A Literature Review on Road Segmentation Techniques in SAR ... · A Literature Review on Road Segmentation Techniques in SAR Images for Video Surveillance Applications 1V. Padmanabha

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,


[email protected] 3Dept. of CSE, Institute of Aeronautical Engineering,


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/

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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

International Journal of Pure and Applied Mathematics Special Issue

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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.


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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].


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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.


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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


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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


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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


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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.


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