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A Literature Review on Road Segmentation
Techniques in SAR Images for Video Surveillance
Applications 1V. Padmanabha Reddy,
2R. Obulakonda Reddy and
3N. Poornachandra Rao
1Dept. of ECE, Institute of Aeronautical Engineering,
Dundigal, Hyderabad.
2Dept. of CSE, Institute of Aeronautical Engineering,
Dundigal, Hyderabad.
[email protected] 3Dept. of CSE, Institute of Aeronautical Engineering,
Dundigal, Hyderabad.
Abstract
Road extraction from satellite images has a pivotal role in the context of
automated mapping systems for smart cities planning and to update the
graphical information systems. SAR sensing methodology that genuinely
work on all the days in a year i.e. 24 X 7 X 365 except in most abnormal
situations. Since it covers a broad area throughout the word, it provides the
surveillance quite a long period. Manual techniques for extracting the ROI
from images are faded, costly and time consuming. It is essential to
automate the segmentation and objects classification in SAR images for
high-level processing and in many other real time applications. It is
significant to extract the road regions from satellite images from the way
that it enormously improves the proficiency of generating maps and it will
be a major help in vehicle navigation systems. This criteria leads expanding
the research is being committed and focused on efficient techniques for
extracting the useful features i.e. roads from the input images. Our key
contribution in this paper is, identifying and automating the extraction of
road regions from the SAR images by using convolution neural networks.
This paper is mainly focused on the earlier works in this field such as
numerous segmentation methods. The metrics for evaluating the
segmented results are also crucial for identifying the efficient methods for
extracting the ROI.
Key Words: Segmentation, SAR images, road extraction, CNN, evaluation
metrics.
International Journal of Pure and Applied MathematicsVolume 119 No. 16 2018, 5349-5365ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/
5349
1. Introduction
Image Segmentation is said to be one in the emerging trends in the field of
image processing. It has found applications in the field of medical applications.
It helps in segmenting the images into sub regions which are of our interest
which can be analyzed individually. Satellite imagery is one of the wide areas in
segmentation. There exists several techniques such as K-means Clustering,
Thresholding Technique and Active Contours for satellite image segmentation
and evaluate the best method in satellite image segmentation using various
performance parameters like Segmentation Accuracy, Correlation Ratio [1].
Road extraction from satellite imagery has become a heated research subjects in
recent years. It is especially used in the city planning, cartography and to update
previously detected roads in Geographic Information Systems (GIS)
environment. GIS is becoming well-known day to day because of internet
attractiveness and also with satellite image. The Google, Yahoo, Virtual Earth
and other maps are some of the instances which exhibits satellite images with
high resolution. [3].The challenging issue here is extracting the road part from
aerial images which are noisy and of lower resolution Roads are considered as
key planar features prevailing in the terrain. In the past few years, the rapid
urbanization led to make new methods so that to update maps, which is not
possible via already established techniques known as long term surveying and
mapping techniques. We have to apply Gaussian filtering on an image for
removing the noises with higher frequencies. To make improvements on road
region edges there used a paradigm known as canny edge detection [5]. During
the happenings of disasters like strikes and any other roads performs an
important role in getting normal conditions to the disaster-stricken areas.
Afterwards road blockage information is to be delivered promptly for
assistance. Hence sufficient acquisition was made towards the speedy road
extraction choices from remotely sensed images [6].
Humans are able to find roads easily in the remotely sensed images. But this is
not so easy to automate by using computers. For detecting road sections from
satellite images, some experts initially search for a collection of planar and
curvilinear features and later they apply their knowledge otherwise they also use
their experience to decide whether the searched ones are roads or not. On the
basis of human perception a method called automatic hybrid road detection is
introduced. It also takes the benefit of statistical (Gaussian Mixture Model
method) and artificial neural network approaches. If there is expansion in
transportation network from the satellite images is not that much easy for roads
extractions and to keep the maps up-to-date. Even so roads are hard to find in
the SAR images since they visually look similar to rivers and railways. Still no
approach was discovered/developed to extract total networks of road from the
SAR images but deep convolutional neural networks were very successful in
segmenting the objects[2]. Figure 1 shows a block diagram for most of the road
extraction techniques. The input is based on what we call SAR image images.
