Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring

Accepted Manuscript Speckle suppression in sar images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring Vikrant Bhateja, Anubhav Tripathi, Anurag Gupta, Aime Lay-Ekuakille PII: DOI: Reference:

S0263-2241(15)00359-0 http://dx.doi.org/10.1016/j.measurement.2015.07.024 MEASUR 3477

To appear in:

Measurement

Received Date: Revised Date: Accepted Date:

12 February 2015 4 July 2015 14 July 2015

Please cite this article as: V. Bhateja, A. Tripathi, A. Gupta, A. Lay-Ekuakille, Speckle suppression in sar images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring, Measurement (2015), doi: http://dx.doi.org/10.1016/j.measurement.2015.07.024

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SPECKLE SUPPRESSION IN SAR IMAGES EMPLOYING MODIFIED ANISOTROPIC DIFFUSION FILTERING IN WAVELET DOMAIN FOR ENVIRONMENT MONITORING Vikrant Bhateja1*, Anubhav Tripathi1, Anurag Gupta1 and Aime Lay-Ekuakille2 1

Department of Electronics & Communication Engineering, SRMGPC, Lucknow (U.P.) India. 2 Department of Innovation Engineering, University of Salento, Lecce, Italy.

* Corresponding Author Contact Details: Vikrant Bhateja, Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Faizabad Road, Lucknow-227105 (U.P.) India. Email: [email protected]. Contact No.: +91-9935483537. Abstract: Synthetic Aperture Radar (SAR) is a tool of coherent imagery utilized for meteorological and astronomical purposes. But, these images are contaminated with speckle noise which degrades the image quality and automatic information extraction becomes difficult. This paper presents an improved filtering technique which combines the Wavelets and proposed Anisotropic Diffusion (AD) filter for despeckling SAR images. The speckled image is initially decomposed into sub-bands using 2D-Discrete Wavelet Transform (2D-DWT) followed by application of modified AD filter. The diffusion coefficient presented in this modified AD filter consists of a combination of gradient and Laplacian operators. The spatial variation of this diffusion coefficient occurs in such a way that it prefers forward diffusion to backward diffusion resulting in effective reconstruction of structural content and detection of weak edges. The filtered sub-bands are then reconstructed after soft thresholding. Based on the simulation results as well as the values of image quality metrics; filtered SAR images obtained by the proposed speckle suppression methodology can be claimed better in comparison to other recent works. Key words: SAR, Despeckling, Diffusion coefficient, Multiplicative noise, Soft thresholding, 2D-DWT. 1.

INTRODUCTION

Synthetic Aperture Radar (SAR) is a tool of coherent imagery which uses signal processing to improve the resolution beyond the limitation of physical antenna aperture [1]-[2]. SAR is proven to be useful over a wide range of applications in environment monitoring, which include: sea and ice monitoring, mining, oil pollution monitoring, oceanography, snow monitoring, classification of earth terrain etc. [3]-[5]. By proper selection of operating frequencies, the microwave signal used for radiating the target in SAR can penetrate clouds, haze, rain and fog, and precipitation with very little attenuation, thus allowing operation under unfavorable weather conditions. It is a type of high-resolution radar capable of producing hundreds of megabits of data per second which enables high processing speed and fast image acquisition process [6]-[9]. In spite of being important for various purposes, SAR images are generally found to be contaminated with a special type of highly unordered and chaotic noise pattern termed as speckle noise. When an object is illuminated by a coherent source of radiation and the object has a surface structure that is roughly of the order of the wavelength of the incident radiation, the wave reflected from such a surface consists of contributions from many independent scattering areas. Interference of these de-phased but coherent waves results in speckle noise. Since, SAR imagery is directly based on interference and reconstruction through coherent microwave source; therefore, speckle is the dominating noise factor in these images [10]-[12]. Speckle noise is caused by the inherent characteristic of SAR and is a kind of multiplicative noise. It gets multiplied randomly with pixel values (in an image), resulting in a granular pattern. This not only makes the SAR images blurred and less important in terms of information content; but also poses difficulties in edge detection, segmentation and subsequent features extraction. In SAR systems, the speckle noise is generally referred as the difference between the measured and true mean values of the image pixels which degrade the visual quality of the images. Here, the statistics of speckle noise is considered to be independent of the nature of signal and noise [13][17]. Speckle reduction is needed for two main reasons: firstly to improve the human interpretation, or for visual enhancement and secondly, it forms mandatory pre-processing step for many image processing tasks. These algorithms are generally classified in three categories: Conventional spatial filtering, Multi-resolution filtering and Anisotropic Diffusion based filtering techniques [18]-[20]. A literature overview of the said categories of techniques along with a framework for present problem formulation is detailed in Section 2. The proposed work is explained

