A new approach of edge detection in sar images using

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
_______________________________________________________________________________________
Volume: 02 Issue: 09 | Sep-2013, Available @ http://www.ijret.org 308
A NEW APPROACH OF EDGE DETECTION IN SAR IMAGES USING
REGION-BASED ACTIVE CONTOURS
Ch.SriVeeraSagar1
, K.RamaDevi2
1,2
Department of Electronics and Communications Engineering, JNTUK Kakinada, sairamsagar4472@gmail.com
Abstract
This paper presents a new methodology for the edge detection of complex radar images. The approach includes the edge
improvisation algorithm and followed with edge detection. The nature of complex radar images made edge enhancement part
before the edge detection as the data is highly heterogeneous in nature. Thus, the use of discrete wavelet transform in the edge
improvisation algorithm is justified. Then region based active contour model is used as edge detection algorithm. The paper
proposes the distribution fitting energy with a level set function and neighborhood means and variances as variables. The
performance is tested by applying it on different images and the results are been analyzed.
Keywords: Edge detection, Edge improvisation, Synthetic Aperture radar (SAR), wavelet transforms.
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1. INTRODUCTION
Image processing is a rapidly emergent area of computer
science. Its growth has been uplifted by technological
achievements in digital imaging, computer processors and
mass storage devices. The flexibility and affordability of
various streams are now made path to digital systems.
Examples are medicine, remote sensing and security
monitoring etc.., and the other sources also produce huge
amount of digital image data day to day, which cannot ever
be examined manually. Digital image processing is
concerned primarily to extract useful information from
images. Ideally, this is done by computers, with little or no
human intervention. Image processing algorithms may be
placed at three levels. At the lowest level are those
techniques which deal directly with the raw, possibly noisy
pixel values, with denoising and edge detection being good
examples. In the middle are algorithms which utilize low
level results for further means, such as segmentation and
edge linking. At the highest level are those methods which
attempt to extract semantic meaning from the information
provided by the lower levels.
Synthetic aperture radar (SAR) has great ease of observation
during the day as well night. This property made the SAR
more superior than the optical space borne sensors and any
other, as it has continuous observation over earth which is
independent of sun light. Apart from the merits, the visual
perception of SAR data for an inexperienced viewer is very
difficult and cannot distinguish the meaningful data. So, the
image processing techniques are used for the extraction of
useful data. The available general methods cannot give the
better image result compared to optical space borne sensor
images. Hence, some particular algorithms are still to make
use for the analysis of complex radar data. Novel edge
detection techniques are essentially in two parts edge
improvisation and detection. An edge enhancement phase is
critical in providing intensive detection rates in multiscale
images. There are many methods used for denoising
normally such as Sobel filter [1], Prewitt filter [2],
morphological operators etc.., but they are not up to the
mark in synthetic aperture image applications. This is due to
the presence of speckle noise, termed as multiplicative noise
like pattern [3] in SAR images which made this phase
critical. The paper gives the new approach for the edge
enrichment of SAR images by the application of discrete
wavelet transforms for the increased efficiency then the
existing conventional methods to extract the information.
There are different models for the edge detection which can
are having easy in application and low computational cost.
As they are not quit efficient in the case of SAR images as it
is having discontinuities of edges. The robust method
proposed in this paper is base on region based active contour
model. This algorithm using region based model to edge
based model as it has better performance in the presence of
noise and weak edge. Finally the successive use of the both
proposed algorithms will tackle the subpixel level efficiency
and robust even in the presence of multiplicative noise
pattern in the images.
2. REVIEW OF SAR IMAGES
The design of SAR and its associated applications are
growing exponentially since 1950’s from the day Carl Wiley
of the “Good year Aircraft Corporation”, observed that a
one to one communication exists between track coordinates
of a reflecting object and the instantaneous Doppler swing
of the signal to the radar by the object. By his conclusion it
made the spurt path to performance of the real aperture radar
(RAR) design.
