 Open Access
 Total Downloads : 1121
 Authors : Pranali A. Hatwar, Dr. Heena R. Kher
 Paper ID : IJERTV4IS010560
 Volume & Issue : Volume 04, Issue 01 (January 2015)
 Published (First Online): 26012015
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Analysis of Speckle Noise Reduction in Synthetic Aperture Radar Images
Pranali A. Hatwar Elec. & comm. of Eng. Dept.
A.D. Patel Institute of Technology
New V. V. Nagar, Gujarat388121
Dr. Heena R. Kher
Elec. & comm. of Eng. Dept.

Patel Institute of Technology
New V. V. Nagar, Gujarat388121
Abstract Synthetic aperture radar (SAR) is an active imaging system that can achieve high resolutions both in range and azimuth. Speckle noise is one of the most critical disturbances that alter the quality of SAR coherent images. Before using SAR images in automatic target detection and recognition, the first step is to reduce the effect of speckle noise. In this paper, filtering techniques have been used on the input image and statistical parameters are calculated for the output images obtained from all filters for performance measurement and these are implemented in MATLAB.
Keywords Synthetic aperture radar (SAR), speckle noise, statistical parameter.

INTRODUCTION
The idea behind SAR is to synthesize the effect of large aperture by moving small aperture radar along the flight path to obtain much finer resolution. While moving along the flight path, the SAR system records reflected waves from the imaged surface at different instants. Coherent processing of this reflected waves from the illuminated area of different range and azimuth results in the formation of 2D SAR images [1]. However, such recorded SAR raw data contain unwanted artifacts, which result in granular appearances in SAR image. Those granular parts of SAR image are known as speckle, which is multiplicative in nature and degrades the SAR image quality significantly, leading to loss of crucial information. Therefore, speckle filtering is a critical preprocessing step for many SAR image processing tasks, such as segmentation and edge detection.
Several methods have been proposed for speckle noise reduction in SAR images. Some methods average out the speckle noise by taking multiple "looks" at a target in single radar sweep [3]. The average in this case is the incoherent average of the look [17]. The other methods involve signal processing using adaptive and nonadaptive filters. Non adaptive filtering, such as Mean and Median filters, is simpler to implement and requires less computational power, while adaptive speckle filtering is better in preserving edges and details in hightexture areas such as forests and urban areas [8].
In this paper, the adaptive methods concern for eliminating speckle noise in SAR images. In Section II, gives a brief review of SAR despeckling methods that are present in literature. In section III, the Adaptive despeckling filters and algorithm for speckle noise reduction are discussed for SAR images. In Section IV, estimation of statistical
parameter for performance measurement is explained. In section V, an experimental result for applying these filters on some real SAR images and compares it with existing speckle removal methods. Finally, in Section VI, we conclude the paper.