These products result from combining SAR images from multiple passes. Here
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we assume the images have been calibrated and are collected at approximately
the same aspect and grazing angles. The current work is focused on extracting
the road regions from the satellite images. Consider an example, one view of the
satellite image in RGB format is shown in Figure 2 and it is the kind of input for
our work.Motivated by the report of Zion, Global satellite imaging market is
ever increasing on demand. Capturing High resolution of satellite images and
Segmentation of the objects or the regions from these SAR images is essential
in the field of commercial satellite imaging. It is dominated by defense and
intelligence and accounted more than 30% of the total share in the year of 2014-
2015. The statistics of this area and predicted share by 2020 is shown in Figure
3 (Zion, research Analysis 2016).
Figure 1: Block Diagram of SAR Road Detection Approach
Figure 2: Sample SAR Image
Figure 3: Statistics and Predicted Share of GIS
SAR Images
Image Pre-Processing
Segmentation
Feature Extraction
Classification
Road
Models
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2. Segmentation Methods
The main goal of segmentation technique is to divide the image in to regions of
interest. Several segmentation methods exists for doing this task.There is a
broad research has been created and many techniques are applied for getting
segmented regions, for example SLIC super pixel methods on images [6].
Furthermore there exists numerous methods [7, 8, 9], which considers local
detection with the help of global optimization by reducing the noise. These
methods works based on k-means method using Euclidean distance as a metric
for computing the distance or similarity [10]. In [11], the authors described that
Euclidean distance is almost similar to ratio-intensity. Segmentation techniques
are divided in numerous categories on the basis of application, imaging
modality and some other factors. In this section the overview of present
methods which are utilized for computer automated segmentation of satellite
images. There is having six general divisions in which the segmentation
techniques are divided. Those are thresholding, region growing, classification
based, cluster based, ANN based, watershed algorithm [12]. Along with these
methods, other methods are also exist for the same work. These methods can be
work alone or with the combination of the other techniques for giving better
segmentation results. However, most of the methods work based on either
considering either discontinuities or similarity of the pixels [13]. We discuss
only few of the segmentation methods and the summary of their work is shown
in Table 1
Thresholding
The procedure of thresholding tries to describe an intensity value known as
threshold. This intensity value divides the required classes. The segmentation is
attained by clustering entire pixels at when intensity greater than the threshold
as one class, and all other pixels. as another class. This is easy but becomes
effective means in obtaining segmentation in images. The drawback with
thresholding is that, it is possible to generate two classes in it but it is
impossible in the sense of multi-channel images applications. Besides
thresholding do not considers the spatial features of an image and so that are
noise sensitive. On these reasons the deviations in classical thresholding was
proposed which incorporates data on the basis of local intensities and
connectivity [14].
Region Growing
It is a technique to extract image region that is linked on the basis of some
predefined criteria. This criterion relies on intensity data and/or edges of that
image. Region growing needs a seed point and extracts entire pixels that are
mapped with the first seed with the similar intensity value. The key drawback
here is that it needs manual interaction to get seed point. This could be solved
by using split and merge paradigms which need not a seed point [15]. Region
growing is noise sensitive, causes the extracted regions with holes otherwise
disconnected. On the other hand partial volume effects will cause separate
regions connected.
International Journal of Pure and Applied Mathematics Special Issue
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Table 1: Recent Study on Segmentation Techniques for Numerous Applications
Edge based Segmentation
Author Method/Technique used Description
Katz. [20] Canny Edge detection Applied Thresholding value for extracting the ROI region.
Ganesan P et.al. [21] Sobel, Prewitt, Canny CIELAB color model is utilized with numerous edge detection algorithms. Among which, canny has given the better results.