In 1990’s the significant expansion of SAR missions with
the launch of five earth oriented SAR satellites along with
other two more Shuttle Imaging Radar missions and other
are still in use and in progress for improving the space

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observation. The new sensors have improved resolution in
all dimensions, spatial resolution with reduced pixel sizes,
sequential resolution with shorter repeat times and spectral
resolution with increased number of spectral bands. By these
new specifications, new challenges have appeared. The
colossal amount of remote sensing data raises new
computational issues. These applications are accessible or
can achieve new results like change detection, natural
disaster monitoring, landscape planning, biomass
measurement detection of oil slicks. These advances are
especially true for Synthetic Aperture Radar (SAR) sensors,
with high resolution capablility.SAR images are difficult to
interpret and new difficulties arose with the increase of
spatial resolution.
The representation of SAR image can be explained on the
basics convolution of the complex reflectivity of area α(τ,η)
with the impulse response of the SAR system µ0(τ,η) [3].
µ(τ,η) = α(τ,η)*µ0(τ,η) (1)
Where τ and η are the azimuth and the space coordinates,
respectively, in use to define the coordinate system of a
SAR image. The SAR system impulse response µ0(τ,η), the
SAR data pixels are the low pass-filtered [4] version of the
complex local scattering properties may be interpreted as a
bidimensional low pass filter, in such a way the finite local
support of this filter determines the spatial resolution of the
SAR image. The SAR imaging process indicated by (1) can
simplify by a mathematical model that considers linearity
and Born approximation [5].
µ τ, η = α τ, η ∗ µ τ, η 2
Where αk(τ,η) is the complex reflectivity of the kth
individual scattering within the resolution cell. As speckle
represent one of the major defaults for the access of useful
information form SAR images. This can be filtered before
the actual processing of the SAR images but with the
conventional methods the speckle produces over
segmentation even though they are able to smooth speckle.
This leads to the unnecessary date present in the processed
images as this results will be having poor spatially
resolution. There are different speckle filters which are
proposed in different literature, the commonly used are Lee
filter [6], the gamma filter [7] and the frost filter [8]. In this
paper we will be using one among the above mentioned
filters along with the Sobel operator.
3. GENERALISED EDGE IMPROVISATION
ALGORITHM
As we seen that the SAR image has multiscale nature, the
combination of N individual scatter produces the speckle
noise within the resolution cell which can be considered as
the pixel level (i.e., which is significantly a small scale)
spatial varying characteristic. For the justification of
multiscale approach, the product model [9] formalizes a
multiresolution notation stating intensity I as
I= µIxn (3)
Where µI is mean intensity, x is the texture random variable
and n is the speckle is also a random variable. x and n are
considered statistically self-determining. Thus, (3) enables
the identification of different ranges of scales, so in order to
analyze SAR this approach is most reliable. As the wavelets
transforms allow the time-frequency localization. It can also
be used as the multiscale differential operator [10]. The
Algorithm for edge improvisation in SAR images proposed
in this paper relies on the difference of behavior along the
wavelet scales of the speckle in face of the edges. The
discontinuities are highlighted by the wavelet transform and
they tend to persist over scales. Simultaneously the speckle
is progressively smoothening; it is almost spatially
uncorrelated between scales (see Fig. 1).
Fig-1. Simulated speckle signal is obtained through the
application of Haar mother wavelet.
A. ALGORITHM OF GENERALIZED EDGE
IMPROVISATION
The algorithm is proposed for various images ie., either one
dimensional or two dimensional. The size of the window is
also not concern for the application of this algorithm.
Step1: The image is first initialized, which has to be
enhanced the distributions of scales are observed over the
color bar. Some of the images are not smooth and does not
monotonically progresses from dark to light. This type of
indexed image is not suitable for direct wavelet
decomposition with the toolbox and needs to be
preprocessed. To convert the RGB images into a gray-scale
intensity image, using the standard perceptual weightings
functions for the three-color components. Then, convert the
gray-scale intensity image back to a gray-scale indexed
image with desired distinct levels and create a new
colormap.
Step2: In SAR processing the application of logarithmic
operation is used for the minimization of dynamic range of
multiplicative speckle. The multiplicative noise transformed
in this method is additive and signal independent, but its

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probability density distribution is approximately taken as
Gaussian [11].
Step3: Generally the Haar transform is the oldest and
simplest of all the wavelet transforms. Owing to its low
computing capability, the Haar transform has been mainly
used for pattern recognition and image processing. Hence,
two dimensional signal and image processing is an area of
efficient applications of Haar transforms due to their
wavelet-like structure. Moreover, wavelets are considered as
a generalization of the Haar functions and transforms. Such
a transform is also well suited in communication technology
for data processing, multiplexing and digital filtering.
