An assessment of software defined networking approach in surveillance using sparse optimization algorithm

Journal Pre-proof An assessment of software defined networking approach in surveillance using sparse optimization algorithm T.K.S Rathish Babu, N.M. Balamurugan, S. Suresh, L. Sharmila

PII: DOI: Reference:

S0140-3664(19)31590-7 https://doi.org/10.1016/j.comcom.2019.12.061 COMCOM 6118

To appear in:

Computer Communications

Received date : 5 November 2019 Revised date : 14 December 2019 Accepted date : 30 December 2019 Please cite this article as: T.K.S.R. Babu, N.M. Balamurugan, S. Suresh et al., An assessment of software defined networking approach in surveillance using sparse optimization algorithm, Computer Communications (2020), doi: https://doi.org/10.1016/j.comcom.2019.12.061. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier B.V.

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An Assessment of Software defined networking approach in surveillance using Sparse Optimization Algorithm T.K.S Rathish Babu1 Professor1, Department of Computer Science and Engineering1 Sridevi Women’s Engineering College1, Hyderabad. [email protected]

[email protected]

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N.M.Balamurugan2 Associate Professor , Department of Computer Science and Engineering2 Sri Venkateshwara College of Engineering1, Sriperumbudur, Chennai 2

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S.Suresh3 Assistant Professor3, Department of Computer Science and Engineering3 Panimalar Engineering College3, Chennai [email protected]

L.Sharmila4 Assistant Professor (S.G) , Department of Computer Science and Engineering4 Alpha College of Engineering4, Chennai

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[email protected]

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Abstract: Unmanned aerial vehicles (UAV) can be used as basic elements of the sensor network or an upgrade of existing network that are built with static wireless sensor nodes. Wireless Networks (WN) are utilized across in surveillance in all domains like natural disasters, agriculture, water, forest, military, buildings, health monitoring, disaster relief & emergency management, area and industrial surveillance, due to its wider applicability. Software Defined Networking (SDN) provide a hopeful resolution in bendy supervision WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDNbased supervision in structure of WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. Synthetic Aperture Radar (SAR) images are difficult to analyze due to the presence of speckle noise. Speckle noise must be filtered out before applying to other image processing applications. Three Layered Feed Forward Back Propagation Neural Network (TLFFBPNN) has been proposed to suppress the speckle noise. GLCM properties have been extracted and Back propagation training algorithm is used to train the neural network.

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Keywords: SDN, SAR, Speckle noise, Contour wavelet transform, Wiener Filter, Neural Network, SSI, SSIM, EPI, ENL.

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1 Introduction SAR images are inevitably corrupted by multiplicative noise during acquisition and transmission. The actual SAR image is not always in good quality because of the presence

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of Speckle Noise (SN). Therefore, it is very essential to reduce the speckle noise considerably before applying to other operations such as Segmentation, Compression,

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etc[1]. Also, it is important to improve the accuracy of the possible subsequent processing such as feature extraction, object recognition. The aim at any de-speckling algorithm is, it has no blurring impact at the image and makes no adjustments on edges of the SAR image. Multi look processing and filtering are the two methods used to suppress the speckle noise

filtering is utilized after the data is stored.

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2 Related Works

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of radar satellite image. Multi look processing is utilized at the data acquisition stage while

There are various SAR image de-speckling filters such as mean filter, median filter, gamma filter, sigma filter, Lee filter, wiener filter, etc. These filters suppress the speckle noise by means of smoothening the homogeneous regions and diminishing the edges of the image. Some unwanted blur has been introduced in the de-speckled images due to smoothening. To overthrow the above issue, Partial Differential Equations (PDE) based despeckling method was developed [2]. Even though PDE method suppresses the speckle noise, it fails to preserve the edge characteristics of the SAR image. Translation Invariant

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second era Bandlet Transform (TIBT) with Possibilistic Fuzzy C-Means clustering (PFCM) [3] and TIBT with Unsupervised FCM clustering[4] were utilized to de-speckle the edge evacuated image. Due to more repetitive information, the little relics still exist in the despeckled SAR image. The Rayleigh Maximum Likelihood Estimation (RMLE) based de-

Journal Pre-proof speckling filter was proposed to de-speckle the SAR image[5]. A unified de-speckling sifting toolbox was created for satellite images[6]. About 65 textures features and 15 execution parameters can be analyzed. The configurable parameters such as, size of the moving Window, number of iterations are still to be changed by the user to accomplish ideal

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outcomes and also the computational time is to be made strides. Dual-formulation-based Adaptive Total Variance (ATV) regularization technique was used for de-noising[7]. The

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parameter adjustment of the TV regularization is percolated based on the speckle noise level predicted through wavelets.

