Synthetic Aperture Radar Image Despeckling Research Articles

A new SAR image despeckling using correlation based fusion and method noise thresholding

This paper presents a new technique for despeckling of Synthetic Aperture Radar (SAR) images using a local correlation based fusion of high-frequency coefficients in Discrete Wavelet Transform (DWT) with method noise thresholding. The decomposition level is decided by analyzing the texture of the input image at each level by calculating entropy. The core idea of the proposed technique lies in the selection of decomposition level in 2D-DWT based on entropy parameter and on the fusion of high-frequency coefficients. On decomposition, the low-frequency coefficients remain untouched and the high-frequency coefficients are thresholded using two different shrinkage rules. Therefore the Bayesian and Bivariate shrinkage methods are applied to the high-frequency coefficients. After performing two different thresholding methods, the improved high-frequency coefficients are fused using local correlation based strategy. The threshold value is calculated by correlation strategy. Later the correlation coefficient (CC) is evaluated between the two improved high-frequency coefficients. The CC is now compared with the threshold value for the fusion purpose. On the basis of defined fusion strategy, the average and maximum operation are applied to perform the fusion of high-frequency coefficients. The despeckling scheme is followed by method noise thresholding in order to preserve the fine details of the image. The performance of the proposed method is assessed using metrics such as Signal-to-Noise Ratio (SNR), Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM) and visual appearance of the despeckled image. The experimental results demonstrate the effectiveness of proposed work over prior works on SAR image despeckling.

A Review on Recent Developments in Fully Polarimetric SAR Image Despeckling

The use of synthetic aperture radar (SAR) technology with quad-polarization data requires efficient polarimetric SAR (PolSAR) speckle filtering algorithms. During the last three decades, many effective methods have been developed to reduce the speckle in PolSAR images, and recent studies have generally shown a trend developing from local single-point filtering to nonlocal patch-based or globally collaborative filtering. The main goals of this paper are to make a comprehensive review of the existing PolSAR despeckling algorithms and highlight the recent development trends. In the experimental part, the filtering results obtained with both simulated and real PolSAR images are deployed to compare the performance of some of the state-of-the-art despeckling algorithms, which shows that all of the selected filters have their individual strengths and weaknesses.

Learning a Dilated Residual Network for SAR Image Despeckling

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN). SAR-DRN is based on dilated convolutions, which can both enlarge the receptive field and maintain the filter size and layer depth with a lightweight structure. In addition, skip connections and a residual learning strategy are added to the despeckling model to maintain the image details and reduce the vanishing gradient problem. Compared with the traditional despeckling methods, the proposed method shows a superior performance over the state-of-the-art methods in both quantitative and visual assessments, especially for strong speckle noise.

Synthetic aperture radar and color visible images fusion algorithm based on region division strategy

Multisensor image fusion can be used as an advanced technique for image enhancement. A fusion method based on region division strategy is proposed for synthetic aperture radar (SAR) and color visible images. The SAR target feature detection function is first constructed to divide the images into the SAR target region and non-SAR target region. Meanwhile, SAR image despeckling is considered during the generation of this detection function. Then, two different fusion rules are designed for these two regions. More specifically, the fusion rule adopted in the SAR target region is fusing the high-frequency feature within the SAR image, and the fusion rule employed in the non-SAR target region is maintaining the spectral and detail information of the color visible image. Experimental results on several pairs of SAR and color visible images demonstrate the effectiveness of the proposed method. Compared with the conventional image fusion methods, the proposed method provides better results for all evaluation criteria, including spectral distortions, feature similarity, mutual information, and peak signal-to-noise ratio.

Statistical Quality Analysis of Wavelet Based SAR Images in Despeckling Process

Synthetic aperture radar (SAR) images are mainly denoised by multiplicative speckle noise, which is due to the consistent behavior of scattering phenomenon known as speckle noise. This paper presents the basic concept, role and importance of Discrete Wavelet Transform (DWT) in the field of despeckling SAR images and also offers a study of SAR image quality on applying DWT on the speckled image and log transformed speckled image. Log transform operation plays a decisive and comfortable role in despeckling SAR images as this operation changes the multiplicative behavior of the speckle noise to an additive which enables to use the additive noise restoration model efficiently. Wavelet transform has now become important in the field of image restoration although being in practice for a decade. Wavelet transform allows both time and frequency analysis simultaneously around a particular time. This transform is most appropriate for the non-stationary signals, so it deals with satellite imagery in a more efficient manner. The major part of this paper is revolving around DWT image decomposition with its role and practical implementation on the speckled image and log transformed speckled image. All the experimental results are performed on the SAR images.