under the materials and methods in Section 3. Section 4 presents the simulation results and discusses them with the help of objective evaluation parameters which are used for quality assessment and comparison of obtained results. Lastly, Section 5 concludes the work.

2.

LITERATURE OVERVIEW ON SPECKLE SUPPRESSION

Conventional techniques for speckle suppression assumed statistical independence of original image and speckle noise; where the ratio of noise standard deviation to the mean of noisy image is assumed constant throughout. In this category of techniques, the most classical one are the Local Statistics Filters which employed the concept of local statistics filtering to generate the output image (by computing the central pixel intensity from the average intensity values of the pixels and a coefficient of variation inside the moving window). This working principle described weighted average calculation using sub-region statistics for estimating statistical measures over different pixel windows as in Lee Filter, Kuan Filter and Frost Filter [19]. Their continuation was Enhanced Lee and Frost Filter which adopted the approach based on local heterogeneity [21]. Other moderations in the local statistics filtering were presented recently in the form of hybrid filters by V. Bhateja et al. [22]. Sophisticated techniques for speckle suppression are categorized under Wavelet based and Anisotropic Diffusion based filtering techniques [18][20]. Speckle filtering techniques in either of these categories are able to provide satisfactory speckle suppression but with certain constraints. Various filtering approaches lying under these categories and their relevance in speckle suppression are discussed in sub-sections to follow: 2.1 Anisotropic Diffusion (AD) Filtering Diffusion filters removed noise from an image by modifying the image through partial differential equations (PDE). The most viable category of diffusion filters is based on Anisotropic Diffusion (AD) filtering which has been useful in reducing image noise without removing significant parts of the image content and details that are important for the interpretation. The foremost AD filter is the Perona and Malik Anisotropic Diffusion (PMAD) filter [23] which performs selectively smoothing of an image. Diffusion takes place in this method according to the prescribed partial derivative equations. Yet, its effect on images corrupted with multiplicative noise such as speckle is less satisfactory. Similar constraints were also evident in conventional Lee filter and Frost filters. By combining the positive traits of PMAD and Lee filters, in terms of instantaneous coefficient of variation, another Speckle Reduction Anisotropic Diffusion (SRAD) filter was developed by Yu and Action [24]. SRAD excels in terms of mean preservation, variance reduction and edge localization but at the expense of computational complexity. Later an improvement in SRAD filter was introduced in the form of Detail Preserving Anisotropic Diffusion (DPAD) filter [25] providing ease in implementation as well as its computational efficiency. Although, both DPAD and SRAD methods enhanced the prominent edges during speckle filtering; but the fine features of the image were eradicated due to blurring. The Oriented Speckle Reducing Anisotropic Diffusion (OSRAD) [26] technique was another AD filter which utilized Kuan filter based diffusion coefficient. The extension of the SRAD method to a matrix scheme in OSRAD is performed by finding the local directions of gradient and curvature which allows the speckle adaptive diffusion to vary in strength in the curvature directions. This filter reduced speckle content and also enhanced the contours, but in turn posed heavy computations owing to the iterative methodology. 2.2 Wavelet Based Multi-Resolution Techniques Speckle reduction filtering has also been widely explored in wavelet domain with its origination by Donoho [27]. Donoho’s Thresholding in wavelet domain applied shrinkage on wavelet coefficients of the SAR images after speckle reduction. The speckle reduction method of Zong et al. [28] involved Discrete Dyadic Wavelet Transform owing to its edge enhancement properties; but resulted in complex calculations. Nonlinear Multiscale Wavelet Diffusion (NMWD) technique [29] applied image sub-band decomposition using wavelets followed by diffusion filtering process. NMWD was based on evaluation of modulus of wavelet coefficients at each stage along with the estimation of Rayleigh mixture parameters. This was an iterative method where the filtering steps were repeated in order to achieve the desired level of noise removal. Similar works in this domain were those of Pizurica et al. [30] and Lay Ekuakille et al. [31]. Further, to address the issues of shift sensitivity and directionality of wavelet transform; Contourlet transform was proposed as a multi-directional approach by Do and Vetterli [32]. However due