The wavelet transform can be expressed as
, =
1
√
∗
−
4
The Haar wavelet is defined as
Fig-2. The graphical representation of Haar wavelet.
Hence, in the wavelet transform of the logarithm of the SAR
signal, the influence of the speckle is low since its
contribution in each of the semi intervals counteracts with
the other, if we assume that the speckle is homogeneous
through the observed scene. ie., the speckle in the larger
intervals that obtained through the application of logarithm
by the assumption of homogeneity made its values in that
range very low and it can be taken as approximately zero.
Step4: Let us consider the subbands obtained through the
application of wavelet transform: 1D wavelet transform has
two frequency bands and the 2D wavelet transform has four
bands for each level of decomposition. In 2D transform one
band has the low pass filtered image and the rest will be in
transformed in three orientations as horizontal, vertical and
diagonal respectively. As the wavelets maximizes the
discontinuities in the edges so, by taking the absolute value
of the subbands (i.e., H,V&D) the maximum values of the
each band can be enhanced by applying normalization to the
maximum.
Step5: Than by considering the pixel level local maxima in
the each subband in the neighborhood of the discontinuity
and multiplying these smallest resolution cells the
discontinuities can be minimized and the speckle in the
resolution cell will un-correlated which is divergent to the
intrascale combination with the useful data and the edges are
enhanced in each direction. This process is carried out for
the different levels of scales.
The algorithm is preformed for the predefined number of
iteration as the higher iterations of the algorithm may
produce the thick edges. Compared to the other conventional
methods such as Sobel gradient method the results shown in
the figure (3) have reviled the good and much efficient
method for the edge improvisation by the application of
proposed method.
(a) (b)
(c) (d)
Fig-3. The result of the simulated images (a) it is the
original image. (b) Result of Sobel gradient method. (c), (d)
the result of proposed algorithm in second and third
iteration. (Image is taken from Google SAR images
courtesy).
As iteration count increases the edge will be much thinner
than they appear, so the better implementation of the
method for various images may be differ from one to
another. The discussion on results can be elaborated in the
result section.
4. EDGE DETECTION WITH REGION-BASED
MODELS
Image segmentation is the one of the essential part in the
scenario of image processing. There is been a great study
especially in this class of area. An entrenched class of
methods is active contour models, which have been widely
used in image segmentation with promising results. The
models can achieve subpixel accuracy and provide smooth
contours. Active contour models can be mostly categorized
into two models: edge-based models [12] and region-based
models [13].Edge-based models uses image gradient to
direct curve evolution, which are generally sensitive to noise
and weak edges [14]. Instead of utilizing image gradient,
region-based models usually intend to identify every region
of interest by using a convinced region descriptor, such as
intensity, texture or motion, to guide contour [15]. Therefore
t11/2
Ψ(t)
1
-1

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region-based models generally have better performance in
the presence of image noise and weak object boundaries. In
practice, intensity inhomogeneities occur in many real world
images acquired with different modalities [16]. Especially, it
is frequently seen in medical images and multi-resolution
images.
Once the improvisation algorithm estimation has been
obtained, a final step of edge detection is required to build
an unsupervised algorithm. In our model, the image
intensities are described by standard normal distribution
with diverse means and variances. The paper proposes the
distribution fitting energy with a level set function and
neighborhood means and variances as variables. The energy
minimization is achieved by an interleaved level set
evolution and estimation of intensity means and variances in
an iterative process. The means and variances of
neighboring intensities are considered as spatially varying
functions to handle noise of spatially varying strength (i.e.,
multiplicative noise). Then the new approach to the SAR
images with multiplicative noise like pattern gives
comparatively better results than the geodesic active
contours which are driven by the edge based model [17].
Chan and Vese [18] developed a mean curvature flow based
level set implementation of a specific case of the Mumford
Shah energy functional, where the mean intensity of the
pixels inside and outside the curve are used as a smooth
approximation of the image but they are not optimum in the
case of non homogeneous intensity images. So, by
considering the LBF model proposed by C.Li [19] which is
approximate the intensities inside and outside the contour.
