Synthetic Aperture Radar Image Despeckling Research Articles

Blind Speckle Decorrelation for SAR Image Despeckling

In the past few decades, several methods have been developed for despeckling synthetic aperture radar (SAR) images. A considerable number of them have been derived under the assumption of a fully-developed speckle model in which the multiplicative speckle noise is supposed to be a white process. Unfortunately, the transfer function of SAR acquisition systems can introduce a statistical correlation, which decreases the despeckling efficiency of such filters. In this paper, a whitening method is proposed for processing a complex image acquired by a SAR system. We demonstrate that the proposed approach allows the successful application of classical despeckling algorithms. First, we perform an estimation of the SAR system frequency response based on some statistical properties of the acquired image and by using realistic assumptions. Then, a decorrelation process is applied on the acquired image, taking into account the presence of point targets. Finally, the image is despeckled. The experimental results show that the despeckling filters achieve better performance when they are preceded by the proposed whitening method; furthermore, the radiometric characteristics of the image are preserved.

SAR Image Despeckling via Bivariate Shrinkage Based on Directionlet Transform

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of the scattering phenomenon. A novel and efficient SAR image despeckling algorithm based on Directionlet transform using bivariate shrinkage is proposed to remove speckle noise while preserving the structural features and textural information of the scene. First, an anisotropic directionlet transform is taken on the logarithmically transformed SAR images. The distribution of speckle noise is modeled as an additive Gaussian distribution with zero-mean. Then, a bivariate shrinkage with local variance estimation is applied to the decomposed directionlet coefficients of the logarithmically transformed image to estimate the best value for the noise-free signal. Finally, the performance of the proposed algorithm is compared with those of existing despeckling methods applied on both synthetic speckled images and actual SAR images. Experimental results show that compared with conventional wavelet and contourlet despeckling algorithm, the proposed algorithm can keep the better balance between suppresses speckle effectively and preserves image details, and the important feature of original image like textures and contour details is well maintained.

SAR image despeckling by combining saliency map and threshold selection

Speckle suppression and detail preservation are a unity of contradiction during synthetic aperture radar (SAR) despeckling. In this article, an effective despeckling method is proposed based on a self-adaptive neighbourhood, which takes into account the trade-off between homogeneous region suppression and point target preservation. The size and shape of the neighbourhood depend on an adaptive threshold that is treated as a linear function of a saliency map and a threshold range estimated by the Monte Carlo algorithm based on a given significance level and the number of looks in SAR. If the saliency of the current pixel is large, which always means that the current pixels should belong to a point target, a linear object, or an edge, a relatively large threshold will be assigned to search a neighbourhood with a small size. Conversely, pixels with a small saliency value will obtain a non-local neighbourhood with a large size. A maximum-likelihood estimation is employed to estimate the real radar reflectivity based on the pixels in the adaptive neighbourhood. The comparison experiments on simulated and actual SAR data validate the effectiveness of the proposed algorithm and demonstrate its overall speckle filtering characteristics compared with other algorithms. The visual and numerical experimental results show that the proposed despeckling method provides superior performance compared with several state-of-the-art despeckling methods.

SAR image despeckling using adaptive multiscale products thresholding in directionlet domain

A directionlet transform (DT)-based multiscale products thresholding is presented which uses a generalised cross-validation (GCV) technique for synthetic aperture radar image despeckling. DT has gained popularity over the past few years as an anisotropic, critically sampled and perfect reconstruction transform with directional vanishing moments along any two directions. In this reported work, the multiscale correlation of DT is exploited by multiplying the adjacent scale coefficients to enhance edge structures while weakening noise. An optimal subband adaptive threshold based on GCV is then computed using this multiscale product. The despeckled image is finally synthesised by using the threshold applied DT coefficients. The employment of DT results in the significant features in images evolving with high magnitudes across scales, while the noise decays rapidly. The proposed scheme outperforms many of the traditional despeckling schemes in terms of speckle reduction and edge preservation.

Supervised Constrained Optimization of Bayesian Nonlocal Means Filter With Sigma Preselection for Despeckling SAR Images

Speckle reduction is an important problem in synthetic aperture radar (SAR) image analysis. Recent years have seen how Bayesian filters emerge as the natural extension of the nonlocal means filters, providing a general framework to deal with multiplicative (speckle) noise. In this paper, we present an easy-to-use software tool applying an evolutionary algorithm to optimize a Bayesian nonlocal means filter with sigma preselection for denoising SAR images. The desired result is a filtered image having a significative reduction in its variance but preserving the original mean value of the noisy image. A mixed-integer constrained optimization problem is stated and solved with the human intervention, where the user assists the evolutionary algorithm to reduce the noisy image variance under the restriction of keeping the mean value of the noisy SAR image within a predetermined interval of acceptance. We apply the methodology to a set of synthetic and real SAR speckle corrupted images. The results through the evaluation of objective global and local quality criteria show the excellent potential of the proposal.