Adam Huang. [22] Shape based approach with
Gaussian Filter
Extracted the shape features from the 3D medical images.
Threshold based Segmentation
Ghosh et.al. [23] Fuzzy divergence method Applied threshold based segmentation for leukocyte segmentation.
Fabio Scotti [24] WBC Applied WBC method using L*A*B color model for blood
microscopic images.
Sankar Reddy et.al. [25] Otsu’s Method Applied Otsu’s method to enhance the contrast in the poor quality
images.
Cluster based Segmentation
Subrajeet Mohapatra [26] k-means clustering Applied two step method for segmentation using semi supervised k-
means clustering.
P Palani Swami [27] Unsupervised machine learning Applied un supervised machine learning method for segmentation i.e. k-means clustering.
Subrajeet Mohapatra [28] Fuzzy c-means clustering Applied fuzzy c-means clustering by converting RGB to L*A*B color
space.
Other Segmentation Techniques
M.Sudhakar and M. Janaki Meena [29]
Region based Segmentation Applied gamma correction for detecting the eye region in poor contrasted image for real time images.
J. Cheng et.al.[30] Double window template Applied local segmentation method using double window template
method for road segmentation
R. Achanta et.al. [31] Super Pixel method Applied SLIC super pixel method for segmenting the road regions from
the satellite images.
Classification-based Approaches
Classifier methods are considered as techniques of pattern recognition which
always seeks to divide a feature space that is derived from an image using data
with the known labels. Total pixels which are having same features are grouped
into a single class. Classifiers are called as supervised methods as they need
training data which is segmented manually. These methods involve nearest-
neighbor classifier, K-nearest-neighbor (kNN) classifier, Parzen window
classifier, Bayes classifier and so on. Since it is non-iterative there are efficient
in computational aspects and can also be applied on multi-channel images.
Nonetheless the usually do not perform any spatial modeling. This has been
cleared by the inclusion of intensity in homogeneities and neighborhood and
geometric information [16].
Clustering-based Approaches
Clustering algorithms intentionally perform the similar function as classifier
methods without using training information and are said as unsupervised
methods. To compensate deficiency of training data, clustering methods iterate
between image segmentation and characterizing the criteria of every class.
Shortly the clustering methods train themselves by using available data. Mainly
we use three clustering algorithms in common, those are: k- means, the fuzzy
means algorithm and the expectation- maximization (EM) algorithm. Even
though clustering algorithms need not training data, but they require an initial
segmentation (or equivalently, initial parameters). Like classifier methods, these
clustering algorithms do not straightly incorporate the spatial modeling and can
therefore be noise sensitive and intensity in homogeneities. This leads to yield
important benefits for faster computation [17].
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Artificial Neural Networks
ANNs provides an algorithm for machine learning and can also be used in
different ways to image segmentation. Classifier is the widely used and applied
in satellite imaging [18], where the weights relies on the utilizing training
information. Later the ANN is used for segmenting new data. Besides ANNs are
also utilized in an unsupervised way as a clustering approach [19], also for
models which are deformable. Since ANNS are parallel inherently, their
processing is generally activated on the standard serial computer so that resizing
this potential computational advantage.
Watershed Algorithm
Watershed segmentation is the most predominant intensity correction and
prevents noise by using edge-preserving directional anisotropic diffusion
method. Lastly an IDWT (Inverse DWT) is performed to get the improved
image. As most of the interconnections utilized in neural network, spatial data
could easily be incorporated into its classification procedures.
3. Satellite Image Classification
A satellite is equipped with a SAR (Synthetic Aperture Radar). It is able to scan
topography of an area. The resultant data of visible terrain has greater resistance
over optical imagery to modifications in exposition and color. Furthermore SAR
sensors are able to operate without depending on weather conditions, a key
benefit while surveying a region affected by weather based disaster. Our current
study concentrates on roads extraction in SAR satellite images. It is not that
much easy to identify roads in SAR images. Since those are usually
characterized by thin dark lines even though some important feature disparities
can be observed.