to oversampling and excessive decomposition, it was proved to be more redundant than the wavelet method [33][34]. 2.3 Motivation and Problem Formulation For the past two decades, several speckle suppression techniques have been developed for removing speckle and retaining edge details in SAR imagery. It is evident that if a despeckling technique tends to remove speckle at higher noise variance levels; then it usually degrades the edges and other fine details in SAR images. As the noise variance in the image increases, a compromise has to be done between suppressing speckle content and preserving the high frequency structure of the image. Moreover, the existing spatial domain techniques (Spatial as well as AD filtering) are capable to smooth the homogeneous region but degrade the spatial resolution. On the other-hand, multiresolution techniques, preserves the spatial isotropy but do not yield satisfactory performance towards speckle removal; especially at higher noise variance levels. Therefore, in order to achieve both noise smoothing as well as preservation of structural content (irrespective of the speckle noise variance levels); a hybrid approach of applying AD filter embedded in wavelet domain is proposed.

3.

MATERIALS AND METHODS

The proposed speckle suppression methodology combines the positive traits of AD filtering like noise smoothing in homogeneous regions along with the multi-resolution and sparsity properties of 2D-DWT to carry out restoration of edges and structural content. The main modules involved in this procedure are: Preprocessing of SAR images, Wavelet Decomposition, Modified AD Filtering, Thresholding, Wavelet reconstruction and Post processing as shown in flow diagram given in Figure 1. The various modules deployed in this work are detailed in the following sub-sections: ***** FIGURE 1**** 3.1

Noise Model and Preprocessing

SAR technique is efficiently applicable under various weather conditions and is highly popular because of its ability to penetrate clouds and soil. A SAR image is a mean intensity estimate of the radar reflectivity of the region which is being imaged [1]-[2], [6]. Speckle noise in such system is referred as the difference between the measured and the true mean value. Since, the speckle noise is a multiplicative noise; therefore it can be mathematically modeled as:

G (i, j ) = R(i, j ) × S (i, j ) + A(i, j )

(1)

where: R(i, j) and G(i, j) represent original and noisy SAR image pixels respectively. S(i,j) and A(i, j) represent the multiplicative and additive noise respectively. Since, the effect of additive noise is considerably smaller than that of multiplicative noise, therefore it can be neglected and Eq. (1) may be re-written as:

G (i, j ) = R(i, j ) × S (i, j )

(2)

In the present work, the noisy SAR image (contaminated with speckle noise) is initially pre-processed by taking the log transform. The log transform performs pre-processing by slightly reducing the additive noise, present along with speckle and it further converts multiplicative noise into the additive noise. Logarithmic transform affects the speckle noise statistics which can be approximated as white Gaussian noise as given in Eq. 3(a) & (b):

ln G (i, j ) = ln R (i, j ) + ln S (i, j )

(3a)

I (i, j ) = F (i, j ) + N (i, j )

(3b)

The term, lnG(i,j) which is the SAR image after logarithmic transform is denoted as I(i,j) and the terms lnR(i,j) and lnS(i ,j), refers to the original image noisy pixels and are denoted as F(i, j) and N(i, j) respectively after logarithmic compression. 3.2