The LBF model cannot provide the required information for
the perfect segmentation in the presence of heavy noise and
intensity. So, by taking the input image from the above
proposed algorithm speckle noise and the intensity
inhomogeneities can be avoid as possible as maximum and
the intensity can be taken from the normal probability
distribution.
The energy function can be written as
E ϕ, c , c# = λ % % K' x − y p ,+I y H.ϕ y / dy dx
+λ# K' x − y p#,+I y 1 − H ϕ y dy dx 5
Where λ1, λ2, v, µ are weighting positive constants. Kσ is
kernel function with a localization property that Kσ (x-y)
and the C1, C2 are the two regions in the image domain C,
the local point x with a curvature of r. The kernel function is
considered with the fixed σ. The two regions are considered
as they are having mean and variance are spatially varying
and defined as ui, σi
2
within the regions concern to local of x.
As the smoothing functions are defined in the Vese and
Chan model [20] and C.Li [21] as
3 ϕ = v %5∇H.ϕ x /5 dx 6
Where ϕ is level set function, the L (ϕ) is penalizing the
length and for smooth contour propagation. The regularized
level set evolution is from a distance function [22] which is
for the accurate execution is given as
D ϕ = μ % #
|∇ϕ x | − 1 #
dx 7
Fig-4. Flow chart for edge detection algorithm.
The energy function is considered after including the both
smoothing and distance functions is
F ϕ, c , c# = E ϕ, c , c# + L ϕ + D ϕ 8
This model is different from the C.Li and Chan Vese as we
have taken the different distribution and also the application
of method for specified images. The method is most
complimentary with the iterative process done in various
steps shown in the flow chart as follows.
5. RESULTS
The application of algorithms is absolutely gives the
expected results for the costal line detection of SAR images
as shown in figure. 5(a)&(b). The simulation is performed in
desktop of 2.8 GHz processor with 2 GB ram in MatLab
2009 software.
Yes
No
Start
Input image
Define?, n, i=1, σ
Evaluating mean,
variance, @A ⁄ @C
If i≤n
Result
Stop
i++

IJRET: International Journal of Research in Engineering and Technology
__________________________________________________________
Volume: 02 Issue: 09 | Sep-2013, Available @
Fig-5. (a) the simulated image of detected edges by the
application of algorithm proposed for edge detection of SAR
images.
Fig-5. (b) the simulated image of extracted & detected
edges by the application of both algorithms proposed.
CONCLUSION
This paper is mainly focus on one of the problem general
occurs in the SAR date and the detection of costal area
boundaries of images for the application of remote sensing.
The results are also good compared with other methods such
as Sobel gradient method and geodesic active contours. As
“snakes” are not so significant in detecting edges of most
discontinuously formed with the application of wavelet
transforms. So, the region based approach is most eligible
method for the detection of edges in multiplicative noised
images. The work can be further extended on 3D images and
also for target and remote sensing applications with more
reliability.
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IJRET: International Journal of Research in Engineering and Technology eISSN: 2319
_______________________________________________________________________________________
2013, Available @ http://www.ijret.org
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boundaries of images for the application of remote sensing.
The results are also good compared with other methods such
active contours. As
“snakes” are not so significant in detecting edges of most
discontinuously formed with the application of wavelet
transforms. So, the region based approach is most eligible
method for the detection of edges in multiplicative noised
es. The work can be further extended on 3D images and
also for target and remote sensing applications with more
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eISSN: 2319-1163 | pISSN: 2321-7308
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BIOGRAPHIES
Ch. SriVeeraSagar received B.Tech degree in
Electrical and Electronics Engineering. He is
currently pursuing M.Tech degree in
instrumentation and control systems from
University College of Engineering, JNTU
Kakinada. His area of interest is image processing
in typical noised images.
Presently working on speckle noised images to detect the significant
and delicate edges with the study of some of the existing works in
different application and simulated in MatLab environment.
K. RamaDevi obtained M.E degree in Microwave
and Radar Engineering from Osmania University
College of Engineering in 2003. She is presently
working as Assistant Professor in the Department of
Electronics and Communication Engineering. She is
presently pursuing Doctoral degree from JNTUK.
She presented many research papers in various national and
international journals and conferences. Her research interests include
Micro Strip Patch Antennas, slot antennas and image processing in radar
applications simulated in MATLAB and HFSS.

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