SAR Image Classification by Multilayer Back Propagation Neural Network

IJMTST | International Journal for Modern Trends in Science and Technology
Volume 1, Issue 2, October 2015 36
SAR Image Classification by Multilayer Back
Propagation Neural Network
Syed Asif Ali K. Venkata Ramaiah
PG Scholar, Department of CSE Assistant Professor & Head, Department of CSE
Chebrolu Engineering College, Chebrolu Chebrolu Engineering College, Chebrolu
Abstract - A novel descriptive feature extraction method of
Discrete Fourier transform and neural network classifier for
classification of Synthetic Aperture Radar (SAR) images is
proposed. The classification process has the following stages (1)
Image Segmentation using statistical Region Merging (SRM) (2)
Polar transform and Feature extraction using Discrete Fourier
Transform (3) Neural Network classification using back
propagation. This is generally the first step in image analysis.
Segmentation subdivides an image into its constituent parts or
objects. The level to which this subdivision is carried depends on
the problem being solved. The image segmentation in this study
is performed using Statistical Region Merging proposed
Richard Nock and Frank Nielsen. The key idea of the Statistical
Region Merging model is to formulate image segmentation as an
inference problem. Here the merging procedure is based on the
theorem. Feature vectors as the input for the neural network.
Polar transform is applied to segmented SAR image. The
rotation problem under the Cartesian coordinates becomes the
translation problem under the polar coordinates.
Keywords: Back-propagation algorithm, feature extraction,
MSTAR database, SAR images, SRM segmentation.
I. INTRODUCTION
There has always been a need of protecting the messages
that are sensitive in nature. Such messages if exposed to some
intruder may pose a threat to nation’s security or company’s
critical decisions. Thus, such information must be secured at
any cost and to serve the purpose there has been a trend to
encrypt or hide the secret information. Cryptography (derived
from Greek work ‘kryptos’ meaning hidden and ‘graphein’
meaning to write) is used to encode the text to make
itunderstandable. Steganography (composed of Greek word
‘steganos’, meaning covered and ‘graphein’ meaning to
write) on the other hand, is used to hide the text behind some
other media. Cryptography may draw the suspicion of the
intruder towards the text that is in encoded format.
Steganography do not lures the eavesdropper as it hides the
message. Steganography can be classified based on the type of
media it uses to hide the text[1]. These are as
follows:Synthetic Aperture Radar (SAR) is a coherent radar
system that generates high resolution remote sensing
imagery. SAR imagery is used in finding comparatively small
mobile or immobile targets for military applications. The
need is to classify the targets using the SAR images. SAR
images containing objects that are small, influenced by
speckle still requires efficient classification technique to
correctly classify objects. The foci of this study are on
providing an advanced classification techniques for SAR
images.
II. SYSTEM DESIGN MODEL
The proposed method is used to classify vehicle astank or
armed personal carrier of SAR images. Areference database is
maintained with the SAR image,which are released by
MSTAR database. In proposed method, the SAR image
classification is done by employing feature extraction
algorithm to extract the stable, repeatable and distinctive
features of the SAR image and then by matching these
features with the features of reference images. Proposed
method introduces a SAR classification method with rotation
invariance. The rotation invariance feature is represented by
the absolute valueof Fourier coefficients of polar image of the
SAR. Then SAR image can be distinguished by feeding those
features into a multi-layered BP neural network. The block
diagram of proposed method is represented in Fig. 1.
Fig. 1: Block diagram of the proposed method

