulture or agriculture and any tank. (Dey, 1993). In subsistence economy, a
sizable part of family income in real terms comes from homestead and plays a significant role
in family nutrition and social status (Marsh, 1998, Hanstad and Lokesh, 2002). In Indian
scenario, the homestead trees contribute significantly to total tree cover. Homestead trees now
forms one of the nine categories of “Trees outside Forest” being surveyed by Forest Survey of
India. The homestead pattern and composition in India is undergoing rapid changes due to the
population pressure. As the population increases, new households are formed, either annexed
to the existing homestead or formation of new homesteads in agricultural fields. Preference to
road/canal or other utility services modulates the homestead pattern in an area. Knowledge of
the spatial pattern of the homestead is thus essential for many of these studies.
In this context, the satellite remote sensing data has a significant role to play, particularly that
of microwave data. The sensitivity of radar data to dielectric and geometric property has been
found to have an advantage in detection of settlements. The agglomeration of di- and trihedral
corner reflectors in urban environments makes these regions stand out as clusters of more or
less bright signal returns in radar data. This effect has been used for monitoring the urban
footprint (Haack, 1984; Henderson, 1995, Henderson and Xia, 1997). However, the highly
variable nature of the urban landscape enhances the complexity and multifarious nature of
interactions between urban features and radar signals.
The effects of some of the landscape factors on the appearance and interpretability of
radar images in general have been reviewed previously by Fung and Ulaby (1983), Simonett
and Davis (1983). Both theoretical considerations and empirical observations indicate that the
cross-polarized imagery is less susceptible to the specular return from dihedral and trihedral
reflectors (cardinal effect) that is apparent on the like-polarized imagery. On like-polarized
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imagery, similar land use types often appear dissimilar, or vice versa. However, numerical
analysis of pixel values and use of automated or manual interpretation strategies have led to
mixed conclusions as to the preferred (like or cross) polarization. The applicability of textural
information using polarised and/or multifrequency radar data have proven to be particularly
valuable for urban study. In general large collections of structures with relatively little or no
vegetation appear quite visible on HH polarization, while HV polarization would be preferred
in analyzing the other land uses within the urban area, particularly vegetative area. In recent
years, object-oriented approaches are being investigated (Hofman, 2001, Thiel et al., 2008).
These more sophisticated techniques provide possibilities to describe and utilise the geometric,
textural and especially contextual properties of the real-world objects in the classification
process (Henderson and Xia, 1998). However, most of the earlier studies were more
experimental in nature, based on data available on opportunity. This paper highlights the
results of a study on delineation of the rural settlement/homesteads using multidate C band
SAR data.
2. Study area and Data used:
The study area was spread over five blocks (Ketugram 1, Ketugram II, Katwa I, Katwa II and
Mangalkot ) in rural Bardwan district, West Bengal in the east coast of India. Radarsat-2 Wide
2 Synthetic Aperture Radar data obtained in HH and HV polarization was used (Table-1).
Three date data acquired during May 05, 29 and June 22, 2010 were used.
3. Methodology:
3.1 Data set preparation
The processing of multi-temporal Radarsat data for this study involved: i) data download; ii)
speckle reduction; iii) data calibration; iv) image georeferencing, v) multidate image coregistration. Work was carried out using PCI Geomatica Image processing software. The
detailed steps are:
Data Download:
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The acquired SAR data was in 16-bit as .tiff format. It was imported alongwith the
ephemeris data. The Ground Control Points (GCPs) of the respective scenes are stored
in a separate .xml file called Product.xml and the calibration coefficients are stored in
another .xml file called lutSigma.xml. Necessary xml parsers were developed to extract
the GCPs and the calibration coefficients from the respective files.
Speckle was suppressed using Enhanced Lee Filter with window size 5*5.
After suppressing the speckle by filtering, the gain scaling values provided across the
range were used for converting slant range data to ground range where the feature wise
backscatter could be quantified. The equation used for calibration is as follows:
σ° (in dB) = 10* log10 (DN2 + A0)/Aj ))
where DN is amplitude value from the raw data
A0 is offset value, usually 0
Aj is the gain scaling coefficient.
The incidence angle component is built into the gainscaling coefficient and hence not
computed separately.
This calibrated data was georectified and used for analysis..
Image co-registration:
The calibrated data need to be stacked together to generate multi-temporal data for use
in the study. The output co-registered and georectified image contained the three multitemporal data at 10 meter pixel spacing. A second order polynomial with cubic
convolution resampling was adopted for this.