In patch ordering based de-speckling, two-Stages of winnowing technique are suggested. In the first stage, the patches of radar image is sifted by learned SSC

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(Simultaneous sparse coding). Then the coarse separating result is additionally filtered out through 2-D wavelet hard thresholding in the second stage of filtering[8]. Even though, SSC delivers solid speckle reduction, the second phase of filtering still creates new antiquities at

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high frequency components. Spatially adaptive wavelet-based technique that utilizes a Bayesian MAP estimator is suggested for de-speckling the real SAR image[9]. This approach smoothens speckle noise in homogeneous area. A local adaptive median filter was developed and it made use of neighborhood stats to identify SAR multiplicative noise also to supplant it with a nearby median value[10]. Bayesian processor is developed to gauge noise free curvelet coefficients in light of utilizing 2-D GARCH-GG (2-D Generalized Autoregressive Conditional Heteroscedastic Generalized Gaussian) model[11]. The despeckled images have bend let-like antiques when the noise is more. Data driven tight

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through strategy is taken from the logarithmic transformed image and utilized for commotion evacuation[12]. Adaptive Multi temporal SAR Image Filtering Based on the Change Detection Matrix is developed for Satellite images[13]. This techniques fails to preserve the edge and structural characteristics of the original image.

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3 Speckle Noise Reduction Methods Speckle noise diminishes the look and quality of SAR images which in turn decreases the performances of SAR image processing and Analysis. Therefore, the noise

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must be suppressed before processing the SAR images using various image processing techniques like multiple-look processing, adaptive and non-adaptive filters, etc.

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3.1 PMD Model

SAR images are affected by various noises which include both non-additive and additive noise. The non additive ie, multiplicative noise in SAR image is otherwise called as

I  f *u  n

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speckle noise and it is defined in[14],

(1) Where, I is the captured SAR image, u is the multiplicative component of speckled image, n is the additive component of the speckled image. Since, the SN is multiplicative

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in nature, suppressing the speckle noise in the remote sensing images is important. Only the multiplicative component is considered and the additional component is not to be considered. Therefore equation (3) can be written as, I  f *u

(2)

The process used to de-speckle the SAR image using Perona Malik Diffusion (PMD) model is given in fig.1. In order to convert the multiplicative component into additive component, log transform has been applied on both the sides of the above equation. Therefore, the above equation becomes,

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S  log( I )  log( f )  log(u )

(3)

PMD model depends on heat diffusion equation which is defined in[15], S  .cS  t

(4)

Journal Pre-proof Here, S is the log transformed noisy SAR image, c is the diffusivity co-efficient, is the gradient operator,

.



is the divergence operator. This equation was developed by

Perona and Malik [15]. It can be expanded as, S  .c (log( f )  log(u ))  t

(5)

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This equation is more effective for suppressing the speckle noise while preserving the edge attributes of the SAR image. In this case, initially the gradients of the SAR image

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grad x, grad y are estimated in both x and y directions respectively. Then diffusivity is computed by using the below equation which is stated in[16], 1

c

2

k2

(6)

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1

S

Here, k is a small constant used to control the diffusivity. It must be chosen between 5 to 100. The value of diffusivity 'c' changes at different regions of the image. The gradient

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of the image is high during the edges of the images, this leads diffusivity to have a small value. This consequently preserves the edges from smoothing. After that, the diffusivity is multiplied with the gradient images grad x and grad y images respectively. Thereafter, the divergence of grad x and grad y images are computed and both the images are fused together to get the resultant divergence image. At last, take exponential transform to get the

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de-speckled SAR image.