NRDSP: A novel assessment of SAR image despeckling

In order to properly characterize the preservation of structural features for synthetic aperture radar (SAR) image despeckling, a novel metric called the NRDSP (no-reference despeckling structure-preserving) is put forward and investigated. To begin with, the DSSIM (distance for structural similarity) metric is presented to characterize the distance between the ratio image and the despeckled result. As an improvement of the ENLR (equivalent number of looks of ratio images), the NENLR (nominal number of looks-oriented ENLR) metric is proposed with the advantage of rationally bounded function. The DSSIM and NENLR are indispensable and complementary for characterizing the degree of structure-preserving. Furthermore, based on the new factors of DSSIM and NENLR, the novel NRDSP metric is proposed to appropriately measure the structure-preserving performance. Lastly, we carry through some testing for simulated and real SAR images, and it is verified by the observers’ evaluation as well as scientific data that the proposed NRDSP metric is more consistent with the perceptual perceptions than other metrics, and possesses the better effect for characterizing structure-preserving performance.

Insights into prior learning for despeckling SAR images

This study presents an exploratory comparative study for learning the priors of synthetic aperture radar (SAR) images, using the Fields of Experts approach, a filter-based higher-order Markov random fields model, which has substantially improved the learning capability of the entire image priors. First, the authors provide new insights into prior learning for despeckling SAR images, which commonly exhibits a variety of the likelihood functions due to the complex physical process of the scattering. Second, the authors introduce a prior learning framework using a bi-level optimisation algorithm. Third, some interesting experiments are conducted to learn the priors of SAR images. Finally, the authors validate the learned priors on despeckling SAR images. It suggests that the representation of the prior is more accurate by using the training samples from the filtered real SAR images themselves than from both the optical images of the Earth's surface and the natural images.

SAR Image Despeckling by Soft Classification

We propose a new approach to synthetic aperture radar (SAR) despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixelwise characterization of the image, one can take advantage of this diversity by selecting the most appropriate combination of estimators for each image region. Following this paradigm, we develop a simple algorithm where only two state-of-the-art despeckling tools, characterized by complementary properties, are linearly combined. To ensure the smooth combination of contributes, thus avoiding new artifacts, we propose a novel soft classification method, where a basic estimate of homogeneity is improved through nonlocal and multiresolution processing steps. The results of a number of experiments conducted on both synthetic and real-world SAR images are very promising, thus confirming the potential of the proposed approach.

Scattering-Based Nonlocal Means SAR Despeckling

Speckle noise greatly limits both synthetic aperture radar (SAR) data human readability, especially for non-SAR-expert users, and performance of automatic processing and information retrieval procedures by computer programs. Therefore, despeckling of SAR images is an essential preprocessing step in SAR data analysis, processing, and modeling, as well as in information retrieval and inversion procedures. Up to now, one of the most accurate and promising despeckling approaches—among those based on a single SAR image—is the one relying on the nonlocal means concepts. However, at the best of our knowledge, most of the state of the art considers the despeckling problem only within a statistical framework, completely discarding the electromagnetic phenomena behind SAR imagery formation. In this paper, we introduce the novel idea of a physical-based despeckling, taking into account meaningful physical characteristics of the imaged scenes. This idea is realized via the implementation of a physical-oriented probabilistic patch-based (PPB) filter based on a priori knowledge of the underlying topography and analytical scattering models. This filter is suitable for SAR images of natural scenes presenting a significant topography. An adaptive version of the proposed scattering-based PPB filter for denoising of SAR images including both mountainous and flat areas is also developed. The performances of the proposed filter and its adaptive version are evaluated both qualitatively and quantitatively in numerical experiments using both simulated and actual SAR images. The proposed technique exhibits performance superior w.r.t. the standard PPB filter and comparable or, in some cases, superior to the state of the art, both in terms of speckle reduction and texture and detail preservation.

