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ABSTRACT
Slums are becoming inevitable phenomenon of the urban fabric
in the developing world. Many cities in developing countries lack
detailed information on the emergence and growth of highly
dynamic slum developments. Due to the lack of spatial and
temporal data, informal settlements are often not spatially
documented. There are no maps indicating the position, patterns,
size, complexity and influence of the settlements. The challenge
therefore lies in having appropriate methods to identify and
monitor the spatial behaviour of informal settlements. One
possible solution is to use remote sensing imagery as the primary
data source. This project explores different approaches to extract
slum in which the gray level co-occurrence matrix(GLCM)
texture features are extracted from small individual blocks to
classify slums and formal built-up areas in very high resolution
satellite imagery ( WorldView-2 image of Madurai city). The
next approach employs the utility of homogenous urban patches
(HUPs) for which the information extracted from the GLCM
variance is aggregated for better accuracy. The result evaluated
using collected ground-truth information and visual image
interpretation shows that an accuracy of 81% is achieved.
KEYWORDS – Gray level co-occurrence matrix (GLCM),
Homogenous Urban Patches(HUP), texture features, slum
classification, accuracy.
I. INTRODUCTION
A slum is a collection of households living in close proximity
to one another in a number of buildings such that the
households share one or more deprivations of access to
improved water; access to improved sanitation facilities;
sufficient-living area; structural quality/durability of
dwellings; and security of tenure. According to UN-
HABITAT (2010),the world’s slum population is expected to
reach 889 million by the year 2020.The intent of the United
Nations Millennium Development Goal 7, Target 7D is ―to
have achieved a significant improvement in the lives of at
least 100 million slum dwellers‖ by 2020. Reliable
identification of slums and tracking of their growth has always
been a difficult task for urban administrators in the developing
world. There is no doubt that slum areas are expanding in
major urban centres of developing countries worldwide, but
significant challenges remain in evaluating them and
measuring expansion of built-up areas with scientific methods.
Although shape-based measures (fractal dimension,
lacunarity, mathematical morphology) and texture measures
(gray-level co-occurrence measures) have been used to
identify individual slum communities in the past two decades,
minimal progress has been made on development of
geospatial measures that can distinguish differences between
informal and planned settlements using readily-available very
high resolution (VHR) multispectral imagery. The United
Nations Global Urban Observatory project has acknowledged
there is a lack of data and an immaturity of applicable
methodology to measure durability of informal settlements
and also emphasized the need to ―be able to identify and
define slums spatially in a consistent manner to be able to use
geographical targeting for slum intervention programs‖. The objective of this research is to develop a small set of
statistically significant indicators that distinguish settlement
type to be used by organizations such as the UN HABITAT
and urban planners without the need for field work or surveys.
This research advances the ability to distinguish informal
(slum) from formal areas by analyzing shape (form), texture,
vegetation, lacunarity of a built-up areas using geographic
information systems (GIS) and remote sensing image analysis. Recent advances in computing power and the increasing
availability of remote sensing imagery have revived renewed
interest in remote sensing as potential data ware for
monitoring informal settlement behaviour. Results showed
that mapping high-resolution data using purely spectral
information resulted in relatively low map accuracies while
using image segmentation and classification tree approach
increased map accuracy. The classical approach used in extraction of slum is based
on Object Based Image Analysis which is carried out in two
steps, namely segmentation and supervised classification. The
main limitation in Object Based Image Analysis is due to the
complexity of satellite images, segmentation is a difficult task. Machine learning approaches have been employed
successfully for extraction of slum in the recent time. As
machine learning techniques are pixel based it has become a
major drawback for VHR imagery. From the detailed review
of prior literatures, informal settlements appear to exhibit
more dirt roads, less vegetation, less road accessibility, higher
SLUM EXTRACTION APPROACHES FROM HIGH RESOLUTION
SATELLITE DATA – A CASE STUDY OF MADURAI CITY
G.Girija1 , R.Immaculate Nikhila
2
1,2UG Student,
Department of Electronics and Communication Engineering,St.Joseph’s College of Engineering, Chennai.
International Journal of Pure and Applied MathematicsVolume 119 No. 12 2018, 14509-14514ISSN: 1314-3395 (on-line version)url: http://www.ijpam.euSpecial Issue ijpam.eu
14509
texture and contrast, more heterogeneity of spaces between
built-up areas, steeper slopes and less favourable terrain
geomorphology for dwellings than formal settlements.