Fig.1 Work flow of PMD Model

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3.2 VISU Shrinkage VISU Shrinkage (VS) is one the wavelet shrinkage methods. In VS, thresholding is performed by applying universal threshold (UT) and it is proposed by donoho and Johnstone. The block diagram of VS de-speckling method is shown in fig.2. In this method,

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there is no need to calculate the threshold value at every subband level. Initially, 2D-DWT is applied on the speckled SAR image where the image is separated into four subband

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regions namely LL LH, HL and HH. Then UT soft thresholding is performed on the wavelet coefficients. There are two types of thresholding viz. Hard thresholding produces unwanted artifacts in the de-speckled images while soft thresholding yields visually pleasing images. In soft thresholding, the coefficients below UT are set to zero while important coefficients

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are replaced by UT value. The shrinkage of the wavelet co-efficient is given by[17],

   Noise 2 log(n)

(7)

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where,  Noise is the standard deviation (STD) of noise and 'n' is the number of pixel elements in the image. While UT selection, it is most necessary to evaluate the STD of the noise (σ Noise) from the wavelet coefficients. It is obtained by using the below formula,  

MAD 0 . 6745

(8)

where, MAD is the median of the absolute values of the wavelet coefficients (HH band of speckled image). In this experiment, 0.4349 is used as the universal threshold. At last, compute the 2D-IDWT to get the de-speckled SAR image. This technique is simple and effective, it removes speckle noise co-efficient that are insignificant relative to UT. The UT

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tends to be high for large values of MAD, it over-smooths the speckled SAR image and affects many original image co-efficient along with speckle noise. Also, it has been observed that, threshold value should be smaller value for soft thresholding. For despeckling of SAR images, VISU shrink does not adapt well to reduce the SN of SAR image.

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Fig.2 VISU Shrinkage (VS) De-speckling Method

3.3 Speckle Reduction Using WFSOC and SSFSOC

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To preserve the edge characteristics of SAR images, canny edge detection operator is applied with the threshold value of 0.2. Then, the image is gone through the contour

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wavelet transform to partition the low recurrence and high recurrence subband. In the SSFSOC method, self snake diffusion filter is used to reduce SAR multiplicative noise in low recurrence subband. Since, wiener filter has good speckle noise reduction capability and

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good preservation characteristics, the low recurrence subband of WFSOC method is sifted through wiener filter. Fusion method is used to combine the low recurrence subband and high recurrence subband. Finally, the de-speckled image can be obtained by taking inverse

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contour wavelet transform. The procedure of SAR image de-speckling using WFSOC

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method is given in fig.3.

Fig.3 De-Speckling of SAR Image using WFSOC

Journal Pre-proof The proposed method has the following steps. Step.1 Initially, speckle noise with STD of 0.2 is added with the original SAR image in order to make the image noisy. Step.2 Edge detection is performed through canny operator with the standard threshold

Step.3 Contour wavelet transform is applied on the SAR image.

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Step.4 Obtain the low recurrence and high recurrence subband.

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value of 0.2.

Step.5 Wiener filter is applied to the low recurrence subband.

Step.6 Sparse Optimization filter is applied to the high recurrence subband. Step.7 De-speckled image can be obtained by taking inverse Contourlet transform.

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3.3.1 Wiener Filtering for Lower recurrence subband

Wiener filtering is used to reduce the speckle noise present in the lower recurrence subband (LL). The LL band contains more information when compared with HH subband.

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Wiener filter is good in speckle noise suppression. Therefore, it is used in LL band for speckle noise suppression. With the noise variance σn2, the wiener filter is defined as, W f (u , v ) 

Ps Ps   n2

(9)

3.3.2 Sparse Optimization (SO) for higher recurrence subband Sparse optimization technique is used in WFSOC and SSFSOC methods to retain the original characteristics of SAR image. The inadequate degree can be characterized as straight blend of ψ, and it is given in [18], f 

N



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k 1

k



(10)

k

Where, ψ is the scanty premise and α is the inadequate coefficient. Change f with

estimation framework Φ which is unrelated among ψ to get the estimation y. It is given in[18],

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y   f    

Where,

   .

This paper embraces the contour wavelet as sparse optimization

premise, Gaussian irregular matrix is the arbitrary estimation matrix represents the four directional subband and smothers the multiplicative noise of the higher recurrence subband

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with sparse optimization algorithm. The inadequate coefficient α with the improved pursuit algorithm [19], the optimization problem can be expounded by the following formula which

arg min  l satisfies f l   l o

2



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is taken from [18],

Where, fl and αl is the lth component of f and α. ||.||0 is the zero-mean,

(12) arg min  l

o

is the

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non-zero element. The multiplicative (speckle) noise concealment of the higher recurrence components of the radar image could be depicted as accompanying scanty streamlining model:

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B1'( n )  arg min B1( n )  T B1( n ) satisfies B1( n )  B1'( n )  

(13)

Where, ΦT is the contrary transform of Φ, ΦTB1(n) is the frequency co-efficient of higher recurrence B1(n) under Φ and ‘n’ is the control of higher recurrence component. At last, the low recurrence subband and high recurrence subbands are fused using inverse contour wavelet transform (λ = 0.00).