SAR Image Despeckling by the Use of Variational Methods With Adaptive Nonlocal Functionals

In this paper, we focus on the despeckling of synthetic aperture radar (SAR) images by variational methods which introduce nonlocal regularization functionals. To achieve this goal, two models are investigated from different aspects. The first model is derived for the logarithmically transformed (homomorphic) domain of the SAR data, and the other is derived for the original (nonhomomorphic) domain. The statistical properties of the speckle and the log-transformed speckle are analyzed, and the similarity measurements between pixels in the homomorphic domain and nonhomomorphic domain are then derived for constructing the corresponding nonlocal regularization functionals. Meanwhile, in the proposed models, we develop a strategy to adaptively choose the regularization parameters based on both the local heterogeneity information and the noise level of the images, aiming at getting a better balance between the goodness of fit of the original data and the amount of smoothing. A quasi-Newton iteration method is employed to quickly minimize the proposed adaptive nonlocal functionals. Experiments conducted on both simulated images and real SAR images confirm the good performances of the proposed methods, both in reducing speckle and preserving image quality.

A SAR Image Despeckling Method Based on Two-Dimensional S Transform Shrinkage

Speckle is a granular disturbance that affects synthetic aperture radar (SAR) images. Over the last three decades, many methods have been proposed for speckle reduction, where a tradeoff between despeckling and detail preservation is required. As an attempt to balance the performance on both sides, in this paper, we propose a 2-D S transform shrinkage algorithm using adaptive soft threshold for SAR image despeckling. It follows the idea of the wavelet shrinkage algorithm, but extends its major steps to take into account the peculiarities of S transform, i.e., adding adaptivity in the estimation of speckle standard deviation and threshold function, in an optimized computation procedure. Homogeneous and heterogeneous SAR images are used for quantitative evaluations, and both vintage and prevailing algorithms are used for comparison, which demonstrates the validity of the proposed method. Additionally, some instructive pieces of advice are given on the selection of suitable parameters of the proposed method under different circumstances.

SAR Image Despeckling Based on Combination of Laplace Mixture Distribution with Local Parameters and Multiscale Edge Detection in Lapped Transform Domain

The speckle noise badly affects the tasks of automatic information extraction and scene analysis in Synthetic Aperture Radar (SAR) images. Therefore, the despeckling of SAR images while preserving edge and textures is highly important. In this paper, a lapped transform (LT) based SAR image despeckling algorithm is proposed. Since lapped orthogonal transform (LOT) is orthogonal and has good energy compaction, the statistical modeling of noise and signal can be done precisely in the same domain. The use of LOT in denoising applications is motivated by its low computational complexity and its feature of robustness to over-smoothing. Since the LOT is block transform, the dyadic remapping is carried out first and then the subband LOT coefficients are modeled similar to wavelet coefficients. The LOT coefficients of the logarithmically transformed reflectance and the speckle noise are modeled using 2-state laplace mixture pdf that uses local parameters and zero mean Gaussian pdf respectively. A MAP estimator within Bayesian framework based on proposed prior is developed. The mixture distribution parameters are estimated using Expectation-Maximization (EM) algorithm. Subband LOT coefficients at each scale are classified into edge and non-edge coefficients using LOT modulus maxima computation. The non-edge coefficients are filtered using the proposed Bayesian MAP estimator and edge coefficients are kept unmodified. The method is implemented in ‘cycle spinning’ mode to solve the problem of lack of shift-invariance property of the LOT. Experimental results carried out on real SAR images show that the proposed scheme very effectively preserve the edges of a SAR image with notable speckle suppression and outperform two undecimated wavelet transform based methods.

Fast Algorithm for Inverse Two‐Dimensional S Transform and Its Application in Time‐Frequency Filtering for SAR Image Despeckling

S transform is a time-frequency representation which has been applied in various fields, yet suffers the problem of time and resource consumption. In order to overcome this problem and facilitate its application in image analysis, we introduce a fast algorithm for inverse twodimensional S transform. A two-dimensional S transform time-frequency filter for Synthetic aperture radar (SAR) image despeckling is proposed on the basis of this fast algorithm. Synthetic and actual SAR images are both used to quantitatively evaluate its performance. The proposed algorithm is compared with several classical algorithms with better results both in speckle noise reduction and detail preservation.

Despeckling of SAR images by directional representation and directional restoration

Despeckling algorithm for synthetic aperture radar (SAR) imagery, which uses directional representation and directional bases effectively to achieve comprehensive results. The directional representation directly relies on the multiplicative noise model and directional bases are used in coherent system model for restoration purpose. The proposed method is evaluated using qualitative analysis on real SAR data and quantitative analysis on simulated data. The results of the evaluation show that the proposed despeckling method outperforms the other despeckling algorithms.