Sub-band Decomposition using DWT

During the last decade, DWT has become a popular and useful tool in the arena of signal and image processing owing to its ability to perform multi-resolution decomposition [35]. Wavelet Transform projects a two dimensional signal f(x, y) onto subspaces Zm from the signal space Z at different resolutions so that it becomes the closest representation of the signal f(x, y). This gives rise to a sequence Cmf(x, y) which represents approximations of f(x, y) at resolution 2m. Now, the detail signal Dmf(x, y) can be defined as:

Dm f ( x, y ) = Cm+1 f ( x, y ) − Cm f ( x, y) In terms of orthogonal basis functions ϕn

m

(4)

( x, y ) , the detail signal Dmf(x, y) is represented as:

Dm f ( x , y ) =

∑∑ m

f nm ( x , y )ϕ nm ( x , y )

(5)

n

These basis functions have a very special property that they all can be produced by shifting and scaling of a single function ϕ ( x, y ) called Mother Wavelet such that:

ϕ nm ( x , y ) = 2 m / 2 ϕ (2 m x − n, 2 m y − n ) Also,

(6)

f nm (t ) is represented as: f nm ( x , y ) =

∞

∞

∫ ∫

f ( x , y )ϕ nm ( x , y ) d x d y

(7)

−∞ −∞

This equation is called Direct Wavelet Transform of signal f(x, y). Therefore, the noisy SAR image is decomposed into corresponding approximation and detail sub-bands (horizontal, vertical and diagonal) with the wavelet of appropriate family. It is known that the sub-band statistics are different for different scales and orientations during image decomposition. Further, the entropy of the diagonal sub-bands is comparatively higher in comparison to those of vertical and horizontal sub-bands [43]-[44]. In the present work, therefore, the decomposition of SAR images is being done up to two levels resulting in one approximation sub-band and six detail sub-bands such that the speckle suppression could be applied on diagonal details of both the levels. 3.3

Proposed Anisotropic Diffusion (AD) Filtering Approach

It is known that AD filters employ Partial Differential Equations (PDE) based methods to resolve an image in order to get expected results of noise removal. AD filtering approach provides backward diffusion around transients and forward diffusion in the smooth areas in favor of edge sharpening and noise removal [36]. The diffusion constant of PMAD in Eq. (8) uses the gradient operator for the purpose of detection of sharp edges [23]. This concept poses constraints in case of non-sharp edges leading to unnecessary blurring (around the edges); thereby distorting the high frequency structure of the image. Laplacian operator, being a second order derivative operator consists of a zero crossing level in the middle of edges. This aspect makes this operator more robust towards effective detection of weak edges [37]. Based on this idea, the present work proposes a modified diffusion coefficient as a combination of first and second order derivative operators (gradient and laplacian). Mathematically, this can be formulated as:-

c = f (|| ΔI ||)

(8)

⎛ 1+ || ΔI || ⎞ c=⎜ ⎟ ⎝ 1+ || ∇I || ⎠

2

(9)

In the proposed formulation, the spatial variation of this diffusion coefficient occurs in such a way that it prefers forward diffusion to backward diffusion resulting in improved structural details and edge preservation. The proposed AD filter uses diffusion coefficient of Eq. (9) to remove speckle content from diagonal sub-bands through several iterations of the filter until the diffusion gets saturated. In this process, firstly, the diffusion coefficient is applied to the concerned diagonal sub-band (initialized as d0 (x, y)) by calculating gradient and Laplacian of it. After that, the directional derivatives of each pixel of sub-band is calculated in the respective direction by using a 3x3 spatial mask centered at any pixel location d(i, j). This can be mathematically expressed as under:

∇N din, j = din−1, j − din−1, j ∇NE din, j = d in−1, j+1 − d in, j

∇NW din, j = din−1, j+1 − din, j

∇Edin, j = din, j+1 − din, j ∇W din, j = din, j−1 − din, j

(10)

