DENOISING TECHNIQUES FOR SYNTHETIC APERTURE RADAR DATA A REVIEW

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1 International Journal of Computer Engineering & Technology (IJCET) Volume 6, Issue 9, Sep 2015, pp , Article ID: IJCET_06_09_001 Available online at ISSN Print: and ISSN Online: IAEME Publication DENOISING TECHNIQUES FOR SYNTHETIC APERTURE RADAR DATA A REVIEW Arundhati Ray Space Applications Centre, ISRO, Ahmedabad, India [PhD Scholar (external candidate) CSE Dept at Nirma University, Ahmedabad] B Kartikeyan Space Applications Centre, ISRO, Ahmedabad, India ABSTRACT Synthetic Aperture Radar (SAR) data have been increasingly gaining importance in the field of remote sensing applications due to its all weather, and day and night imaging capabilities. However, the images which are obtained after a series of complex signal processing operations on the received data, are affected by a grainy kind of noise, called speckle, which renders the data difficult for further interpretation and analysis. Thus, image denoising becomes an important and mandatory step in the SAR processing domain. A variety of denoising filters for SAR images have come up in the recent years. Wavelet based SAR data denoising techniques have also been gaining popularity due to its space-frequency localization capability and the capacity to analyse data at different scales. In this paper, we are making a comprehensive review of some of the well known filtering techniques for SAR data denoising. Key words: Adaptive Filters, Decomposition, Denoising, MAP, MMSE, Mother Wavelets, SAR, Speckle, Wavelet Based Thresholding. Cite this Article: Arundhati Ray and B Kartikeyan. Denoising Techniques for Synthetic Aperture Radar Data A Review. International Journal of Computer Engineering and Technology, 6(9), 2015, pp INTRODUCTION SAR and its active mode of signal acquisition entails a very complicated sensor design, signal processing, image processing and its interpretation [1-3]. The fundamental theory behind SAR needs transmission and reception of linearly frequency modulated chirp signals, and Doppler processing of the encoded returned echo [1],[4]. Coherent signal processing is done to attain high spatial resolution. 1 editor@iaeme.com

2 Arundhati Ray and B Kartikeyan However due to this coherent nature of signal, a grainy type of noise is inherent in the image, which degrades the radiometric quality of the data. This is known as the speckle noise whose characteristics are described in detail in [1] [2] [4] [5]. A variety of application in the areas of agriculture, land use type discrimination, forestry, biomass estimation, geology, flood mapping, disaster zone mapping, oil spill detection etc. is possible with SAR data. Due to speckle noise, the accuracies of image analysis involving classification, segmentation, texture analysis, target detection etc however are severely affected. In the field of image processing, denoising is still a challenging task to the researchers. Reducing noise always comes at a cost of some other aspect of image degradation such as blurring, spatial resolution degradation and edge smearing. Apart from that, the type of noise present in the image is a critical factor in formulating the denoising scheme. Most of the images in the optical domain are afflicted with additive white Gaussian noise. Images produced by coherent processing such as those of SAR, USG etc are however, tarnished with speckle noise, which is more difficult to remove. Here, we discuss briefly about the characteristics of speckle noise followed by some of the well known speckle filters, which are being used by SAR users. Then we give a review of some of the wavelet based image denoising techniques, with special emphasis on SAR images. 2. SPECKLE NOISE Speckle noise is multiplicative in nature. This type of noise is formed as a result of the random interference between the coherent returns from active imaging sensors such as LASER (Light Amplification by Stimulated Emission of Radiation), USG (Ultra SonoGram) and SAR. Its response is represented as: S = x * η (1) where S is the output scattering coefficient of a target, x is its true value, and η is the speckle noise affecting the input signal. Fully developed speckle noise has the characteristic of multiplicative noise as shown by G April et al [4]. Speckle model is explained by using the signal geometry for a SAR sample in Fig:1. The basic assumption is that, one resolution cell contains the scattering from a large number of scatterers with a wavelength which is comparable to the roughness of the terrain or object being imaged. Hence the response from one cell is a coherently summed signal from many scatterers. Figure 1 SAR Speckle Model 2 editor@iaeme.com