A SHORT OVERVIEW OF SAR DESPECKLING METHODS
Many adaptive filters for SAR image denoising have been proposed in the past. The simplest approaches to speckle reduction are based on temporal averaging, median filtering, and Wiener filtering. Ahmed S. Mashaly et al. [8] proposed Adaptive morphological filter method significantly suppressing speckle noise and preserving the potential targets.
Recently, there has been considerable interest in using the wavelet transform as a powerful tool for recovering SAR images from noisy data [4]. When multiplicative Conta mination is concerned; multiscale methods involve a pre processing step consisting of a logarithmic transform to separate the noise from the original image. Then, different wavelet shrinkage approaches are employed. More specifically, methods based on multiscale decompositions consist of three main steps: First, the raw data are decomposed by means of the wavelet transform, then the empirical wavelet coefficients are shrunk through a thresholding mechanism, and finally, the denoised signal is synthesized from the processed wavelet coefficients through the inverse wavelet transform. Alin Achim et al. [12] suggest method Waveletbased image denoising nonlinear SAR. Proposed method based on wavelet decomposition using daubechies and MAP processor is used to estimate signal and noise component instead of thresholding technique. Charles Alban Deledalle et al. [3] proposed Patch based Non local estimation is used to eliminate speckle with increasing spatial resolution and gives better performance for speckle reduction in SAR intensity image. Method focusing on two methods which are measuring patches similarity and estimate parameters of interest from similar patches. Alin Achim et al.
[11] suggest method MAP filter Based on the HeavyTailed Rayleigh Model. Initially shows the subband decomposition of log transformed SAR images can be accurately modeled by alphastable distributions. Xie et al. developed a similar method by fusing the wavelet Bayesian denoising technique with Markovrandomfieldbased SAR image regularization. 
ADAPTIVE DESPECKLING FILTERS FOR SAR IMAGES
The despeckling filtering is to move a kernel over each pixel in the image and apply a mathematical calculation using the pixel values under this kernel, and then replace the central pixel with the calculated value. The kernel is moved along the image one pixel at a time until the entire image has been covered. In the preceding subsections, we will give a brief review on the mathematical principles of these filters as we will use them in the comparative study with filter.
The implementation of this filter is based on defining a circularly symmetric filter with a set of weighting values M for each pixel:
(3.3)
Where, T is the Euclidean distance between the central pixel of the filter window and its neighbors pixels, while Damp Factor is an exponential damping factor and A is given by:
The smoothed pixel value given by:
A. Lee and enhancedLee filters
The lee filter is basically used for speckle noise reduction. This filter is based on the assumption that the mean and variance of the pixel of the interest is equal to the local mean
R (P1* M1 P2 * M2 … Pn * Mn ) (M1 M2 … Mn )
(3.4)
and variance of all pixels with in the moving kernel [20]. The resulting gray level value R for the smoothed pixel is:
(3.1)
Where, )
S= standard deviation of intensity within filter window
=central pixel of filter window
=mean intensity with filter window
The weighting function W is a measure of the estimated noise variation coefficient with respect to the image variation . The number of looks parameter ENL is the effective number of looks of the radar image. ENL is used to estimate the noise variance and it controls the amount of smoothing applied to the image by the filter. Moreover, ENL should be close to the actual number of looks, but it may be changed if the image has undergone resembling. The user may experimentally adjust the ENL value to control the effect of the filter. A small ENL value leads to more smoothing while a large ENL preserves more image features.
EnhancedLee filter divided the SAR image into areas of three classes [16]. The first class corresponds to the homogeneous areas in which the speckles may be eliminated simply by applying a low pass filter, or equivalently, averaging in multilook processing. The second class corresponds to the heterogeneous areas n which the speckles are to be reduced while preserving texture. The third class is the areas that include the isolated point targets that the filter should preserve. The resulting grey level value R for the smoothed pixel is:
(3.2)
Where,W=
Where, … are grey levels of each pixel in filter window,
and… are weights computed for each pixel as defined above. The use of large values for damp factor allows better preservation of sharp edges, but reduces the smoothing effect. The use of small values for damp factor increases the smoothing effect, but does not preserve sharp edges well.
The enhanced Frost filter is similar to the enhanced Lee filter [16] in that it considers three different types of image areas separately: homogeneous, heterogeneous, and areas of isolated point targets. The filter output is:
(3.5)
Where, is the result of convolving the image with a circularly symmetric filter whose weighting values M for each pixel is: Where,A=
Rf (P1* M1 P2 * M2 … Pn * Mn )
(M1 M2 … Mn )
C. Kuan and enhancedKuan filters
The kuan filter transforms the multiplicative noise model into an additive noise model [18]. This filter is similar to Lee filter but uses a different weighting function. The resulting grey level value R for the smoothed pixel is:
(3.6)
Where,
The enhanced kuan filter is similar to the enhanced Lee filter in that it considers three different types of image areas separately: homogeneous, heterogeneous, and areas of isolated point targets. The filter output is:

Frost and and enhancedFrost filters
Frost filter is designed to smooth out noise while retaining edges or shape features in the image through using an exponentially damped convolution kernel [19]. This kernel adapts itself according to the statistics of the local features.
Where,
(3.7)

Root mean square error (RMSE)
Start
Mean square error (MSE) is given by:
N
MSE
i j 1
[f(i, j) F(i, j)]2 / N 2(4.2)
Input SAR image
Where, f is the original image F is the image denoised with some filter and N is the size of the image.
Preprocessing of SAR image
RMSE
MSE
(4.3)
Add Speckle Noise in SAR
Denoise image output
RMSE [4] is an estimator in many ways to quantify the amount by which a filtered/noisy image differs from noiseless image.
Apply Denoising procedure on Degraded SAR image

Peak signal to noise ratio (PSNR)
PSNR is the ratio between possible power of a signal and the power of corrupting noise that affects the fidelity of its preservation [4].
PSNR 20log10 (255 / RMSE)
(4.4)
Figure 1: Algorithm for Speckle Noise Reduction in SAR Images

Gamma MAP filter

To apply the MAP (Maximum a posteriori) approach to speckle reduction, the a priori knowledge of the probability density function of the scene is required. With the assumption of a gamma distributed scene, the GMAP filter is derived with the form similar to enhanced Lee [16] with different
filter model ( ) for heterogeneous areas:
Higher the PSNR gives lower the noise in the image, i.e. higher image quality.
D. Correlation Coefficient (CoC)
Correlation gives the linear relationship between two signals [4] with respect to strength and direction, and its value lies between 1 to +1. The correlation is 1 for increasing linear relationship, 1 for decreasing linear relationship, for all the other cases the value lies between 1 to +1.
(3.8)
CoC
(g g)(g g)
(g g)2 (g g)2
(4.5)
Where,


ESTIMATION OF STATISTICAL PARAMETERS The parameters which are used in the filter performance
evaluation are Signal to Noise Ratio (SNR), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Correlation Coefficient (CoC).

