International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
Adapting Seeded Region Growing for Segmenting the
Flooded Area from the SAR Images
Chirumamilla SrinivasBhargav.1, Kamlesh Murari2
M.E Computer Science and Engineering1, Faculty of Computer Science and Engineering2,
Sathyabama University1, Sathyabama University2
Chennai-600119, India
ABSTRACTSAR images are used for detection of flooded
areas in the region. Here we used cross calibration
technique for the detection of flooded areas in the
SAR images. The intensity of the image is low by
using cross calibration so we proposed a new
method called seeded region growing. SRG is
based on the pixels of the region is controlled by
choosing a number of pixels known as seeds. By
adapting the SRG segmentation the detection of
flooded region can be more accurated. A novel
images of preprocessing phase, and also so-called
“cross-calibration/normalization,” is proposed to
solve this problem. This, in turn, facilitates images
of enhancement and the numerical comparison of
the different images takes together with data
fusion and visualization processes. The proposed
processing of the images chain includes filtering,
histogram truncation, and equalization steps
applied in an adaptive way to the images.
KEYWORDS:SAR images, Histogram Clipping, RGB, Region
Growing.
processed and/or displayed on a high-resolution television
monitor. For display, the image is stored in a rapid-access
buffer memory, which refreshes the monitor at a rate of 25
frames per second to produce a visually continuous
display.
Digitizing or digitization is the representation of an
object, image, sound, document or a signal (usually an
analog signal) by a discrete set of its points or samples.
Digital information exists as one of two digits,. An image
is digitized to convert it to a form which can be stored in a
computer's memory or on some form of storage media such
as a hard disk or CD-ROM. This digitization procedure can
be done by a scanner, or by a video camera connected to a
frame grabber board in a computer. Once the image has
been digitized, it can be operated upon by various image
processing operations. A computer is an electronic device
that accepts raw data, processes it according to a set of
instructions and required to produce the desired result.
Mathematical processing of the digitized image such as
convolution, averaging, addition, subtraction, etc. are done
by the computer.
II RELATED ARTICLES
I .INTRODUCTION
Digital image processing is the use of computer
algorithms to perform image processing on digital images.
The 2D continuous image is divided into N rows and M
columns. The intersection of a row and a column is called a
pixel. The image can also be a function other variables
including depth, color, and time. An image given in the
form of a transparency, slide, photograph or an X-ray is
first digitized and stored as a matrix of binary digits in
computer memory. This digitized image can then be
ISSN: 2231-5381
The aim of the process proposed here is to
preserve all of the information in the images of a
temporal sequence while enhancing the differences
between the data takes. When working with SAR
multitemporal images, it is very difficult to attain two
perfectly matching images in terms of satellite
position, looking side, or angle of incidence. The
calibration process is very sensitive process and
depends on many parameters, and some errors may
occur. In such a case, comparing two or more images
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
becomes critical due to significant signal differences
even in unchanged areas. As a result, one of the
major challenges is the image preprocessing step
required to make the images comparable, allowing
the use of distance measures to evaluate the presence
of changes. Because of the large SAR distribution
skewness
value,
some
traditional
image
preprocessing procedures that are generally used to
improve visual perception and image analysis are
inapplicable here. To solve this problem, a new
specific preprocessing chain is proposed here been
designed to achieve cross-calibration/normalization,
allowing image enhancement and the numerical
comparison of various image takes.
In this paper, some applications of image
processing and segmentation of SAR images are
presented, in order to generate fast-ready and detailed
flood maps. For both purposes, multi temporal
image-analysis methods are applied to a pair of SAR
images acquired on the same area at different times.
Fast-ready flooded maps have been generated by an
RGB composition that is able to enhance the changes
occurred in the couple. It is thus possible to focus the
user attention on flooded areas. Multi-temporal image
segmentation has been used to generate the detailed
maps of flooded areas. The proposed method is able
to generate connected regions of flood, steady-water
and no-change areas.
Synthetic-aperture radar (SAR) is a form of
radar whose defining characteristic is its use of
relative motion, between an antenna and its target
region, to provide distinctive long-term coherentsignal variations that are exploited to obtain finer
spatial resolution than is possible with conventional
beam-scanning means. It originated as an advanced
form of side-looking airborne radar (SLAR). SAR is
usually implemented by mounting, on a moving
platform such as an aircraft or spacecraft, a single
beam-forming antenna from which a target scene is
repeatedly illuminated with pulses of radio waves at
wavelengths anywhere from a meter down to
millimeters. The many echo waveforms received
successively at the different antenna positions are
coherently detected and stored and then postprocessed together to resolve elements in an image of
the target region.
ISSN: 2231-5381
Current (2010) airborne systems provide
resolutions to about 10 cm, ultra-wideband systems
provide resolutions of a few millimeters, and
experimental terahertz SAR has provided submillimeter resolution in the laboratory.SAR images
have wide applications in remote sensing and
mapping of the surfaces of both the Earth and other
planets. SAR can also be implemented as "inverse
SAR" by observing a moving target over a substantial
time with a stationary antenna. An image histogram
is a type of histogram that acts as a graphical
representation of the tonal distribution in a digital
image. It plots the number of pixels for each tonal
value. By looking at the histogram for a specific
image a viewer will be able to judge the entire tonal
distribution at a glance. Image histograms are present
on many modern digital cameras. Photographers can
use them as an aid to show the distribution of tones
captured, and whether image detail has been lost to
blown-out highlights or blacked-out shadows.