3 Denoising Techniques For Synthetic Aperture Radar Data A Review 3. DENOISING OF SPECKLE NOISE IN SAR In the field of image filtering, the basic aim of denoising technique is to remove noise, irrespective of the spectral content of the noisy signal. The basic aim of SAR image denoising is to improve the backscattering coefficients in homogeneous areas and the edges in the images too. The filter should also preserve the spatial variability. i.e. the textural information for areas with texture (like forests). The challenge thus, lies in cleaning the image without sacrificing the geometric resolution. Noise removal techniques for optical images which are corrupted by Additive White Gaussian Noise (AWGN) are based on ideal low pass filtering(lpf), Butterworth low pass filtering, Gaussian low pass filtering etc, which remove the low frequency noise present in such data sets. A comprehensive review of denoising for optical images is done by A Buades et al [6]. Here it has been pointed out, that in all denoising methods, there is an assumption on the noise model, and a generic image smoothness model, which is local or global. In the optical image domain, a huge plethora of denoising techniques have come up, and are still being developed. Several denoising techniques based on spatial domain as well as frequency domain exist However, since speckle is a coherent type of noise, general noise removal techniques which are effective for additive noise present in optical images, do not work effectively for such data. One method of speckle noise reduction is the multilook technique which is applied to the SAR signal, during SAR data processing in the azimuth direction, in the frequency domain [4]. Here the signal bandwidth is split up into smaller segments, and these smaller bandwidth signals are processed in the frequency domain, and after that, these are incoherently summed up to get the output having better radiometric resolution, i.e. less noise. However the spatial resolution is degraded, in the process. For N look processing the effective spatial resolution becomes N times the original spatial resolution, while the radiometric resolution is improved by a factor of N. Geometric resolution degradation is, however, not desired in most of the application work, as edges are perturbed. In order to circumvent this problem, several filtering techniques have evolved, over the last decade or so, some of which have also been implemented in the commercial software packages, available for the SAR users. The papers discussing some of the well known filters in this domain are given in [7-12]. 4. SPATIAL DOMAIN DENOISING Spatial domain filtering are classified into two classes: Non-adaptive and adaptive. The non-adaptive filters take the parameters of the full image and not the local properties of the signal or the sensor characteristics. These are not proper for nonstationary scene signals. FFT is an example from this class. Such filtering also eliminates actual image information, particularly, the high-frequency information. Adaptive filters adapt their weightings across the image to the speckle level. The applicability of filtering and the choice of filter type involves tradeoffs. Adaptive speckle filtering is better at preserving edges and details in high-texture areas. Several such filters such as Lee, extended Lee, Frost, extended Frost, Kuan, Gamma MAP(Maximum-A-posteriori Probability) etc are extensively used for SAR denoising as shown by Lee et al, Frost et al, Kuan et al and Baraldi et al in [7-12]. Lee filter and Lee_sigma filter utilize the statistical distribution of the pixel values within a chosen moving kernel to estimate the value of the central pixel in the window. Here it is assumed that the mean and variance of the pixel of interest is equal to the mean and variance of all the pixels within the chosen window. Frost filter, 3 editor@iaeme.com

4 Arundhati Ray and B Kartikeyan another adaptive filter, replaces the pixel with a weighted sum of the values within the window. The weighting factor decreases with distance from the central pixel. Leesigma filter had some shortcomings, such as blurring of targets, and depressing strong targets. This was removed in the extended Lee filter [12], whereby the sigma range was estimated using the probability density functions, and by incorporating minimum mean square error (MMSE) estimates for adaptive speckle reduction. Extended Lee and Frost filters were introduced [13], to alter the performance locally according to three cases. Pure averaging is done when the local coefficient of variation(lcv) is below a low threshold, selected. Above a higher threshold the filter performs as an all pass filter, while for other cases lying in between, a balance between these two filtering is done as is done in standard Lee and Frost techniques. However these also have limitations of window size, and directional sensitivity and do not enhance the edges. The hard thresholds many a times lead to artifacts, and noisy boundaries by leaving the edges unfiltered. Gamma-MAP filter also gives good denoising performance with preservation of edges. This assumes that natural targets have Gamma distributed cross section, rather than Gaussian distribution, which is a better approximation for most SAR images. It also assumes non-stationary mean and variance parameters. Though a good filter, it produces artifacts in many of