Mathematical Morphology
This is a primary and conventional framework in the field of general image
processing. The development in Mathematical Morphology (MM) tends to
various processing tools with the motive of image filtering, image retention and
classification usage measurements, pattern recognition, or texture analysis and
synthesis. Whichever the morphological operators belonging to recent form are
having the draw backs of grating artificial patterns and distorting or removing
significant details to cope with the problems we introduce new morphological
operators that uses Adaptive neighborhood aspects. This proposed method
utilizes new type of opening and closing operators (NOP & NCP)
Road Segmentation
There is a greater demand for automatic acquisition and update road
information. In spite of more research study was done on semi and fully
automated models for road extraction. The required automation high level
cannot be attained for now. The main risk with this approach is that the result
quality is not enough for most of the applications. Few of the segments of the
road network are skipped and some are erroneous [32, 33].
International Journal of Pure and Applied Mathematics Special Issue
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Satellite Imaging Techniques
There exist various methods and techniques in satellite image classification. In
the hierarchy of satellite image classification methods; Automated, Manual,
Hybrid are 3 main categories. [34]. Hui Kong et. al. [35] in 2010 worked with a
method known as general road detection from an image by using an approach
called as vanishing point detection. In this method it was suggested that using
36 orientations Gabor Filters in order to extract an orientation data at every edge
pixel.J. Shabnam et al., [36] proposed supervised satellite image classification
method for classifying satellite images with very high resolution into particular
classes by using fuzzy logic. This method helps to classify the satellite images
into five classes majorly as bare land, road, vegetation, building and shadow.
This method also utilizes image segmentation and fuzzy approaches for the
classification of satellite image. It involves in two segmentation levels. In the
first level of segmentation identification and classification of shadow,
vegetation and road is incurred. In the second level of segmentation buildings
are detected.Later it utilizes contextual check to classify the unclassified
segments and regions. Fuzzy techniques are specially used to increase the
accuracy of classification at the edges of objects.
DibyaJyoti Bora et al. (2014) [37], was proposed her paper work on image
segmentation. It is mentioned as a vast research topic and option of more
number of researchers by the author. The reason to become image segmentation
popular is its significance in image processing and computer vision. The
important work of researchers who are working in the domain is to develop an
efficient method for segmentation. The benefit with clustering approaches of
image segmentation is that it is broad area and could be implemented in many
of the engineering domains. In this paper she was developed a new technique to
segment an image by putting a base as clustering. K-means algorithm is worked
and distance parameter is used to decide the performance. Then Sobel filter is
utilized to filter and to obtain the results we use Marker Watershed algorithm.
For this work the author considered Mean Square Error and PSNR as
performance parameters.
Muhammad Waseem Khan et al. in 2014 [38], in his paper work he was stated
that image segmentation is an integral part of image processing. The author says
that the steps of image segmentation are required during image processing. The
work of image segmentation is partitioning the image into different regions in
order to analyze the image easily. We can also easily detect the number of
objects in an image after image segmentation is done. To make the process to
evaluate and analyze images easy several image segmentation techniques are
developed till today. He also developed a new technique for image
segmentation with the utilization of innovative technology.Zheo et al. [39]
Combined spatial, spectral and spatial location cu es with the use of Conditional
Random Field (CRF) model to provide remote sensing image with high
resolution. This proposed method clears the problem of spatial variability, even
though it fails to use better information available in spatial location cues.
International Journal of Pure and Applied Mathematics Special Issue
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Corentin Henry et.al. Proposed a method for identifying the road in SAR images
using Fully Convolutional Neural Network. According their study the
segmentation is done with two approaches i.e. binary segmentation and
regression.