The horizontal axis of the graph represents
the tonal variations, while the vertical axis represents
the number of pixels in that particular tone. The left
side of the horizontal axis represents the black and
dark areas, the middle represents medium grey and
the right hand side represents light and pure white
areas.The vertical axis represents the size of the area
that is captured in each one of these zones. Thus, the
histogram for a very dark image will have the
majority of its data points on the left side and center
of the graph. Conversely, the histogram for a very
bright image with few dark areas and/or shadows will
have most of its data points on the right side and
center of the graph. One of the most basic and
simple, yet powerful tools in image enhancement is
the histogram. This tool is simply a frequency count
of the intensity levels of each digitized point, or
pixel, contained in the image. Utilizing the
information contained in a histogram allows us to
improve the contrast of an image. This information
may be hidden from the human eye; however it is
readily acquired by use of a computer. Whereas the
Human Visual System (HVS) can only distinguish
approximately 100 levels of gray shades, the
computer can detect an almost infinite number of
levels. The practical limiting factor for the computer
is the number of various intensity levels recognizable
by the digitizing equipment.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
III SUMMARY OF EXISTING SYSTEM
cross-calibration/normalization, filtering can
be used to reduce granularity in the original images
The self-normalization procedure can be used for
image analysis or classification purposes, as well as
during the training or modeling phases, and can be
performed using more than two images. Ratio
information is often used with SAR images.
However, such a measure is not symmetrical and is
nonlinear. Moreover, after the preprocessing steps
presented here, noise is no longer modeled as
multiplicative. Intensity of the image is not accurate.
Methodologies based on the use of COSMO/Skymed
SAR Data”. Images acquired in different acquisition
modes from Cosmo/Skymed satellites have been used
for the experiments.
For example, using the histogram, underdeveloped or over-developed photographs can be
restored or enhanced to produce an image usable by
the HVS. Assuming the histogram reveals a number
of intensity levels all located in the low intensity
range, each current value can be mapped to a new
level so that the new histogram is scaled to cover the
entire range of available intensity levels.
IV PROPOSED SYSTEM
A simple approach to image segmentation is to
start from some pixels (seeds) representing distinct
image regions and to grow them, until they cover the
entire image.For region growing we need a rule
describing a growth mechanism and a rule checking
the homogeneity of the regions after each growth
step. The proposed preprocessing chain, followed by
the color image generation process, makes it possible
to obtain better and more easily understandable visual
results than the original images, for a successive
photo interpretation analysis aimed at identifying the
changes that have occurred in the pair of images.
The proposed self-normalization procedure can
be used for image analysis or classification purposes,
as well as during the training or modeling phases, and
can be performed using more than two images.
Because of this project, the applications of the
findings to flood monitoring are the main focus. The
method has also been successfully used to manage
responses to other disasters such as earthquakes and
tsunamis, or to carry out sea monitoring through
polarimetric SAR, such as in the project
“Development of Imaging and Monitoring
ISSN: 2231-5381
Fig:-Block Diagram
It involves an automatic adaptive self-normalization
procedure that works with various sensor settings, so it
didn’t make calibration unnecessary and it didn’t
produce calibration errors. Time complexity is low.
V IMPLEMENTATION
Matlab is a program that was originally
designed to simplify the implementation of numerical
linear algebra routines. It has since grown into
something much bigger, and it is used to implement
numerical algorithms for a wide range of
applications. The basic language used is very similar
to standard linear algebra notation, but there are a
few extensions that will likely cause you some
problems at first.
MATLAB (matrix laboratory) is a numerical
computing environment
and fourth-generation
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
programming language. Developed by Math Works,
MATLAB allows matrix manipulations, plotting
of functions and data, implementation of algorithms,
creation of user interfaces, and interfacing with
programs
written
in
other
languages,
including C, C++, Java, and Fortran.
MATLAB was first adopted by researchers
and practitioners in control engineering, Little's
specialty, but quickly spread to many other domains.
It is now also used in education, in particular the
teaching of linear algebra and numerical analysis, and
is popular amongst scientists involved in image
processing. The MATLAB application is built around
the MATLAB language. The simplest way to execute
MATLAB code is to type it in the Command
Window, which is one of the elements of the
MATLAB Desktop. When code is entered in the
Command Window, MATLAB can be used as an
interactive mathematical shell.
Sequences of
commands can be saved in a text file, typically using
the MATLAB Editor, as a script or encapsulated into
a function, extending the commands available.
MATLAB provides a number of features for
documenting and sharing your work. You can
integrate your MATLAB code with other languages
and applications, and distribute your MATLAB
algorithms and applications.
VI. PERFORMANCE EVALUTION
Feature extraction involves simplifying the
amount of resources required to describe a large set
of data accurately. When performing analysis of
complex data one of the major problems stems from
the number of variables involved. Analysis with a
large number of variables generally requires a large
amount of memory and computation power or a
classification algorithm which over fits the training
sample and generalizes poorly to new samples.
Feature extraction is a general term for methods of
constructing combinations of the variables to get
around these problems while still describing the data
with sufficient accuracy.
4
3.5
3
2.5
2
1.5
1
0.5
0
Existing Proposed
system
system
Fig 2:- Performance of existing and proposed
systems
VII. CONCLUSION
The accuries of ettracting flodded region can’not be
achieved by the existing system thus we adapt a seeding
region segment grow alg by which the accurices of
defecting flooded region will be more accurate we have
presented here. A new cross-calibration/normalization
processing method for SAR images has been
implemented that is able to reciprocally calibrate images.
ACKNOWLEDGEMENT
I would like to thank Dr. B. Bharathi, Head
of the Department, Department of Computer Science
and Engineering and Kamlesh Murari for his
encouragement and support.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 9 Number 6 - Mar 2014
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