the cases, for different window sizes, and has to be used with caution, and many a times several iterations are needed to get good results, thereby increasing the computation time. Another method to produce sharp edges, is the simulated annealing technique [14]. When this algorithm is applied to despeckling SAR data, the RCS (Radar Cross Section) is modeled as a Markov random field that is governed by the Gibbs distribution of the conditional probability of the RCS. Simulated annealing filters produce relatively good results if parameters are selected appropriately. However, processing times are very long when compared to other filters, and the results are dependent on the judicious choice of iterations and the filtering parameters. Another breakthrough in the domain of data denoising came with the introduction of speckle reducing anisotropic diffusion (SRAD) technique by Y Yu et al [15]. This method introduced a diffusion method which is specifically tuned to denoise USG and radar images which are afflicted with speckle noise. This paper deals with the edge sensitive diffusion for speckled images, using a method which is similar to the conventional anisotropic edge sensitive diffusion for images which are corrupted by additive noise, such as in optical images. Here it is shown that, in the case of Lee and Frost filter, adaptive filtering is done using the coefficient of variation, whereas the SRAD algorithm exploits the instantaneous coefficient of variation, which is shown to be function of the local gradient magnitude and Laplacian operators. The technique is based on the minimum mean square error (MMSE) approach for filtering, as in Lee, and Frost, but is the edge sensitive extension of the conventional adaptive speckle filters. Here it is mentioned that the existing diffusion techniques use a homomorphic approach [16], while this work deals with linear domain SAR data thereby useful information with regards to SAR data is preserved. A denoising technique which reports a concept of block-matching and 3D algorithm (BM3D) which is modified to suit SAR image noise, is reported in [17]. Research work in the BM3D was initially reported by Dabov et al [18]. This technique exploits the specific non-local image modeling through a process called, grouping and collaborative filtering. The grouping process finds mutually similar 2-D image blocks and stacks these together in 3D arrays, while collaborative filtering produces individual estimates of all the blocks, by filtering them jointly, by applying transform domain shrinkage of the groups. Hence both local and non local 4 editor@iaeme.com

5 Denoising Techniques For Synthetic Aperture Radar Data A Review characteristics of natural images are taken into consideration. Finally the effectiveness of the subsequent shrinkage is related to the sparsity of the real signal [19]. This method is supposed to be a state of the art denoising for images with additive noise. However it is observed by Hasan et al [20] that for images rich in texture or those corrupted with higher level noise, this technique does not give the best results. So scope exists to make more improvement. It is to be noted that most of these techniques have been tested on images corrupted by additive white Gaussian noise (AWGN). For speckle noise removal, this technique is not so widely applicable, and Parilli et al have used this concept with the wavelet domain shrinkage to denoise SAR data in [17]. A recent work for SAR image denoising is reported by J Liu et al [21]. Here a method called total variation (TV) regularization has been resorted to, in order to filter multiplicative noise. It is reported here that though TV means is highly researched area, this can sometimes give rise to undesirable staircase artifact. To circumvent this problem, high order TV norms have been judiciously used to balance the edge and smoothness regions. This method has been shown to give better efficiency in terms of SNR and structure similarity index. In a recent work by Zhang et al [22], it is pointed out that with self adaptive sliding window based methods, the local stationary assumption of classical speckle filters can be satisfied better than those with fixed windows. But region variance is always affected by speckle and is not accurate enough specially for textures, edges and point type targets. Hence these are only effective for homogenous regions. In this paper, trade-off between homogeneous areas and point target preservation is resorted to, by an adaptive threshold technique which is treated as a linear function of saliency map, and the threshold range is estimated by Monte Carlo algorithm based on a given significance level and the number of looks in the image. This technique claims to give good noise removal without sacrificing the sharp edges. In one of the most recent papers on SAR denoising, it is reported that nonlocal grouping and transformed domain filtering has led to the state of the art denoising techniques, and that this has been extended to SAR images based on disjoint local regions with similar spatial structure[23]. Each such region is then denoised by linear minimum least square error (LMMSE) filtering in principal component analysis (PCA) domain. Both clustering and denoising are done on image patches. This method reportedly gives good performance in terms of noise reduction and detail preservation. Here the technique has been done based on additive signal dependant noise (ASDN) model. 