A. Image segmentation:
In this section, a method forsegmenting the object from
the SAR image is proposed.Image segmentation is a
technique for extractinginformation from an image. This is
generally the firststep in image analysis. Segmentation
subdivides animage into its constituent parts or objects. The
level towhich this subdivision is carried depends on
theproblem being solved. The image segmentation in
thisstudy is performed using Statistical Region
Mergingproposed Richard Nock and Frank Nielsen.
Fig. 2: (a) T72-Tank (b) BMP2-armored personnel carriers (c) BTR60- armored
personnel
The keyidea of the Statistical Region Merging model is
toformulate image segmentation as an inference
problem.Here the merging procedure is based on the
theorem‘The independent bounded difference inequality’. It
isthe reconstruction of regions on the observed image,based
on image on which the true regions we seek arestatistical
regions whose borders are defined from asimple axiom.
Second, the model shows the existenceof a particular blend of
statistics and algorithmic toprocess observed images
generated with this model, byregion merging, with two
statistical properties. Theregions in the query image are
merged using the SRMsegmentation procedure to obtain the
object regionfrom the image. The segmentation procedure
outputwas shown in (Fig. 2) which shows the region
mergedimage and the shows the segmented output.
B. Polar transform:
The segmented SAR image isconverted from Cartesian
coordinates to logarithmicpolar coordinates. The rotation
problem under theCartesian coordinates becomes the
translation problemunder the logarithmic polar coordinates.
The imagetransform from Cartesian to logarithmic polar
coordinates is introduced.
C. Feature extraction:
Feature vectors as the input for the neural network. Polar
transform is applied to segmented SAR image. The rotation
problem under the Cartesian coordinates becomes the
translation problem under the polar coordinates. Then the
Fourier transform applied to the output of the polar
transform. Then the feature vectors will be obtained as shown
in Fig. 3.
Fig. 3: Feature vector computation
D. Fourier transform:
The output of the transformation represents the image in
the Fourier or frequency domain, while the input image is the
spatial domain equivalent. The absolute value of Fourier
coefficients will not change after the image is rotated. The
absolute values of Fourier coefficients are used as feature
vectors, then the feature vectors are rotation invariant. The
Fourier coefficients can be used to reconstruct the image
under the logarithmic polar coordinates. The number of
Fourier coefficients maybe be infinite, but with increasing
frequency, the amplitude of the coefficients will be reduced
significantly. So the Fourier coefficients above a certain
frequency can be ignored. Here fifteen co efficient used as
feature vectors.
E. Neural network training and classification:
Multilayer back propagation neural network is taken as
the network architecture for the present application. After
choosing the network, the number of neurons in each layer
has to be decided. The number of neurons in the output layer
is fixed. the neural network input is Fourier co efficient. By
applying polar transform to the segmented image and then
applying Fourier transform to that, feature vectors are
obtained. The number of hidden layers in the network and the
number of neurons in each layer is chosen by trial and error

method based on the performance function until it reaches the
specified goal.
III. SIMULATION RESULTS
The proposed method is required to find the feature
vectors. The database contains 3 images. BTR60, BMP2, T72
are used as the reference for experimentation. In this study,
SAR ATR experiments were performed using the MSTAR
database to classify three targets as shown in Table 1. The
image data are composed of SAR images chips roughly
centered on three types of military vehicles: the T72, BTR60
and BMP2 (the T-72 is a tank and the other two vehicles
are armored personnel carriers) as shown in Fig. 2.The
segmented image and Neural Network Analysisas shown in
Fig. 5. Based on the features extracted from the SAR images,
fifteen parameters of Fourier coefficients values are given as
input to train the network as shown in Table 2. These input
values are compared with those image feed into the network.
Then the matching is performed. The network is trained with
the parameters corresponding to three types of SAR image, to
their respective targets. After training the performance
function reaches the goal for all the samples.
Fig. 4: a) Original Image b) segmented image
Fig. 5: a) Original Image b) segmented image c) Neural Network Analysis.

CONCLUSION
In this study, a new SAR image classification method has
been proposed. The image of the SAR is acquired using a
MSTAR database. Then the SAR image of the BTR60,
BMP2, T72 (armored personnel carriers & Tank) has been
subjected to the segmentation process. The Image is
segmented using the Statistical Region Merging (SRM)
method. After that the feature vectors are extracted using
Fourier descriptor and it given in to the neural network.
Neural network will trained in to the three types of SAR
Images. The DWT based SYM wavelet coefficients perform
the better quality in the image with more than 35dB in Peak
Signal to Noise Ratio. The classification rate of the proposed
algorithm is around 88%. The future study is to Automatic
target recognition of SAR images with reduced time.
REFERENCES
[1] JBennamoun, J. and G.J. Mamic, 2002. Object Recognition Fundamentals
and Case Studies. Springer, ISBN: 1-85233-398-7.
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Series in Engineering Series. ISBN-1-898563-52-7. Minoru, F., O. Sigeru,
T. Fumiaki and K. Toshihisa, 1992. Rotation invariant neural pattern
recognition system with application to coinrecognition. IEEE T. Neur.
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World Automation Congress, 2008 (WAC 2008), International Symposium
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ISBN: 978-1-889335-38-4, IEEE Catalog No. 08EX2476.
[4] Neagoe and A. Ropot, 2009. A New Neural Approach for Pattern
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[5] Fusion Technologies, NATO Science for Peace and Security Series-C:
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SAR Image Classification by Multilayer Back Propagation Neural Network

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