∇SEdin, j = din+1, j−1 − din, j ∇SW din, j = din+1, j+1 − din, j ∇ S d in, j = din+1, j − d in, j where: n denotes n-th iteration and ∇N denotes directional derivative in north direction. Similarly, N, S, E, W, NE, NW, SE and SW mentioned as subscript with ∇N denotes directional derivative in north, south, east, west, north-east, north-west, south-east and south-west directions respectively. Next, each pixel of sub-band is modified by using directional derivatives and diffusion coefficient as:

⎡cN∇Ndin, j + cNE∇NEdin, j + cNW∇NWdin, j + cE∇Edin, j ⎤ din, +j 1 = din, j + λ ⎢ ⎥ n n n n ⎢⎣+cW∇Wdi, j +cSW∇SWdi, j +cSE∇SEdi, j +cS∇Sdi, j ⎥⎦

(11)

where: λ is a constant parameter whose value is determined experimentally and cX denotes the diffusion coefficient in the respective X direction. 3.4

Thresholding

Due to the arising discontinuity at the point of threshold; the hard thresholding function is known to yield abrupt artifacts in the denoised image. The next step in this process therefore includes thresholding of rest two detail subbands (i.e. horizontal and vertical) via soft-thresholding method [32] which incorporates the signum function in its mathematical model. Soft-thresholding yields more visually pleasant images over hard-thresholding as the reconstruction process is not smooth especially in case when the noise variance levels are significantly high. In case of SAR images, hard thresholding may have adverse effect while denoising homogeneous regions [42].

In soft thresholding, the detail sub-band coefficients of horizontal and vertical sub-band having amplitude greater than t are put to zero, while reducing the amplitude of other coefficients by the quantity t. Hence, the sub-band coefficients are shrinked mathematically in the following manner: ⎧ ⎪ sgn( d i , j ) × (| d i , j | − t ), d i, j = ⎨ ⎪0 ⎩

| d i , j |< t | d i , j |> t

(12)

where: sgn is signum function and t is the threshold given as:

t =α

4 × ln N

(13)

where: α is a constant and NxN is the size of image. Owing to the similarity in the statistics of horizontal and vertical sub-bands (at a particular scale), they are being smoothened via soft-thresholding (whereas the diagonal sub-bands are processed using the methodology discussed in Sec. 3.3 above.). 3.5

Wavelet Reconstruction and Post-processing

Finally, the sub-bands are reconstructed by taking the inverse 2D-DWT using the same wavelet family as used in decomposition. This result in a despeckled image (I1 (x, y)) which is then compared with the noisy image I0 (x, y) and the resultant error is denoted as E1: E1 =| I1 (x, y) - I0 (x, y) |

(14)

Then, the image I0 (x, y) at the input is replaced by the resulting image, I1 (x, y) and the entire filtering process is repeated again. The reconstructed image generated at the second iteration is now referred to as I2 (x, y) such that the error computed is denoted as E2. E2 =| I2 (x, y) - I1 (x, y) |

(15)

The difference between E2 and E1 is therefore computed: ∆E = E2- E1

(16)

where: ∆E serves to define the stopping criterion. If ∆E ≥ 0, then the process is terminated else I2 (x, y) is again replaced by the filtered image I3(x, y) obtained at the next filtering iteration; E2 is stored in E1 and new error E2 is generated using I3(x, y) and I2 (x, y) in Eq. (15). This procedure is repeated iteratively until the error minimizes closely to zero. The overall filtering process attains convergence in not more than 3-4 iterations at high noise variance levels. These iterations are helpful in minimization of residual speckle content (left unprocessed) in case of higher degree of noise contaminations. This is followed by post-processing using exponential transform to cancel out the effect of log transform taken initially (during pre-processing) and it also serves to enhance the bright pixels over the dark pixels.

4.