3.4 Three Layered Feed Forward Back propagation Neural Network (TLFFBPNN) The block diagram of TLFFBPNN is given in fig.4. Back propagation is a Feed forward network which has single hidden layer and it is named as three-layer neural

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network. Each neuron in a TLFFBPNN is represented by a circle. All the layers used in TLFFBPNN is connected with interconnection weights. When the number of hidden layer increases, then the computational complexity and the convergence time also increases. To reduce the complexity single hidden layer is used in this network. The term feed forward

Journal Pre-proof represents that the information flows only in the forward direction. In feed forward of information, initially a set of randomly selected weights are given to the input data and an output is estimated. The initial value of randomly selected weights must be between 0 to 1. Back propagation training algorithm has been used to train the TLFFBPNN. In back

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propagation, the margin of error is calculated, then the corresponding new weights are computed and the weights are adjusted accordingly to decrease the error. TLFFBPNN

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repeats both forward and back propagation until the weights are calibrated to accurately

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predict an output.

Fig.4 De-speckling of SAR image using TLFFBPNN

3.4.1 Number of Neurons in the Hidden Layer

Using large number of neurons in the hidden layer may result in over fitting and using too few neurons may cause under fitting problem. Additionally, a large number of neurons may increase the time taken to train the TLFFBPNN. Here, rule-of-thumb method

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is used to find the number of neurons in the hidden layer. Let Ni be the number of input neurons, Nh be the number of hidden neurons and No be the number of output neurons. Rule-of-Thumb: There are three rules. They are given as follows,

Journal Pre-proof I. N i  N h  N o II.

Nh 

2 Ni  No 3

III. N h  2 N i

The training algorithm of back propagation involves four steps.

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3.4.2 Back propagation Training Algorithm

1. Initialization of weights- Small random values are selected as initial weights.

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2. Feed forward- Each input neuron in the input layer receives an input feature and transmits

to each of the hidden neurons in the hidden layer. The activation function is calculated by each hidden neurons and sends it to output layer. The output unit again calculates the activation function to form the response of the given input feature. Sigmoid function is used

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as the activation function.

3. Back propagation of errors- Each output unit compares activation yo' with its target value yo

to determine the associated error. Based on the error, the new weights W*mo and W*nm are

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computed and adjusted to decrease the error. 4. Updation of the weights

3.4.3 Calculation of change in weight

The total error in the TLFFBPNN is represented by the following equation, 

1 ( yo  yo )2 2

(14)

To reduce this error, the TLFFBPNN’s weight must be adjusted. The relationship between

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the weight and the error can be written as,

* Wmo 

W*  

 Wmo

 W

(15)

The error ɛ is not the direct function of weight. The equation (15) can be expanded as follows,

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*   Wmo

 yo Wneto yo Wneto Wmo

(16)

Consider the terms individually and simplify,



1



eWneto

1e 

Wneto 2

 yo (1yo)

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

yo  1eWneto  Wneto Wneto

(17)

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1   ( yo  yo )2   2    y  y    o o   yo yo

Wneto Wmo ym   ym Wmo Wmo

(18)

(19)

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To find the weight change for a hidden layer to output layer, Substitute equation (17-19) in (16).

* Wmo  ( yo  yo ) yo (1 yo ) ym  o ym

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Where,  o   ( yo  y o ) yo (1  yo )

(20)

To find the Weight change for an input layer to hidden layer, we can write  E y W neto  y m W netm * W nm      y W neto y m  W netm W nm     y  y  y (1  y o )W mo y m (1  y m ) y n

      W y  (1  y  ) y  o

mo

m

m

(21)

n

  m y n

4 Experimental Results for SAR image

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4.1 Dataset for the experiment

The images used for the experiment are obtained by the U.S. Geological Survey

(USGS). These images represent one of a variety of land remote sensing derived image information products that are produced for educational and reference purposes rather than