3.2. Classification
Rationale of Approach
Settlements are composed of combinations of natural elements and elements of the built
environment of the cultural landscape constructed from a variety of bio- and
geophysical materials (wood, mud, concrete, metal and stone). These built landscapes
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gives rise to more or less bright signal returns. This effect can be used to map the
settlements. However, many environmental variables affect this bright signal viz, type,
amount, and pattern of vegetation cover, adjoining land cover classes and patterns, and
seasonal changes. This constraint can be overcome to some extent by using multi-date
data to discriminate the invariant nature of the settlements from its surrounding land
cover (agriculture crops, water, fallow fields etc). In addition, scale, and image
processing and enhancement techniques can also have significant effects on the
delectability of settlements and the accuracy of mapping.
Formation of decision rules
Ground-truth locations of homesteads/settlements derived using high resolution optical
data were used to form the decision rule.
The backscatter signature of
settlements/homesteads in single and multi-date data was analyzed. Single date and
multi-date combinations were evaluated and the best set of data was used for
classification. The accuracy of classification was further evaluated using blind site
approach.
3.3 Characterization based on vegetation proportion/density
The cross polarization ratio (XPR i.e. HH/HV) gives an idea of the type of vegetation
that is found under a given area. The XPR was computed for the first acquisition as
vegetation under homesteads does not change during short intervals of around 50 days
when all the acquisitions took place. After demarcating the homesteads from the rest of
the image, the XPR values were used to categorize the vegetation for these homesteads.
3.4 Final map and statistics
The settlement and homesteads were segregated based on area in hectares and 5 classes
were generated. The classes catered to the small, medium and large size of homesteads
and their individual components. The frequency of each class was also noted.
4. Results and discussion:
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The response of homesteads to SAR could be put under two broad categories viz. i)
core man made structural area and ii) vegetation interspersed with the man made structures.
This vegetation may be sparse (SV) or dense (DV) Preliminary analysis of the data set
showed that in HH polarization, the settlements and the core area of homestead showed high
backscatter and clearly identifiable from other classes.. This was attributed to the double
bounce effect caused by the dihedral corner reflector like properties of building materials of
huts, farm sheds and other constructed objects. However, the same was not observed in HV
data as backscatter of all land cover classes overlapped. Fig 1 shows the single date HH and
HV data over a part of the study area highlighting this difference. Further analysis using
known boundary of the homesteads showed that the areas which were surrounded by
vegetation or dwellings interspersed by home gardens/orchards showed a lower backscatter
range, thus mixed with other classes like agriculture/scrub land etc. In case of homestead areas
with dense vegetation (sparse built-up), the signal is more of vegetation. The core area of
settlements composed of more built up structures gave a constant high backscatter of > -1 dB
in all the dates (Fig.2). In the fringe areas interspersed with vegetation or homesteads with
sparse vegetation, the backscatter varied from -2 to -4 dB, and in homesteads with dense
vegetation, the backscatter varied from -4 to -7 dB.
Table-2 shows the mean and standard
deviation of the different settlement categories in three date SAR data.
The core settlements appeared very bright in a two/three date color composite image (HH
polarization) due to high backscatter in all the dates (Fig. 2). HV data showed overlap of
signature in all classes. Thus, it was not found suitable for classification. Only HH data was
used for classification. Use of any single date data resulted poor classification accuracy of
homesteads. Use of two date data (HH polarization) resulted around 70 per cent classification
accuracy, which improved to around 85 percent by using three date data (Fig.3, Table-3).
4.1
Improvement in mapping using GIS
The pattern of homesteads/settlements as classified using SAR data resulted mainly isolated
clusters as the continuity was disrupted by misclassification of vegetated areas. Similarly, the
fringe areas of core settlements with homestead vegetation mixed with adjoining agriculture
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fields. Thus, it was essential to include these areas while mapping. This exercise was carried
out using GIS buffer approach through proximity analysis.
From a large sample of training
sites, it was found that the mean distance of fringe area of homesteads from core settlements
was around 200 meters. This was considered as a threshold to buffer the core clusters to map
the settlement/homesteads. This buffer was then rendered into a single cluster by merging.
After merging, the threshold was now subtracted form the single cluster. This resulted in an
irregular cluster while taking the peripheral areas and the boundary matched well with that
derived using high resolution optical data (Fig. 4, 5).
Mean and standard deviation of backscatter from homesteads for the three acquisitions were
compared, and similar values were observed for all the three acquisitions.