Including measures from each of these categories to correctly
identify settlement type is therefore a worthwhile test, and
should contribute to the collection of replicable, measurable
indicators that can be used to determine the presence of
informal settlements in a variety of urban areas. In this paper, we explore the use of gray level co-occurence
matrix(GLCM) texture features in the extraction of slums and
produce useful information for pro-poor policies. The
technique of combining HUPs and GLCM texture feature
addresses the limitation of pixel-level analysis which is not
suitable for pro-poor policies.
II. CASE STUDY AREA
The study area considered here is the Madurai city in the
South Indian state of Tamil Nadu. The urban population in
Tamil Nadu as per 2011 census is 349.50 lakhs and among
them 20% is living in slums. Madurai is the third largest city
in Tamil Nadu next to Chennai and Coimbatore and the
second largest municipal corporation in Tamil Nadu next to
Chennai. According to 2011 provisional census data, Madurai
city had a population of 1,016,885 (before expansion of city
limit) within the corporation limits, with 509,313 men
(50.08%) and 507,572 women (49.92 %). The urban
agglomeration had a population of 1,462,420. Madurai
metropolitan area constitutes the third largest metropolitan
area in Tamil Nadu and the 24th in India. (TNSCB Report,
RAY 2013)
Longitude 78 6’ 42.34‖E - 78 7’ 17.07‖ E
Latitude 9 55’ 3.46‖ N -9 54’ 33.33‖ N
Fig.1 Study Area
The image is taken by World View-2 satellite sensor which
was launched at September which provides a resolution of
0.46 meter for panchromatic and 1.85 meter for multispectral
images(multispectral bands - red, green, blue, and near-
infrared bands) for enhanced spectral analysis mapping and
observing applications. In this work, the Worldview-2 MSS
(Multispectral data) of Madurai city in the year of January,
2010 is considered. Initially, 331 slums within the Madurai
Corporation area were identified for relocation of the
residents. But later, the officials scrapped 135 of them from
the list as they were already developed. There are nearly 200
slums in the Madurai city along the Vaigai banks and railway
tracks mostly concentrated in Arapalayam, Periyar bus
terminal area, Karumbalai and Alwarpuram.We have
considered the slum dataset of Karumbalai and Managirislum.
Fig.2 Slum dataset-Karumbalai and Managiri slum
III. PROPOSED METHODOLOGY
The steps involved in our research are broadly classified into
four stages: Segmentation, Feature extraction, Classification
and Accuracy Assessment.
Segmentation involves two approaches, one involves dividing
the entire image into individual blocks and another involves
formation of Homogenous Urban Patches(HUP). Feature
Extraction stage involves the computation of features using
statistical (GLCM) method. After extracting different features,
classification is performed by considering a particular GLCM
feature. Finally, in the accuracy assessment stage, the
classified result is verified with the ground truth data with the
help of error matrix.
Fig.3 Proposed methodology Flow chart
International Journal of Pure and Applied Mathematics Special Issue
14510
A. GLCM FEATURES CALCULATION
GLCM is a tabulation of how often different combinations of
gray levels co-occur in an image or image section. The gray
level co-occurrence matrix is found for the whole dataset
image and the corresponding texture feature values like ASM,
contrast, correlation, energy, homogeneity and variance are
calculated. However, we usually do not want a single measure
for a whole image. The texture measure calculation is done to
a GLCM derived from small areas on the image. We then look
at a different small area and record its texture measure to
cover the whole image and find quantitatively how the pixel
relationships differ in different places.
In order to implement this technique, the entire dataset image
is divided into small individual blocks where each block
consists of 16*16 pixels and 8*8 pixels. The gray level co-
features are calculated. Five training data of slum and formal
areas are considered and GLCM texture feature values are
computed for these training data. The average of each texture
feature values corresponding to slum and formal areas are
calculated. These average values are compared with the
texture feature values of each block. The blocks whose values
lie closely with the average value of slum are classified as
slum areas. Thus classification is done based on the
comparison.
B. HOMOGENOUS URBAN PATCHES(HUP)
Homogenous settlements are also referred to as homogenous
urban patches (HUP). Homogenous patches representing
building objects (HUPs at object level) can be extracted
depending on the object characteristics and the spectral and
spatial resolution. Areas that are physically homogenous in
terms of density or texture (e.g., street blocks or settlements)
can be obtained via image segmentation using a larger scale
parameter value. HUPs are the main spatial analysis unit for
aggregating pixel-based information. Key to the methodology
is the extraction of GLCM variance and aggregation to
homogenous neighborhoods. HUPs are derived through image
segmentation by employing five defined characteristics.
• HUPs are areas of homogenous texture that are in
contrast with neighboring HUPs.