Journal Pre-proof for scientific interpretation. These images are geotagged but have not been georeferenced and do not comprise map projection. Many slideshow photos were

produced prior

05/01/2013 and would not have the equal metadata and capabilities to be had with more modern products. The SAR images can be downloaded or accessed from the USGS LRS Gallery

website:

https://remotesensing.usgs.gov

and

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Image

https://landsat.visibleearth.nasa.gov. To approve the adequacy of

TLFFBPNN method,

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SAR image is utilized for this work. Two gatherings of trials are recreated to assess the execution of the proposed technique. (1) Restrain the multiplicative (speckle) noise of SAR image by applying TLFFBPNN method. (2) Comparing impalement of proposed method

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with different existing methods.

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12 13 14 Fig.5 De-speckled SAR images using VISU Shrinkage (VS) method

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Since, it is hard to get the real SAR speckled image, in this work speckle noise

with the STD of 0.2 is added equally with the original image to make the SAR image uproarious. The speckle noise is suppressed by using various methods and the outputs are given in fig (5-9). The de-speckled image using VS method is shown in fig.5. In this

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to 1. Here 0.4349 has been used as UT for thresholding the SAR image.

12 13 14 Fig.6 De-speckled SAR images using PMD model

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The de-speckled image using PMD model is shown in fig.6. The VS method provides poor edge preservation. To overcome that, in PMD model the diffusivity co-efficient is used for preserving the edges of the images. The value of diffusivity co-efficient must be low for preserving the edges of the images. The obtained minimum and maximum values of diffusivity for different SAR images have been listed in table 1. The gradient value will be high during the edges of the image. From equation (6), it can be noticed that, diffusivity will be low when the gradient has larger value. The PMD model does not produce the

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satisfactory speckle reduction results. Speckle noise still exists in the de-speckled image. SSFSOC and WFSOC methods are used to eliminate the SN while preserving the edge attributes of the original image.

Journal Pre-proof Table.1 The minimum and maximum value of Diffusivity for SAR images Minimum 0.0201 0.0202 0.0108 0.0131

Maximum 0.9997 0.9952 0.9981 0.9972

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Images Eastem Kariba Colombia Deforestation Airport SAR Image Ellis Island

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12 13 14 Fig.7 De-speckled SAR images using SSFSOC method

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De-speckled SAR images using SSFSOC method are given in fig.7. This method is same as WFSOC method, self snake filter is used instead of wiener filter to reduce the SN in the lower recurrence subband. A little amount of speckle noise still exists in the de-speckled image. To remove the SN present in the SSFSOC method, WFSOC method has been developed and the output images are shown in fig.8. SSFSOC and WFSOC methods use SO technique to smother the SN of the higher recurrence subband, stifling a considerable

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measure of commotion as well as saving the edge attributes of the SAR image. The WFSOC produces good speckle noise reduction with better PSNR, ENL and SSI parameter values. In order to improve the PSNR and ENL values, TLFFBPNN has been used to de-speckle the SAR image.

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12 13 14 15 a) Edge detection using canny operator with the standard threshold 0.2

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13 14 c) Skeleteon Extraction of SAR image

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a) Training performance

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b) Training state

c) Training Regression

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13 14 d) De-speckled SAR image Fig.9 De-speckling of SAR images using TLFFBPNN method

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The de-speckled SAR images using TLFFBPNN is given in fig.9. In this work, the number of neurons in the input and hidden layers are 10 and 7 respectively and this value

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satisfies the rule of thumb. 10 statistical features including GLCM properties of each noisy images are obtained for training. Using the GLCM properties and the back propagation training algorithm the TLFFBPNN is trained. After training TLFFBPNN, the test images are given to the network to get the de-speckled image. From fig.9 (a) the TLFFBPNN gives the best performance with minimum mean square error at the fifth epochs. The learning rate is represented by mu, used for training is 1e-05 is shown in fig.9 (b). The training will be terminated when both the gradient and mu curves become saturated with a minimum value. Fig.9 (C) shows the cross co-relation between data points and curve fitting plots for training,

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validation, testing and overall performance. The de-speckled SAR images using TLFFBPNN are given in fig.9 (d). It can be noticed that, the TLFFBPNN de-speckling technique smoothens the homogeneous areas and preserves the edge qualities of the SAR image.