Signal to noise ratio (SNR)
SNR compares the level of desired signal to the level of background noise. The higher the SNR ratio, the lesser obtrusive the background noise is. SNR in decibels is defined
as [4]:
Where, g and are original and images denoised with some filter respectively, and and are the means of the original image and image denoised with a few filters respectively.


EXPERIMENTAL RESULTS
In this section, simulation results are obtained by processing several test SAR images using filtering techniques and we compare the results with speckle filtering methods. In order to be able to quantify the improvement achieved by our method, we have first degraded original noiseless images with synthetic speckle in a controlled manner.
g e
SNR 10log( 2 / 2 )
(4.1)
Where,is the variance of the noise free image and is the variance of error (between the original and denoised image). Brighter regions have a stronger signal due to more light, resulting in higher overall SNR.

(b) (c)
Table II: Comparison of different denoising Filters for SAR image
gangotri.jpg Corrupted by Speckle Noise with Variance = 0.4
Filter
SNR (dB)
PSNR (dB)
MSE (dB)
RMSE (dB)
CoC
Elapsed time (sec.)
Lee filter
5.4843
12.4789
3674.4
60.6171
0.6165
16.317789
Enhanced Lee
11.8286
18.8232
852.6226
29.1997
0.8877
13.548386
Frost filter
10.2552
17.2498
1224.9
34.9987
0.8175
35.249355
Enhanced frost
9.7874
16.7820
1364.2
36.9351
0.7946
36.327350
Kuan filter
5.4868
12.4814
3672.4
60.6000
0.6166
15.350475
Enhanced kuan
5.4879
12.4825
3671.4
60.5921
0.6166
15.321556
Gmap filter
5.4490
12.4436
3704.4
60.8637
0.6130
13.895759

(e) (f)
(g) (h) (i) Figure 2: Results taken at variance= 0.01 and ENL=3025 on
SAR image gangotri.jpg. (a) Original image [22] (b) Noisy image (c) Lee filter (d) Enhanced lee filter (damp factor=0.8)

Frost filter (damp factor=0.8) (f) Enhanced Frost filter (damp factor=0.8) (g) Kuan filter (h) Enhanced kuan filter (i)
Gamma MAP filter
Table I: Comparison of different denoising Filters for SAR image
gangotri.jpg Corrupted by Speckle Noise with variance = 0.01


CONCLUSION

Filter 
SNR (dB) 
PSNR (dB) 
MSE (dB) 
RMSE (dB) 
CoC 
Elapsed time (sec.) 
Lee filter 
12.4167 
19.4113 
744.6480 
27.2882 
0.9045 
14.073607 
Enhanced Lee 
15.4810 
22.4756 
367.7198 
19.1760 
0.9492 
14.102054 
Frost filter 
15.3425 
22.3371 
379.6414 
19.4844 
0.9474 
36.745613 
Enhanced frost 
15.1290 
22.1236 
398.7726 
19.9693 
0.9435 
36.953505 
Kuan filter 
12.4193 
19.4139 
744.1933 
27.2799 
0.9045 
13.988123 
Enhance kuan filter 
20.9499 
27.9445 
104.3828 
10.2168 
0.9853 
13.321367 
Gmap filter 
20.9476 
27.9422 
104.4375 
10.2195 
0.9853 
13.397407 
The use of filter in Digital Image Processing improves the image to a great extent. The model preserves the appearances of structured regions. In case of Synthetic Aperture Radar (SAR) Images, Texture and land surfaces have been enhanced. The performance of the algorithm has been tested using statistical parameter measures. Many of the methods are failures to remove speckle noise present in the Synthetic Aperture Radar (SAR) images, since the information about the variance of the noise may not be able to identify by the methods. The Performance of the Speckle noise reduction model for Synthetic Aperture Radar is well as compared to other filters. Lee filters smoothest the image data, without removing edges or sharp features in the images. Enhanced lee filter and enhanced frost filter divide the image into areas of three classes which are homogeneous area, heterogeneous area and isolated point. In the homogeneous area in which speckle eliminated by averaging the multilook processing, in the heterogeneous area in which speckle are reduced while preserving the texture and preserving texture in isolated point target. Kuan, Enhanced kuan and Gamma MAP filter reduces speckle while preserving edges in SAR images.
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