Therefore to
characterize homesteads based on vegetation density, single date HV data was used. The
difference of HH and HV backscatter values, also known as cross polarization ratio, was used
to characterise vegetation density. To categorize vegetation as dense limits of 6 dB to 10 dB
were used. Medium dense vegetation falls into two categories with limits of 2 to 6 dB and 10 to
15 dB. Sparse vegetation was segregated using limits 15 to 23 dB. This was applied with in the
settlement boundary. A qualitative homestead vegetation categorization could be obtained by
this. Analysis showed that around 25 per cent area were under sparse vegetation and 12 per
cent under dense vegetation, the remaining fall into core area.
5. Conclusion
The rural settlements in India are complex in their composition consisting of built-up area,
home garden with miscellaneous trees, small orchards etc. and are referred as homesteads. The
core settlement area with built-up structures causing double bounce scattering in HH
polarization data gave a high backscatter in all dates. However, no such specific signature was
observed in case of HV polarization data. The accuracy of identifying the settlements improved
with two or three date data (HH) by including settlements with sparse vegetation areas.
However, the dense vegetated settlement areas were found to have poor classification
accuracy. This resulted in isolated clusters of settlements. Thus, a GIS based buffer technique
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was developed to include these areas and merge isolated core settlement patches to reconstitute
the homestead boundary. A buffer of 200 meters was found to produce optimum result
compared with the base map prepared using high resolution optical data. This study shows that
Radarsat SAR Wide 2 beam data with large swath has the potential to be used for large area
application to map the rural settlement/homesteads in India. This has the advantage over high
resolution optical data in terms of large swath and calibrated backscatter signature that offers a
common algorithm to detect these features. The HV data though not suitable for identification
of the settlements, was found to be useful to characterize the homesteads on the basis of
vegetation density, by using the cross polarisation ratio. However, further investigation is
required to quantify this aspect.
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6. Acknowledgements:
Authors are grateful to Dr M Chakraborty, Group Director, ATDG/SAC for his
guidance. The Radarsat data used in this study is due to courtesy of the national project
FASAL.
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7. References:
Ahmad N, 1956, The pattern of rural settlement in east Pakistan, Geographical Review, 48 (3)
Dey, N. K., 1993, Homesteads and Orchards in India, Mittal Publications, New Delhi (ISBN
81-7099-510-8), pp 3-5.
Fung A K and F. T. Ulaby, “Matter-energy interaction in the microwave region,” Manual of
Remote Sensing, 2nd Edition, D. S. Simonett and F. T. Ulaby, Eds., American Society for
Photogrammetry, Bethesda, Maryland, 1983, ch. 4, pp. 115–164.
Haack, B.N., 1984. L- and X-Band Like- and Cross-Polarized Synthetic Aperture Radar for
Investigating Urban Environments. Photogrammetric Engineering and Remote Sensing, 50(3),
pp.331-340.
Hanstad T and Lokesh S.B., 2002, Homestead Plots as Land Reform: Analysis from West
Bengal, RDI (Rural Development Institute ) Reports on Foreign Aid and Development, No.
115, 10-11
Henderson, F. M., 1995, “An analysis of settlement characterization in central Europe using
SIR-B radar imagery,” Remote Sensing of Environment, vol. 54, no. 1, pp. 61–70.
Henderson, F.M. & Xia, Z., 1997. SAR applications in human settlement detection, population
estimation and urban land use pattern analysis: a status report. IEEE Transactions on
Geoscience and Remote Sensing, 35(1), pp.79-85.
Henderson, F.M., Xia, Z.G., 1998. Radar Applications in Urban Analysis, Settlement Detection
and Population Analysis.Principles and Applications of Imaging Radar (F.M. Henderson and
A.J. Lewis, eds.), Chapter 15. New York, pp. 733-768.
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Hofman, P., 2001. Detecting urban features from IKONOS data using an object oriented
approach. In: Remote Sensing & Photogrammetry Society (Ed.): Proceedings of the First
Annual Conference of the Remote Sensing & Remote Sensing Society, 28–33.
Marsh, R, 1998, Building on Traditional Gardening to Improve Household Food Security,
Food, Nutrition and Agriculture No. 22, at 11 (Food and Agriculture Organization 1998),
available at tp://ftp.fao.org/docrep/fao/X0051t/X0051t02.pdf6
Simonett D. S and R. E. Davis, “Image analysis—Active microwave,” Manual of Remote
Sensing, D. S. Simonett and F. T. Ulaby, Eds., 2nd Edition, American Society for
Photogrammetry, Bethesda, Maryland, ch. 24, 1983, pp. 1125–1181.