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5 Performance Evaluation To evaluate the performance improvement, five sorts of performance estimation metrics such as PSNR, SSI, EPI, SSIM and ENL are used in this work. The description of those parameters are given below.

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5.1 Peak Signal to Noise Ratio (PSNR)

PSNR is the most widely used as performance analyzing parameter. The higher

PSNR 10log10

Where,

MAX

2 I

MAXI2 MSE

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value of PSNR gives better quality of de-speckled image. PSNR is defined in[20],

(22)

is the maximum possible value of the original image. MSE is the

5.2 Speckle suppression index (SSI)

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mean square error, which must be lower value.

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SSI is used to find the efficiency of the de-speckling algorithm, which is defined in [21], SSI 

Var ( F ) Mean( I ) . Mean( F ) Var ( I )

(23)

SSI should be the lowest value and the range lies between [0,1]. 5.3 Edge Preservation Index (EPI)

Another parameter EPI can be computed by comparing the edges of the de-speckled image and the noisy SAR image. An efficient de-speckling method must have higher value in Edge Preservation Index EPI [18],

Gradient of the Filtered Image Edges Gradient of the Noisy Im age Edges

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EPI 

5.4 Equivalent Number of Looks (ENL)

(24)

Journal Pre-proof Another parameter ENL is used to measure the speckle noise level in the homogenous region of the filtered image. Higher value of ENL shows an effective suppression of speckle. It mainly depends on the size of the image. ENL is defined in[22], mean 2 ( F ) Variance 2 ( F )

(25)

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ENL 

5.5 Structural Similarity Index Measure (SSIM)

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When suppressing the speckle noise in SAR image processing, preserving the edges is the most challenging one. Therefore the additional parameters like EPI and SSIM have been evaluated here. SSIM is utilized to quantify the closeness amid the original SAR and

SSIM 



2 y

X   Y   2

2

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Where,



4 xy X Y 2 x

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the de-speckled SAR image. SSIM is defined in [23],

X 

1 N

N

x i 1

i

Y

,

1 N  yi N i 1

 N

1 1 N 2 2  x2   xi  X   y  N  1 i 1 yi  Y N  1 i 1 ,

 xy 

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

2

1 N  xi  X  yi  Y  N  1 i 1

Comparison of PSNR, SSIM, SSI and EPI are tabulated below. From table.2, it can be plainly said that, the TLFFBPNN de-speckling method accomplishes the higher index value in PSNR. In the meantime, it gives better edge preservation (table.5) when compared with other existing methods.

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Table.2 Comparison of PSNR for TLFFBPNN, WFSOC, SSFSOC, PMD and VS methods SSFSOC

PMD

VS

63.8569

WFSOC 63.7536

63.8028

52.1763

42.3671

65.3589

64.2568

62.4735

51.8818

39.968

3

63.5482

61.1458

61.4409

52.9317

40.6302

4

63.5486

62.9562

62.4903

52.9911

43.1549

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TLFFBPNN

1 2

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64.7856

62.2563

61.9389

54.6492

42.8092

6

64.8597

62.5562

61.8095

53.1611

40.8132

7

63.4512

62.9562

63.2331

53.2029

40.7996

8

64.9612

63.9521

63.2484

59.536

41.5335

9

61.1199

60.8526

60.0004

50.9111

39.5120

10

62.3739

61.8459

61.6181

52.621

42.1377

11

64.5056

63.4158

61.5580

56.9259

42.5481

12

63.4582

62.9851

62.6398

54.0348

44.5755

13

62.6667

61.8450

61.7268

14

64.4568

63.5168

62.7773

15

62.3444

61.2351

61.7015

53.953

42.6382

51.6258

42.2745

50.9693

39.8482

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The comparison of structural similarity index is given in table.3. The higher value of similarity index yields good structural characteristics among the original SAR image and the de-speckled SAR image. From the table, TLFFBPNN and WFSOC methods have higher SSIM value when compared with the SSFSOC, PMD and VS methods. It can be

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visually noticed that (fig. 6,7 and 8), some small amount of noise still exists in SSFSOC, PMD and VS methods. This reduces the structural similarity of the de-speckled image. Table.3 Comparison of SSIM for TLFFBPNN, WFSOC, SSFSOC, PMD and VS methods WFSOC 99.9478 99.9556 99.9158 99.8452 99.91254 99.9147 99.9658 99.9358 99.8769 99.9352 99.9725 99.9231 99.9136 99.9348 99.9098