5. WAVELET FRAMEWORK FOR DENOISING During the last decade, Wavelet-based techniques are finding more and more applicability in noise removal, due to their space-frequency localization capability. Wavelets have been in use since the last decade for various signal and image processing tasks. The time-frequency domain analysis scope renders such technique very useful in the domains of signal processing, image compression, denoising, image enhancement, resolution enhancement, fractals etc. The fundamental idea behind this is to analyse the signal according to scale. Wavelet transforms have advantages over traditional Fourier transforms for representing functions that have discontinuities and sharp peaks, and for accurately deconstructing and reconstructing finite, non-periodic or non-stationary signals, as has been discussed in detail by Mallat[24]. Here the processing is performed at various scales and hence there is tremendous scope of feature analysis at those scales. Thus wavelets are well suited to handling signals with sharp discontinuities. This is the feature which makes wavelet based approaches extremely suitable for filtering noise. Wavelet based denoising schemes involve non 5 editor@iaeme.com

6 Arundhati Ray and B Kartikeyan linear thresholding of wavelet coefficients in time-scale transform domain, whereby the noise frequencies are suppressed. One method for finding the mother wavelet and scaling coefficients was proposed by I Daubechies[25], which ushered in a whole domain of mother wavelet generation for different research purposes. In the domain of optical images, wavelet and its derivatives have been in use since the inception of wavelet based techniques [26]. Donoho in 1995[27] had proposed thresholding techniques for denoising, which had paved the way for research on various means of thresholding for filtering out different kinds of noise in images. Research in the field of speckle noise removal using wavelet based technique gained momentum, very soon, as is discussed by Gagnon et al[28], Argenti et al[29]. Many types of wavelet functions have been in use, out of which the discrete wavelet transform (DWT) which transforms digital signals to discrete coefficients in the wavelet domain, has good capability for both signal as well as image processing applications. Hou et al[30] reported speckle reduction on SAR images based on Bayesian MAP estimation in the wavelet domain. Many papers during this period reported results of wavelet based filtering on SAR as well as medical data sets using different techniques developed, and by extending the original ideas. Some of the results have been reported by P.U Fangling et a l[31], Solbo et al[32], Gleich et al[33]. S Parrilli et al in 2012 proposed a nonlocal SAR image denoising algorithm based on LMMSE wavelet shrinkage [17]. A novel despeckling algorithm for SAR images was proposed here, based on the concepts of nonlocal filtering and wavelet-domain shrinkage. It follows the structure of the blockmatching 3D (BM3D) algorithm, which had been proposed, for additive white Gaussian noise denoising[18]. At present, BM3D is considered to be the state of the art for AWGN denoising, as has been mentioned in the previous section. Most of the filtering techniques in the wavelet domain operate in a homomorphic way for the SAR images [32]. Log transform for SAR data, however changes the basic characteristics of the SAR image, and after speckle removal and inverse logarithm, cannot be retrieved back, as has been reported by Ulaby et al [34]. Hence wavelet based SAR denoising is still an open challenge in the power domain of data. Alparone et al[35] in a recent work have reported the results of evaluation of Bayesian estimators and PDF(Probability Density Function) models for despeckling in the undecimated wavelet domain. According to their study the best suited method comes from maximum-a-posteriori (MAP) estimation of the wavelet coefficients. Since SAR is corrupted by multiplicative noise, the methods adopted in all the wavelet based work is that of homomorphic approach whereby the logarithm of the data is taken, to force the data into the additive domain. Subsequently the generic denoising filters are applied on those transformed data. Denoising techniques developed, or the thresholding methods developed are of interest to all researchers working in the image processing domain. Different mother wavelets have evolved, which are used to convert the images from the linear domain to the wavelet domain. One such technique was proposed by Daubechies [25]. DWT is used to transform the digital signals in the image to discrete coefficients in the wavelet domain. The output of the low pass filter are known as the approximation coefficients, while those from the high pass filter are called the detailed coefficients. Wavelet transform is used to decompose the image into sub-bands, and this can be repeated up to multiple levels, depending on the dimension of the image [24]. At each level of decomposition, the signal is decomposed into four sub-bands, consisting of the approximation component which is the LL component, and the detailed 6 editor@iaeme.com