RESULTS AND DISCUSSIONS

4.1 Experimental Procedure The results in this work are presented and discussed with the help of two test images: ‘Moon Crater’ (denoted and referred as Image-1) and ‘East Coast of India’ (denoted and referred as Image-2), which were taken from the SAR systems of NASA. The initiation of the experimental procedure on these test images (of size 256x256) firstly involves normalization to scale down the pixel intensity values between the ranges 0 to 1. Speckle noise of different variance levels ranging from, σ = 0.01 to 0.1 is simulated on the normalized SAR images to generate the noisy (speckled) SAR image. The above mentioned range of variance levels of speckle noise (for simulations) has been selected with the perspective to demonstrate the utility of the proposed work on medium to high levels of contamination by multiplicative noise. Secondly, these noisy SAR images are pre-processed using the log transformation discussed in section 3.1. Thirdly, two-level wavelet decomposition of these pre-processed (noisy) SAR images is performed yielding an approximation band of 64x64, three detail sub-bands of 64x64 at second level. During this process, the proposed diffusion coefficient of Eq. (9) is then applied over the diagonal sub-bands where as the horizontal and vertical sub-band coefficients are processed via soft thresholding using Eq. (14) and (15) respectively. The values of parameter λ is experimentally selected between 0 and 0.25 while α is adaptively selected depending upon the variance of the added speckle noise. The processed sub-band coefficients are then subjected to wavelet reconstruction process (as discussed in section 3.5) for post-processing. The reconstruction process is followed by exponential transform alongside in order to suppress the impact of logarithmic transformation. The process of wavelet decomposition and reconstruction is employed using 5-3 wavelet given in Contourlet Toolbox [38]. The said wavelet family works well in order to retain the features of the SAR image. Lastly, the obtained reconstructed images are evaluated in terms of various objective quality evaluation parameters namely SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio in dB) and SSI (Speckle Suppression Index) [17], [37], [39]. SSIM is computed to assess the degree of preservation of structural content in the filtering SAR images. Higher values of SSIM close to 1 are indicative of the properly retained structural details in the filtered image. Similarly, higher the values of PSNR (in dB), better is the quality of filtered images in terms of noise suppression. Further, to estimate average measure of the residual speckle content present in the filtered image; SSI is computed for both the noisy as well as filtered SAR images. Thus, lower SSI values for filtered images are the indicative of better degree of speckle filtering [45]. 4.2 Simulation Results The obtained filtered results for medium (0.04) and high (0.1) variance levels of speckle on test images-1 and 2 respectively are shown in Figure 2. Additionally, for the purpose of comparisons, the results with other speckle suppression techniques such as those of W. Wang et al. [41], L. Torres et al. [40], and A. Gupta et al. [37] are also included and shown in Figure 3 for both medium as well as high speckled images. The results of Wang et al. [41] are satisfactory in terms of speckle suppression for medium levels of speckle content but considerate amount of residual noise is still available at high levels of speckle. Also, the presence of blurred and over despeckled edges in the image is a major limitation of this approach. In work of Toress et al. [40], the obtained results are promising in terms of speckle suppression but the darker region in the image has transformed into a brighter one (i.e. the luster of darker region is degraded upon restoration). The edges are also not very well retained as some amount of edge region contains speckle, even after filtering. Next, the noisy images are processed using the AD approach of Gupta et al. [37]. The visual appearance of the image in this case is appreciable but the processing of homogeneous regions of the image is still not very significant. However, as the proposed approach embeds the AD filtering in wavelet domain, the resultants are effective in terms of speckle noise suppression at both medium and higher levels of speckle content. The results shown in Figure 2 reveals that the filtered images are visually better along with the retention of edges and improvising upon the quality of homogeneous region. Even the suppression of speckle content at higher level of noise is remarkable along with filtering in both homogeneous and edge regions. The sharp edges and darker regions are not over filtered; thereby ensuring the preservation of image details. The corresponding values of image quality assessment parameters (PSNR, SSIM & SSI) at various speckle noise variances levels (ranging from 0.01 to 0.1) determined for Image-1 and 2 both are enlsited under Tables 1 to 3. From Table 1, it is evident that the proposed method yields higher values of PSNR at both medium and high levels of speckle noise. This signifies the retention of maximum amount of signal content and higher the value suggests higher despeckling.