In the first stage, every pixel present in the ground truth image will be
considered as either positive or negative with a spatial tolerance of ‘0’. Positive
pixel indicates that the pixel contains road and negative pixel denotes that the
pixel does not contain the road. Secondly, an adjustable tolerance is utilized in
regression since few of the road pixel may be far distance from the regular
pixels. There are mainly ‘3’ types of CNNs and are application dependent i.e.
R-CNN, Fast R-CNN and Faster R-CNN. The test time, speed and mAp of each
kind of CNN is shown in Table 2.We have listed the summary of various
classifiers in Table 3 and the comparative study of few algorithms are shown in
Table 4. The methods used in this work and the numbering is shown in Figure
4.
Table 2: Comparison of ‘3’ CNNs
R-CNN Fast R-CNN Faster R-CNN
Test time 50 s 2 s 0.2 s
Speed 1x 25x 250x
mAp 66.0% 66.9% 66.9%
Table 3: Classification of Various Classifiers PARAMETERS
k-means ISO Data Minimum
Distance
Parallel piped Maximum like
hood
SRG ESRG
METHOD
Unsupervised Unsupervised Supervised Supervised Supervised Supervised Supervised
DISTANCE
Euclidean
Mean
Euclidean Mean Euclidean
Mean
Standard
Deviation
Standard
Deviation
Euclidean Mean Standard
Deviation
COMPLEXITY
Low Low Low Medium High Medium Medium
ACCURACY
Low Low Low Medium High Medium High
MERITS
Easy to
process and
execution is
fast
Efficient in
detecting inherent
clusters
Easy to
process and
execution is
fast
Easy to process
and execution is
fast
Efficient in objects
classification
Only seed points
are required
rather than
training site
marking
By class
details the seed
points are
pointed at field
level.
DEMERITS
One who
analyzes
cannot know
a priori
number of
spectral
classes.
One who analyzes
cannot know a
priori number of
spectral classes.
Iterations take
lengthy time.
Since it
considers only
the mean the
accurate rate Is
low.
Occurs
overlapping
among each box
of a class and it
led to occur flaws
in outputs.
Sufficient ground
truth data needed
otherwise there
will be failures in
results.
Consumes more
time to process.
Sufficient
ground truth
data needed
otherwise there
will be failures
in results.
Takes more
time to
process.
Our main goal of proposed work is to extract the road region from the input
images. The task is varied depending upon the input image and the captured
image also different in some situations. For example, the input image may be
captured from suburban area, developed urban area, emerging sub-urban area or
emerging urban area and few of these images are shown in Figure 5.The idea
here is to extract the road regions from the SAR images are summarized as
shown in Figure 6. Acquiring the images is the first task for any segmentation
International Journal of Pure and Applied Mathematics Special Issue
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method in image processing. Although, SAR images contains the data in high
quality, we need to perform smoothing techniques to reduce the noise from the
images such as overlapping regions should be minimized. Later the training
process of the images takes place with the help of Keras with backend of
TensorFlow. The loss-weighting is applied on the ground truth images. Post
processing may require multiple iterations for enhancing the accuracy. The
results should be evaluated with different parameters as given in the next
section, and this paper is mainly focused on the literature study. 1. k-Nearest Neighbor 7. Support Vector Machine
2. Maximum likelihood 8. Spectral Information
3. Minimum Distance 9. ISO Data
4. Hybrid Method 10. Region growing methods
5. Parallel Piped 11. Mahalanobis Distance
6. Chain Method 12. CNN(A) / Binary Segmentation(B)
Figure 4: Numerous Parameters Considered in Road Extraction
Table 4: Comparison of Satellite Image Classification Methods
Author Test data used Method used Comparison
T. Jamshid et al.,[40] Landsat 5TM Images 6 5,3,6
H.N Shila et al., [41] Landsat 7 ETM+ data 4 4
N. Maryam et al., [42] Landsat 7 ETM+ data 4 7,2,3,11,8,5
A. Aykut et al., [43] Landsat 7 ETM+ Images 2 2,3,5
Manoj Pandya et al.,[44] Landsat, SOPT and IRS Datasets 10 9,3,2,5,10
T.Shubash et al., [45] Landsat 7 ETM+ data 2 2,3,11
R. Offer et al., [46] Desert outlay Datasets 4 9,2,4
K. Kanika et al., [47] IRIS Plants Dataset 1 1,2,3
Corentin Henry et.al [48] Terra SAR-X 12(B) 12(A)
.
a
b
c
d
Figure 5: Numerous Satellite Images a. Developed sub-urban, b. Developed urban-area, c.