• HUP can have several land-cover types (e.g., a mix of
buildings, soil, roads, and vegetation) and are
sufficiently large.
• HUP boundaries follow physical boundaries.
HUPs are extracted by the commonly used multiresolution
image segmentation algorithm embedded in eCognition
Developer 9.0. The multi-resolution segmentation available in
eCognition is a heuristic algorithm based on the Fractal Net
Evolution Approach (FNEA). FNEA is a bottom-up merging
technique that starts with one-pixel objects and a pair wise
comparison of its neighbours in order to merge smaller image
objects into larger ones. The operator controls the
segmentation outcome by setting several user-defined
parameters including scale parameter, shape and compactness. The scale parameter determines the size of segmented objects.
The smaller number of scale generates objects with small size,
whereas the higher number of scale will generate objects with
large size. After experimenting with various scale values by
incrementing by 5, it is found that the best result is obtained
for the scale value of 100.
C. AGGREGATING GLCM VARIANCE AND HUP
By incorporating the GLCM variance values in the HUPs,
better classification of slums and formal areas can be done.
Slums are characterized by low variance while formal areas
have high variance. By setting suitable threshold values for
slum and formal areas with the help of training data,
classification can be done.
IV. RESULTS AND DISCUSSIONS
A.GLCM feature Calculation
The original dataset is divided into individual blocks where
each block consists of 8*8 and 16*16 pixels. It is found that
better results are obtained for 8*8 pixel blocks compared to
16*16 pixel blocks.
Fig.4 Segmented dataset 8*8pixel blocks
The GLCM features have been calculated for sample datasets
of urban buildings and slum areas having the size of 16*16,
8*8, respectively. These values are tabulated as below. From
Table 1, it has been found that slums are having higher
Contrast, Higher Energy, and Lower Correlation than urban
buildings.
Table 1. Texture measures for the study area
TEXTURE
MEASURE
CONTRAST CORRELATION ENERGY
16*16 Slum 2.1898e+03 0.6549 0.0043
Formal 1.0619e+03 0.7101 0.00425
8*8 Slum 2.2724e+03 0.5827 0.01852
Formal 975.5727 0.7754 0.018
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After dividing the image into block by block, GLCM features
(Contrast, Energy, Correlation) are calculated on each 8*8
block and these GLCM features are compared with the
average GLCM features (Contrast, Energy, Correlation) of
size 8*8 block for both urban and slum classes.
Fig.5 Slum classification result for 8*8 pixel block
Though classification using GLCM features for different sizes
classifies slum with good accuracy it can’t be applied for
complex datasets. So classification using homogenous urban
patches is done for better accuracy.
B.HUP based classification
Though classification using GLCM features for different sizes
classifies slum with good accuracy it can’t be applied for
complex datasets. So classification using homogenous urban
patches is done for better accuracy
Fig.6 HUP for scale value=100
The scale parameter determines the size of segmented objects.
Scale value of 100 is considered for better results. By setting
the GLCM Standard deviation threshold value, slums and
other areas are classified.
GLCM Standard Deviation is the square root of GLCM
variance. Slums are characterised by low GLCM variance
while formal buildings have high variance values.
Table 2. Rule set for classification for scale 100
Fig.7 Final Slum classification for scale value 100
C .Accuracy Assessment
Table 3. Accuracy Assessment for HUP based
classification with scale value=100
From the above table, it is evident that a better overall
accuracy of 80.66% is achieved when homogenous urban
patches (HUP) is aggregated with the GLCM texture feature
value for slum classification.
Classification Rule set
Slum areas GLCM Standard Deviation>=35
GLCM Standard Deviation<=39.11
Building GLCM Standard Deviation>=39.38
GLCM Standard Deviation<=48.5
Vegetation GLCM Standard Deviation>=27.28
GLCM Standard Deviation<=34.42
Ground truth
class
User
Accuracy(%)
Producer
Accuracy(%)
Slum Area 86 82.69
Formal Area 80 84.31
Others 76 80.85
Overall
Accuracy
80.66%
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V.CONCLUSION
The purpose of this study is to discuss different approaches to
capture the information about area from high resolution
satellite image approach in identification of slum areas from
very high resolution satellite data. This project dealt with
different approaches for identifying the slums (informal
settlements) using statistical based feature extraction
approaches. The GLCM based feature extraction when
employed for small blocks produced result with Higher
Contrast, Higher Energy, Lower Correlation for the urban
slum than urban buildings. But the results produced in conflict
manner for some of the datasets. In order to overcome this,
HUP based classification was done and GLCM variance
values aggregated with HUPs produced better accuracy.
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