The computed values for SSIM in Table 2 are also remarkable and demonstrstive of better reconstruction of structural content of the image. Values of SSIM are good enough not just for medium but also for high levels of speckle contaminatoins; unlike the case of other speckle filtering techniques discussed previously. Similarily, SSI values tabulated in Table 3 are indicative of better speckle suppression in terms of lower values of residual noise content in filtered images. ***** FIGURE 2****

***** FIGURE 3****

***** TABLE 1****

***** TABLE 2****

***** TABLE 3**** 5.

CONCLUSIONS

SAR images consist of sharp edges due to the non-uniform landscape which poses difficulties during filtering in terms of estimation of details and other information content. The wavelet domain filtering has positive effect over the edges while AD filtering uniformly removes noise from the entire area of the image. Somehow, AD filter leads to over-filtering of the edges and this effect becomes more pronounced at higher speckle variances. The present work therefore formulates to embed the proposed version of AD filter in wavelet domain to overcome the discussed constriants and achieve a robust speckle suppression at varying degree of speckle variances. In addition, the modified diffusion coefficent in proposed AD filter uses Laplacian operator to facilitate detection of weak edges. Evaluated image quality parameters for the proposed method have shown remarkably distinguished responses in terms of edge and structural preservation while suppressing the speckle content. Simulation results shown for both the test images are visually better in comparison to previous techniques. Further, the reduction in speckle at higher noise variances is achieved without posing any computational loads; as the filtering process converges in not more than three iterations of the proposed approach. Hence, the proposed methodology is capable of producing brighter and detailed images with highly suppressed amount of speckle noise. The methodology owing to its versatility could be effectively applied for environment monitoring. REFERENCES [1] Y. K. Chan and V. C. Koo, An Introduction to Synthetic Aperture Radar(SAR), Journal of Progress In Electromagnetics Research B, (2008) Vol. 2, 27-60. [2] A. Lay-Ekuakille, V. Pelillo, C. Dellisanti and F. Tralli, (2002), SAR Aided Method for Rural Soil Evaluation, SPIE2002 Remote Sensing, Crete (Greece). [3] G. Griffo, L. Piper, A. Lay-Ekuakille, D. Pellicanò and E. De Franchis, “Modelling A Buoy For Sea Pollution Monitoring Using Fiber Optics Sensors,” 4th Imeko TC19 Symposium, Lecce, Italy, June 2013. [4] Lay Ekuakille A. and A. V. Scarano, (2004), Progressive Deconvolution of Laser Radar Signals, SPIE Remote Sensing, Honolulu, November 2004 (USA). [5] Vergallo P. and Lay-Ekuakille A., (2012), Spectral Analysis of Wind Profiler Signal for Environment Monitoring, IEEE I2MTC, May 2012, Graz, Austria. [6] C. J. Oliver, Information from SAR Images, Journal of Applied Physics (1991), Vol. 24, No. 5, 1493-1514. [7] A. Lay-Ekuakille and A. Trotta, (2002) On The Missing Data Problem in Rass Wind Profiler Measurements: An Algorithm Based on Functional Differential Equations, ERAD02, Delft, Holland, december 2002.

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Table 1. PSNR (in dB) values of various speckle suppression techniques. Image-1 Noise Level

W. Wang et al. [41]

L. Torres et al. [40]

A. Gupta et al. [37]

Proposed Methodology

0.01 0.04

27.2338 26.1644

27.1958 26.1510

28.5058 27.5885

31.6992 30.6746

0.08 0.1

25.0433 24.8601

24.7001 23.0879

0.01 0.04 0.08 0.1

27.6927 25.9520 24.4120 24.0562

27.6610 26.3194 25.3698 24.9843

26.8546 25.8324

28.5642 27.3759

28.0832 27.9988 26.3162 25.5015

32.2035 30.3181 28.1935 27.8389

Image-2

Table 2. SSIM values of various speckle suppression techniques. Image-1 Noise Level