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TLFFBPNN 99.9514 99.9652 99.9547 99.9548 99.5040 99.7082 99.97265 99.9462 99.9142 99.9632 99.4712 99.9231 99.8547 99.9574 99.9154

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Images 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

SSFSOC 93.9413 91.9592 89.9008 89.9213 89.9111 89.9087 90.9321 92.9329 93.8573 89.9067 89.951 90.9231 95.9063 91.9267 92.9053

PMD 66.849 61.448 62.2109 75.1325 74.7074 70.2267 70.1141 65.5493 75.5878 63.2829 68.6786 77.1133 74.0905 69.7448 61.1656

VS 53.2925 55.1821 50.2841 67.2074 67.7353 50.5408 57.7747 60.4057 63.3427 57.5908 59.2994 53.228 62.172 55.5487 52.4558

The TLFFBPNN method secures the edge attributes and reduces the SAR

multiplicative noise of the real SAR image to an extraordinary. This method provides good speckle suppression index which is tabulated in table.4. The TLFFBPNN method yields low

Journal Pre-proof speckle suppression index. The SSI value must be lies in the range of [0,1] for better speckle noise suppression. From the tabulated values, it can be said that the TLFFBPNN method has better speckle noise suppression ability. Table.4 Comparison of SSI for TLFFBPNN, WFSOC, SSFSOC, PMD and VS methods WFSOC

SSFSOC

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

0.237 0.2893 0.2604 0.1651 0.1043 0.162 0.1633 0.12 0.1196 0.1568 0.2899 0.2778 0.2524 0.1931 0.2497

0.3932 0.3614 0.3659 0.4419 0.4394 0.413 0.4124 0.3855 0.4446 0.3722 0.4039 0.4536 0.4358 0.4102 0.3597

0.5878 0.5879 0.5876 0.5677 0.5769 0.5876 0.5878 0.5878 0.5873 0.4876 0.5879 0.5777 0.5876 0.5878 0.5876

PMD 0.5878 0.5878 0.5873 0.5872 0.5825 0.5585 0.5826 0.5872 0.5869 0.5894 0.6825 0.5954 0.5962 0.5896 0.6892

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VS

0.5879 0.6234 06485 0.5981 0.6125 0.6321 0.6578 0.5921 0.6869 0.5463 0.6956 0.5978 0.6215 0.6433 0.6159

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The obtained edge preservation index value is given in table.5 for all methods. For better edge preservation, the range of edge protection index is [0,1]. The TLFFBPNN gives the average value of edge preservation index in the range of 0.7905, which is higher than the other existing methods, indicates that it significantly preserves the edge characteristics of the original SAR image [24].

Table.5 Comparison of EPI for TLFFBPNN, WFSOC, SSFSOC, PMD and VS methods TLFFBPNN 0.8712 0.8456 0.8125 0.7007 0.7561 0.8158 0.7851 0.8203 0.8124 0.8691 0.7412

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Images 1 2 3 4 5 6 7 8 9 10 11

WFSOC 0.6853 0.6856 0.6715 0.6562 0.6485 0.6896 0.6841 0.6625 0.6452 0.6712 0.6632

SSFSOC 0.6197 0.7421 0.6652 0.6225 0.7321 0.5314 0.7812 0.7236 0.5233 0.6177 0.6319

PMD 0.5696 0.6864 0.4648 0.4203 0.5044 0.5081 0.4908 0.408 0.4701 0.3895 0.5037

VS 0.377 0.605 0.3624 0.3144 0.3405 0.5392 0.4409 0.4568 0.4275 0.3974 0.3616

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0.7956 0.7292 0.7104 0.7988

0.6512 0.7000 0.6925 0.6925

0.6256 0.7261 0.6812 0.5452

0.6761 0.5583 0.6758 0.5049

0.2267 0.3542 0.3851 0.4733

Table.6 Comparison of ENL for TLFFBPNN, WFSOC, SSFSOC, PMD and VS methods

89.5371 89.8975 90.2258 90.6473 91.0595 91.4445 91.8468 92.3009 92.7745 93.2833 93.6915 94.2206 94.3703 95.2814 95.8195

SSFSOC 65.9730 65.8863 66.1715 66.1579 66.0765 66.0645 66.4068 66.2259 66.2199 66.2335 66.2486 71.3666 46.4787 75.5623 65.3106