Thiel, M., Esch, T. & Schenk, A., 2008. Object-Oriented Detection of Urban Areas from
TerraSAR-X Data. In The International Archives of the Photogrammetry, Remote Sensing and
Spatial Information Sciences.
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Table-1 Specifications of SAR data used
Table-2 Mean backscatter of different settlement classes in three date SAR data
Table-3 Confusion matrix of training site pixels showing the classification accuracy of the homestead
classes in three date HH polarization data
Table- 4: Size-wise distribution pattern of settlement/homesteads in the study area derived
using SAR data
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Table-1 Specifications of SAR data used
Beam Position
W2
Data type
16 bits
Beam mode
Descending (0600
AM local
time)
Incidence angle range
30-39 (degrees)
Polarisation
HH, HV
Pixel Spacing
12 m
Product type
SGF
Swath
150*150 km
Table-2 Mean backscatter of different settlement classes in three date SAR data
Site
Date of Acquisition
no.
(2010)
1
2
3
Feature
Mean
Std. Devn
(dB)
(dB)
May 05
Core Settlement 0.54
1.52
May 29
Area
2.22
1.77
June 22
2.61
1.93
May 05
Homestead with -5.2
0.93
May 29
dense
-4.26
0.94
June 22
Vegetation
-5.39
0.59
May 05
Homestead with -3.57
1.26
May 29
sparse
-2.43
1.49
June 22
vegetation
-2.67
2.16
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Table-3 Confusion matrix of training site pixels showing the classification accuracy of the homestead
classes in three date HH polarization data
Feature
Null Core
Homestead
Homestead
Settlement
sparse
dense
Water
Agriculture
Scrub
Settlement
2.0
90.6
6.0
1.4
0.0
0.0
0.0
Homestead
4.9
22.4
27.0
36.3
0.0
4.9
4.5
0.4
0.9
11.6
72.5
0.0
1.7
12.8
Water
1.4
0.0
0.0
0.0
98.6
0.0
0.0
Agriculture
1.0
0.0
1.0
1.9
0.0
88.9
7.3
Scrub
1.4
0.0
1.4
5.0
0.0
5.3
86.8
– sparse
Homestead
– dense
Table- 4: Size-wise distribution pattern of settlement/homesteads in the study area derived
using SAR data
Area (Ha)
<1
1-10
10-50
50-100
>100
Frequency
53
147
96
13
2
%
17.0
47.3
30.8
4.2
0.7
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Fig.1. Single date HH and HV polarization data over a part of the study area showing backscatter of
settlement/homesteads
Fig.2 Mean backscatter of settlement (core),homesteads (dense vegetation-DVA) and sparse vegetation
(SVA) in three date SAR data.
Fig.3. (a) and (b) showing two and three date color composite of HH data. The core settlement area
appear white due to high backscatter in all the dates, (c) three date classified image; the magenta color
indicates the pixels that were not classified in two date data, while present in three date data.
Fig. 4: (a) Three date FCC of HH polarization data and (b) the homestead map derived before and after
application of buffer (green and red color).
Fig 5: The final settlement boundary (red color) obtained using GIS approach and the reference
boundary (blue color) derived using high resolution optical remote sensing data along with (a) three
date HH SAR and (c) true color composite optical data.
Fig. 6. Three date HH polarization SAR data FCC showing the settlement/homestead class in Yellow
color and the final homestead/settlement map showing size-wise distribution pattern for the study area.
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HH polarisation
Hv polarisation
Settlements
Fig.1. Single date HH and HV polarization data over a part of the study area showing backscatter of
settlement/homesteads
5
Backscatter in dB
3
1
-1
-3
Core
DVA
SVA
-5
-7
0
1
2
3
Acquisition Number
4
Fig.2 Mean backscatter of settlement (core),homesteads (dense vegetation-DVA) and sparse vegetation
(SVA) in three date SAR data.
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(a)
(b)
(c)
Fig.3. (a) and (b) showing two and three date color composite of HH data. The core settlement area
appear white due to high backscatter in all the dates, (c) three date classified image; the magenta color
indicates the pixels that were not classified in two date data, while present in three date data.
(a)
(b)
Fig. 4: (a) Three date FCC of HH polarization data and (b) the homestead map derived before and after
application of buffer (green and red color).
(a)
(b)
(c)
Fig 5: The final settlement boundary (red color) obtained using GIS approach and the reference
boundary (blue color) derived using high resolution optical remote sensing data along with (a) three
date HH SAR and (c) true color composite optical data.
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Fig. 6. Three date HH polarization SAR data FCC showing the settlement/homestead class in Yellow
color and the final homestead/settlement map showing size-wise distribution pattern for the study area.
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