W. Wang et al. [41]

L. Torres et al. [40]

A. Gupta et al. [37]

Proposed Methodology

0.01 0.04 0.08 0.1

0.8725 0.8563 0.7943 0.7228

0.8708 0.8652 0.8037 0.7551

0.8754 0.8610 0.8194 0.8006

0.9161 0.8977 0.8654 0.8500

0.01 0.04 0.08 0.1

0.8572 0.8369 0.8161 0.7997

0.8645 0.8341 0.8021 0.7968

0.8792 0.8518 0.8409 0.8019

0.9116 0.8848 0.8472 0.8393

Image-2

Table 3. SSI values of various speckle suppression techniques. Image-1 Noise Level

W. Wang et al. [41]

L. Torres et al. [40]

0.01 0.04 0.08 0.1

Noisy Image 0.1963 0.2624 0.3574 0.4753

Denoised Image 0.1319 0.1928 0.2412 0.2472

Noisy Image 0.1963 0.2624 0.3574 0.4753

Denoised Image 0.1309 0.1921 0.2597 0.2441

0.01 0.04 0.08 0.1

0.2016 0.2677 0.3604 0.4780

0.1646 0.2188 0.2358 0.2457

0.2016 0.2677 0.3604 0.4780

0.1626 0.2076 0.2265 0.2329

A. Gupta et al. [37] Noisy Image 0.1963 0.2624 0.3574 0.4753

Proposed Methodology

Denoised Image 0.1337 0.1807 0.2358 0.2158

Noisy Image 0.1963 0.2624 0.3574 0.4753

Denoised Image 0.1300 0.1719 0.1905 0.2034

0.1563 0.1997 0.2992 0.3012

0.2016 0.2677 0.3604 0.4780

0.1492 0.1845 0.2210 0.2101

Image-2 0.2016 0.2677 0.3604 0.4780

HIGHLIGHTS

Figure(s)

Start

Speckled SAR Image Iteration 1 Pre-Processing

Decomposition using 2D-DWT

Horizontal Sub-band

Diagonal Sub-band

Vertical Sub-band

Proposed AD Filtering

 1 || f ||  c   1 || f || 

Soft Thresholding

Replace I0(x,y) = I1(x,y)

Wavelet Reconstruction (I1(x,y))

Calculate E2 = I2(x,y) – I1(x,y) Calculate E1 = I1(x,y) – I0(x,y)

No Is ΔE = E2-E1 > 0? Yes Post Processing

De-Speckled SAR Image

Image Quality Assessment

Stop

2

Figure 1. Flow Diagram for Proposed Speckle Suppression Methodology (For Iteration-1).

(a)

(b)

(c)

(d)

(e)

(f)

(g) (h) Figure 2. Simulation Results for Proposed Speckle Suppression Methodology. (a) & (e) Test Image-1 and 2 with speckle noise of medium noise variance (0.04). (c) & (g) Test Image-1 and 2 with speckle noise of high noise variance (0.1). (b) & (f) Filtered SAR Image-1 and 2 for medium noise variance. (d) & (h) Filtered SAR Image-1 and 2 for high noise variance.

Medium Speckle Variance (0.04) High Speckle Variance (0.1)

(a) (b) (c) (d) (e) Figure 3. Comparison of results of speckle suppression from various filtering techniques demonstrated on Image-1. (a) Noisy SAR Image. Results using (b) Proposed Methodology, (c) A. Gupta et al., [37], (d) L. Torres et al. [40], (e) W. Wang et al. [41].

• A combo of Wavelets and modified Anisotropic Diffusion (AD) filtering is presented. • The diagonal sub-bands are processed for speckle using the modified AD filter. • The new diffusion coefficient consists of gradient & Laplacian operators. • The horizontal and vertical sub-bands are processed using soft thresholding. • SAR images with high speckle content are processed within three iterations.