7 Denoising Techniques For Synthetic Aperture Radar Data A Review components i.e. LH, HL and HH. The detailed parts contain the high frequency components which are mostly corrupted by noise. The LL part can be further decomposed using the above technique, and filtering done till the desired results are obtained. Some of the well known wavelet based thresholding methods are discussed in the next section. 6. NOISE REMOVAL USING WAVELET THRESHOLDING The method used for denoising using Wavelets involves the following steps: Discrete wavelet transform is applied on the data sets to get the sub-bands. The detailed wavelet coefficients are taken for denoising at any decomposition level. Thresholding criteria is used to suppress the noise. There are two methods of thresholding such as the hard thresholding given in equation 2 or soft thresholding given in equation 3[27]. After thresholding on one level, further levels of decomposition may be attempted based on the denoising desired, and the above procedure is repeated on the approximate coefficients. After going through the desired number of decompositions, the thresholded values are taken through inverse wavelet transform, through all the levels. Final reconstructed image gives the noise free image. (2) (3) The coefficients of the wavelet transform are usually sparse. That is, coefficients with small magnitude can be considered as pure noise and may be set to zero in order to denoise the data. The detailed wavelet coefficients are compared with a threshold value in order to decide whether it constitutes a signal or a noise, and is known as wavelet thresholding. Donoho in 1995 [27], proposed a method to reconstruct a function f from a noisy data set d. This was done by translating the empirical wavelet coefficients of d towards 0 by an amount given by: X = σ (2log(n)/n) (4) where n is the number of data points in the signal, and is the noise or standard deviation in the data which is given by an estimate in the wavelet domain as : σ 2 = [(median Y ij ) / ] 2 (5) where Y ij denotes the coefficients in the HH subband.this was found to be quite effective for Additive White Gaussian noise(awgn) which is found in optical data. Subsequently several shrinkage methods for soft thresholding have been proposed by researchers. SURE(Stein s Unbiased Risk Estimate) technique whereby the universal threshold is replaced by an adaptive SURE based thresholding which is reported to give better denoising as was reported by Zhang et al[36], and Thierry et al[37]. This also was found out to be highly efficient for optical images. Chang et al, in 2000 proposed a novel concept of denoising and compression of optical images by an adaptive, data-driven threshold for image denoising via wavelets based on soft- 7 editor@iaeme.com

8 Arundhati Ray and B Kartikeyan thresholding[38]. The threshold was derived in a Bayesian framework, and the a priori model used on the wavelet coefficients was the generalized Gaussian distribution (GGD) which is widely used in image processing applications. The proposed thresholding is adaptive to each sub-band since it depends on estimates of the parameters from the data. Experimental results show that the proposed method, called Bayes Shrink, is typically within 5% of the Mean Square Error (MSE) of the best softthresholding benchmark with the image assumed known. It is reported that it outperforms Donoho s[27] SureShrink method most of the time. Xie et al in 2002 reported speckle reduction for SAR using wavelet denoising and Markov Random Field (MRF) modelling[39]. Using MRF has been popular in the field of image processing and this was extended to SAR denoising. In this paper, a technique is developed for speckle noise reduction by fusing the wavelet Bayesian denoising technique with Markov random-field (MRF)-based image regularization. Experimental results showed that the proposed method outperformed standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalentnumber-of-looks (ENL) measures in most cases. It also achieves better performance than the refined Lee filter. It has been observed that model based despeckling mainly depends on the chosen models. Bayesian methods have been commonly used in denoising, whereby the prior, posterior and evidence probability density functions are modeled as illustrated by Chang et al[38]. A novel approach of using SURE based shrinkage to optical image denoising was proposed by Thierry et al [37]. Using the SURE and LET (linear expansion of thresholds) principles, it was shown that a denoising algorithm merely amounts to solving a linear system of equations which is fast and efficient. The very competitive results obtained by performing a simple threshold (image-domain SURE optimized) on the un-decimated Haar wavelet coefficients showed that the SURE-LET principle has a huge potential. Gagnon has reported some numerical results based on wavelet filtering of speckle noise [40]. One multi-scale speckle reduction method based on the extraction of wavelet inter scale dependencies to enhance medical ultrasound images with multiplicative noise was done using dual tree complex wavelet transform [41]. This method is reported to give good performances compared to standard spatial despeckling filters. 