Emerging sub-urban, d. Emerging urban-area
International Journal of Pure and Applied Mathematics Special Issue
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Figure 6: Training Process with Evaluation
4. Qualitative Evaluation of Road
Segmentation
To perform comparison among Segmentation techniques some of the following
parameters are taken into sight.Several quantitative measures are helpful in
evaluating the quantitative performance of proposed road segmentation
algorithm. Those measures are attained by comparing the inputs of segmented
road and ground truth road images. The below are the different quantitative
measures utilized for evaluation.
Mean: The exact definition of mean shows an Average or a mean value of an
array. The Image is in form of a matrix.
Variance: This parameter performs computation on unbiased variance of every
row or column of input, together with the vectors of particular dimension of an
input, otherwise of total input. The block of variance keeps tracks of variance of
inputs sequence for some particular time.
Standard deviation: It is denoted by σ (sigma) and represents how much
variation/dispersion does there exists from the mean average or predicted value.
A minimum standard deviation resembles that data points are closer to mean
whereas the maximum standard deviation denotes that data points exist over a
high range of values. Unlike variance is said as useful property of standard
deviation and is expressed in similar units like data.
Acquisition of Data
Preprocessing
Training the Network
Loss-weighting
Post processing
Evaluation
International Journal of Pure and Applied Mathematics Special Issue
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SNR (Signal to noise ratio): Signal-to-noise ratio is abbreviated SNR or S/N. It
is a measure used in science and engineering that compares the desired signal
level to the background noise level. It is defined as the ratio of signal power to
the noise.
Recall points out how many road pixels from the complete road pixels are
classified exactly.
Precision refers how many road pixels are detected from complete chosen
pixels. Higher Recall value improves chances for identifying road pixels but
also improves chance of classifying non-road pixels as road pixels but it is not
much possible when there is higher precision but in the mean while it decreases
the Recall rate.
Accuracy means we weighted arithmetic mean of precision and inverse of
precision as well as a weighted arithmetic mean of Recall and inverse of Recall.
F-Measure is a weighted harmonic mean of Precision and Recall. Eventually
Recall=𝑇𝑃
𝑇𝑃+𝐹𝑁 (1)
Precision=𝑇𝑃
𝑇𝑃+𝐹𝑃 (2)
Accuracy=𝑇𝑃+𝑇𝑁
𝑇𝑃+𝐹𝑃+𝑇𝑁+𝐹𝑁*100 (3)
F-Measure=2∗𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗𝑟𝑒𝑐𝑎𝑙𝑙
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 +𝑟𝑒𝑐𝑎𝑙𝑙 (4)
Here, the terms TP: True Positives FP: False PositivesTN: True NegativesFN:
False Negatives respectively.
5. Conclusion
Segmentation of road from satellite images has a pivotal role in the context of
automated mapping systems. There exists numerous methods for segmentation
and each method is an application dependent. We have discussed different kinds
of segmentation methods and the works related to the SAR imaging. Although,
there exists several ROI extraction methods, efficient methods are required for
the complete automation of road extraction. Additionally, overview of the
segmentation methods along with the works done earlier on road extraction
from SAR images is discussed. We have given our proposed idea in this paper
for automating this task by training with suitable convolutional networks.
Although, the implementation of our work is limited in this paper, we focused
the literature work in this research field. As per the best of our knowledge, there
exists very few measures for evaluating the segmentation tasks and having good
research scope.
International Journal of Pure and Applied Mathematics Special Issue
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