SAR Image Despeckling by Sparse Reconstruction Based on Shearlets

Synthetic aperture radar (SAR) image is usually polluted by multiplicative speckle noise, which can affect further processing of SAR image. This paper presents a new approach for multiplicative noise removal in SAR images based on sparse coding by shearlets filtering. First, a SAR despeckling model is built by the theory of compressed sensing (CS). Secondly, obtain shearlets coefficient through shearlet transform, each scale coefficient is represented as a unit. For each unit, sparse coefficient is iteratively estimated by using Bayesian estimation based on shearlets domain. The represented units are finally collaboratively aggregated to construct the despeckling image. Our results in SAR image despeckling show the good performance of this algorithm, and prove that the algorithm proposed is robustness to noise, which is not only good for reducing speckle, but also has an advantage in holding information of the edge.

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

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.

QMCTLS: Quasi Monte Carlo Texture Likelihood Sampling for Despeckling of Complex Polarimetric SAR Images

Despeckling of complex polarimetric synthetic aperture radar (SAR) images is more difficult than denoising of general images due to the low signal-to-noise ratio and the complex signals. A novel stochastic polarimetric SAR despeckling technique based on quasi Monte Carlo sampling (QMCS) and region-based probabilistic similarity likelihood has been developed. The despeckling of complex polarimetric SAR images is formulated as a Bayesian least squares optimization problem, where the posterior distribution is estimated by QMCS in a nonparametric manner. The QMCS approach allows the incorporation of the statistical description of local texture pattern similarity. Experiments on two benchmark quad-pol SAR images demonstrate that the proposed QMC texture likelihood sampling (QMCTLS) filter outperforms referenced methods in terms of both noise removal and detail preservation.

Adaptive Total Variation Regularization Based SAR Image Despeckling and Despeckling Evaluation Index

We introduce a total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A dual-formulation-based adaptive TV (ATV) regularization method is applied to solve the TV regularization. The parameter adaptation of the TV regularization is performed based on the noise level estimated via wavelets. The TV-regularization-based image restoration model has a good performance in preserving image sharpness and edges while removing noises, and it is therefore effective for edge preserve SAR image despeckling. Experiments have been carried out using optical images contaminated with artificial speckles first and then SAR images. A despeckling evaluation index (DEI) is designed to assess the effectiveness of edge preserve despeckling on SAR images, which is based on the ratio of the standard deviations of two neighborhood areas of different sizes of a pixel. Experimental results show that the proposed ATV method can effectively suppress SAR image speckles without compromising the edge sharpness of image features according to both subjective visual assessment of image quality and objective evaluation using DEI.

A Modified BM3D Algorithm for SAR Image Despeckling

Abstract In synthetic aperture radar (SAR) imaging, pulses of microwave energy are transmitted towards the ground surface (target). The backscattered signal energy is measured at the receiving end. In this process, a speckle noise is added because of the coherent imaging system and makes the study of images very difficult. For better SAR image processing, the speckle has to be removed in the initial stages of processing and maintain all texture features efficiently. The (Block Matching 3D algorithm) BM3D method is generally considered as state of art method in denoising of SAR images. Still some amount of speckle left with images. This is due to sampling conversion at transmitter and receiver. In this paper, it is proposed a technique to despeckle the speckle noise to the maximum extent while maintaining the edge characteristics. This technique uses un-sampling at both the ends.

A new metric for measuring structure-preserving capability of despeckling of SAR images

In this paper, a new metric called despeckling structure loss (DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural features such as edges and textures. The ratio image of a synthetic aperture radar (SAR) image to its despeckled result can clearly indicate the loss of structural features caused by the despeckling process. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of structural features in ratio images by using local correlations between the ratio image and the noise-free reference image at edge points. The DSL is shown to be a monotonic function of structure loss bounded between the best and worst cases, leading an objective and quantitative measure of the structure-preserving capability of despeckling algorithms. Experimental evaluations of the proposed metric have been carried out on simulated SAR images including one generated by SAR raw signal simulator. Despeckled results using five typical algorithms clearly demonstrate the efficiency of the DSL metric. In comparison, the commonly used despeckling metrics including the signal-to-mean-square error ratio, the edge correlation index, the Pratt’s figure of merit, the structural similarity and the equivalent number of looks of ratio images, fail to keep a consistency with the structure loss shown in despeckled results as well as ratio images.