7. CONCLUSION Synthetic Aperture Radar data is increasingly being used in the field of remote sensing applications due to its all weather, and day and night imaging capabilities. However due to the inherent coherent processing, SAR images are corrupted by speckle noise which needs different ways of filtering. Speckle being signal dependent multiplicative type of noise, is difficult to remove using techniques which are generally suitable for additive white Gaussian noise. Over the last decade several techniques have been developed to suppress speckle noise in SAR as well as medical data. In this paper we have brought out a comprehensive list of different kinds of speckle filtering algorithms, which have evolved during the past as well as the recent times. Initially the multilook techniques were used for speckle reduction, but this came at a cost of degrading the spatial resolution. The algorithms which evolved subsequently were based on spatial filtering techniques. Compared to non adaptive techniques, the adaptive means have shown promising results. A good adaptive speckle filter should reduce the noise in statistically homogeneous areas, but preserve the features such as edges and point targets, and also improve the radiometry. Of late the wavelet based multi-resolution analysis has shown great promise for image feature detection at different scales, and using this property several denoising algorithms have 8 editor@iaeme.com

9 Denoising Techniques For Synthetic Aperture Radar Data A Review been proposed. Factors influencing denoising of images are the choice of the wavelet basis, order of the mother Wavelet, decomposition level, thresholding and shrinkage criteria chosen. Apart from this, the speckle model and statistics play a key role in the filter performance. There is no best solution to speckle reduction, as each method has its pros and cons, as has been discussed in the papers reviewed here. Similarly each technique assumes certain noise model and region based statistics. Thus, denoising of SAR images offers great scope in the field of image processing research. 8. ACKNOWLEDGEMENTS The authors would like to thank Dr S Garg, HOD, CSE Department, Nirma University, Ahmedabad for his valuable support in carrying out this work. They would like to thank Dr Raj Kumar, GD, GSAG, Dr P K Pal, DD, EPSA and Sri Tapan Misra, Director, SAC/ISRO for supporting them in carrying out this research activity. We also thank the reviewers for their valuable suggestions. REFERENCES [1] F. T. Ulaby, R.K. Moore, A.K. Fung, Microwave Remote Sensing Active and Passive, Vol-2, Artech House, [2] J. Reeves, Manual of Remote Sensing, American Society of Photogrammetry, First Edition, 1975, Vol-1. [3] R. K. Raney, Radar Fundamentals: Technical Perspective, Principles and Applications of Imaging Radar: Manual of Remote Sensing, 3 Edition, New York: Wiley Interscience, 1998, Vol- 2, 1998, pp [4] G. V. April, E. R. Harvy, Speckle Statistics in Four-Look Synthetic Aperture Radar Imagery, Optical Engineering, 30(4), 1991, pp [5] J. Bruniquel, A. Lopes, Multivariate Optimal Speckle Reduction in SAR Imagery, International Journal of Remote Sensing, 18(3), 1997, pp [6] Buades, B. Coll, J.M. Morel, A review of denosing algorithms, with a new one, A SIAM Interdisciplinary Journal, 2005, 4(2), pp <hal >. [7] J.S. Lee, Speckle Suppression and Analysis for Synthetic Aperture Radar Images, Optical Engineering, 25(5), 1986, pp [8] J.S. Lee, Digital image enhancement and noise filtering by use of local statistics, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol-2, 1980, pp [9] V.S. Frost, J.A. Stiles, K.S. Shanmugan, J.C. Holtzman, A model for radar images and its application to adaptive digital filtering of multiplicative noise, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol- 4, 1982, pp [10] D.T Kuan, A.A Sawchuk, T.C Strand, P Chavel, Adaptive noise smoothing filter for images with signal dependant noise, IEEE Transaction on PAMI-7, No-2, 1985, pp [11] Baraldi, F. Pannigianni, A refined Gamma MAP SAR speckle filter with improved geometrical adaptivity, IEEE Transactions on Geoscience and Remote Sensing, Vol-33, 1995, pp [12] J.S. Lee,J.H.Wen,T.L. Ainsworth, K.S. Chen, A.J. Chen, Improved sigma filter for speckle filtering of SAR imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol-47, 2009, pp editor@iaeme.com

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