PMD 32.1306 31.9166 32.6301 32.521 32.3019 32.2159 33.519 33.1136 32.9809 32.8495 32.7393 37.7617 38.6591 48.5133 46.1891

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TLFFBPNN 152.0876 153.1771 154.2072 155.2871 156.1569 157.2246 158.2299 159.4002 160.9968 162.0471 163.0322 164.4569 165.5511 167.0586 168.1859

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Images 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

VS 16.5855 16.4847 16.8531 16.7928 16.689 16.6441 17.2613 17.0707 17.0083 16.946 16.8995 15.4300 15.4811 13.3889 14.8317

The estimated Equivalent Number of Looks (ENL) is tabulated and given in

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table.6. The highest value of ENL indicates excellent speckle suppression. The Region of Interest (ROI) of 150 x 150 pixels of images has been selected to evaluate the ENL of homogenous region. TLFFBPNN method produces the highest value of ENL, meanwhile

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VS method gives the lowest ENL.

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(c)

(e)

Fig.10 Comparison of performance metrics for TLFFBPNN, WFSOC, SSFSOC, PMD and VS methods a) PSNR b) SSI c) EPI d) SSIM e) ENL

The correlation of execution measurements in graphical portrayal is given in Fig.10. From fig.9(d), the TLFFBPNN and WFSOC methods have similar de-speckling performance of SSIM but TLFFBPNN method has higher value in PSNR, SSI, EPI and ENL fig.9(a, b, c and e). Overall, from the graphical representation it is noticed that the TLFFBPNN method gives better de-speckling performance in terms of PSNR, SSI, SSIM,

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EPI and ENL.

Journal Pre-proof 5 Conclusion

In this paper, TLFFBPNN has been used to de-speckle the SAR images and compared with other de-speckling techniques. The VS de-speckling method mainly depends on the UT

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value and provides poor edge preservation. In order to preserve the edge attributes PMD model has been used for noise reduction. The diffusivity coefficient has been used for

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preserving the edges of the images. The PMD model does not produce the satisfactory speckle reduction results. Speckle noise still exists in the de-speckled images. SSFSOC and WFSOC methods use SO technique to suppress the speckle noise present in the higher frequency subband. Self snake filter is used for SSFSOC and wiener filter is used in

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WFSOC method for lower frequency subband. The WFSOC produces better speckle noise reduction with the PSNR in the range of 62. To improve the PSNR and ENL values of the de-speckled image TLFFBPNN is used. GLCM properties and Back propagation training

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algorithm have been used to train the TLFFBPNN. Rule-of-Thump has been used to decide the number of hidden neurons. The TLFFBPNN method display the better visual effects of speckle noise reduction with good edge preservation. This method yields better image quality metrics of PSNR, SSI, SSIM, EPI and ENL when compared with other de-speckling methods.

References

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Number 1, January(1) 2018.

Journal Pre-proof Conflict of Interest This paper has not communicated anywhere till this moment, now only it is communicated to your esteemed journal for the publication with the knowledge of all co-authors. Ethical approval

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This article does not contain any studies with human participants or animals performed by any of the authors.

Journal Pre-proof AUTHORSHIP STATEMENT

An Assessment of Software defined networking approach in surveillance using Sparse Optimization Algorithm Manuscript title:

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All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Computer Communications. Authorship contributions Please indicate the specific contributions made by each author. The name of each author must appear at least once in each of the three categories below.

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Category 1 Conception and design of study: T.K.S Rathish Babu, N.M.Balamurugan, S.Suresh, L.Sharmila

acquisition of data: T.K.S Rathish Babu, N.M.Balamurugan, S.Suresh, L.Sharmila ,

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,

,

;

analysis and/or interpretation of data: T.K.S Rathish Babu, N.M.Balamurugan, S.Suresh, L.Sharmila ,

,

,

.

Category 2 Drafting the manuscript: T.K.S Rathish Babu, N.M.Balamurugan, S.Suresh, L.Sharmila

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revising the manuscript critically for important intellectual content: T.K.S Rathish Babu, N.M.Balamurugan, S.Suresh, L.Sharmila

Category 3 Approval of the version of the manuscript to be published (the names of all authors must be listed): T.K.S Rathish Babu, N.M.Balamurugan, S.Suresh, L.Sharmila.