Bayesian Wavelet Shrinkage With Heterogeneity-Adaptive Threshold for SAR Image Despeckling Based on Generalized Gamma Distribution

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analysis. In this paper, we propose a novel Bayesian multiscale method for SAR image despeckling in the non-homomorphic framework. To address the multiplicative nature, we first make the speckle contribution additive by a linear decomposition. Then, in the stationary wavelet transform domain, a two-sided generalized Gamma distribution (GTD) is introduced as a prior to capture the heavy-tailed nature of wavelet coefficients of the noise-free reflectivity. By exploiting this prior together with a Gaussian likelihood, an analytical wavelet shrinkage function is derived based on maximum a posteriori criteria, which further adopts heterogeneity-adaptive thresholding technique to achieve better estimates of noise-free wavelet coefficients. Moreover, a pilot-signal-assisted strategy is proposed to estimate the parameters of two-sided GTD with the estimator based on second-kind cumulants. Finally, experimental results, carried out on the synthetic and actual SAR images, are given to demonstrate the validity of the proposed despeckling method.

Combination of Target Detection and Block‐matching 3D Filter for Despeckling SAR Images

This Letter proposes a combination of target detection and block-matching 3D filter for despeckling SAR images. The proposed method is able to effectively preserve targets, such as the edges and dots of synthetic aperture radar (SAR) images, whilst removing noises. In the first step of our proposed method, based on despekling results of bilateral filtering and edge detection of a canny operator, some targets are detected and removed from an SAR image. The second step uses BM3D for denoising the targets-removed image. Finally, the removed targets are added to the despeckled targets-removed image and desirable results can be achieved.

SAR Image Despeckling with Adaptive Multiscale Products Based on Directionlet Transform

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise generated by radar coherent wave. In this paper, a new despeckling algorithm based on directionlets using multiscale products is proposed. We first take an anisotropic directionlet transform on the logarithmically transformed SAR images and multiply the coefficients at adjacent scales to enhance the details of image under consideration. Then, different from traditional thresholding methods, a threshold is applied to the multiscale products of the directionlet coefficients to suppress noise. Since the multiplication amplifies the significant features of signal and dilute noise, the proposed method reduces noise effectively while preserving edge structures. Finally, we compare the performance of the proposed algorithm with other despeckling methods applied to synthetic image and real SAR images. Experimental results demonstrate the effectiveness of the proposed method in SAR images despeckling.

STRUCTURE PRESERVING SAR IMAGE DESPECKLING VIA L0-MINIMIZATION

In this paper, we propose a new method for synthetic aperture radar (SAR) image despeckling via L0-minimization strategy, which aims to smooth homogeneous areas while preserve signiflcant structures in SAR images. We argue that the gradients of the despeckled images are sparse and can be pursued by L0-norm minimization. We then formularize the despeckling of SAR images as a global L0 optimization problem with difierence of average operations. Namely, the number of pixels with difierence of average that are unequal to one is controlled to approximate prominent structures in a sparsity-control manner. Finally, a numerical algorithm is employed to solve the L0 minimization problem. In contrast with existing SAR image despeckling approaches, this strategy is applied without necessity to consider the local features or structures. The performance of our method is tested on high-resolution X-band SAR images. The experimental results show that the proposed method achieves state-of- the-art results in terms of the equivalent-number-of-looks measure and the edge-preserving index.

Refined sigma filter for SAR image despeckling using reference image

Presented is a refined sigma filter for synthetic aperture radar (SAR) image despeckling, where a novel reference image is constructed to replace the original SAR image for the selection of homogeneous pixels. The reference image holds the advantages of containing almost the same amount of details as the original SAR image but with less speckle noise, and thus enables more accurate sigma selection. Results on synthetic and real SAR images show the effectiveness of the method on both detail preservation and speckle removal.

Despeckling SAR images based on a new probabilistic model in nonsubsampled contourlet transform domain

A method for synthetic aperture radar (SAR) image despeckling based on a probabilistic generative model in nonsubsampled contourlet transform (NSCT) domain was proposed. The shrinkage estimator in NSCT domain consists of a new type of likelihood ratio and prior ratio, both of which are dependent on the estimated masks for the NSCT coefficients. While the previous probabilistic approaches are restricted to parametric models, the limitation is eliminated and the hybrid density model is applied in this paper. The suggested approach does not make heavy assumptions on the NSCT coefficient distribution, so that it can handle complex NSCT coefficient structures. The likelihood ratio is composed of the hybrid density, and the prior ratio is equipped with the selective neighborhood systems to enhance the detail information. The method can effectively adapt the shrinkage estimator to the redundancy property of the NSCT. The proposed approach was applied to real SAR images despeckling and compared through the SAR image vision effect, the equivalent number of looks, and the edge sustain index. Experimental results show that the proposed approach outperforms previous works involved in the paper with the better despeckling result and edge preservation.

SAR Image Despeckling Based on Nonsubsampled Shearlet Transform

Synthetic Aperture Radar (SAR) Image despeckling is an important problem in SAR image processing since speckle may interfere with automatic interpretation. This paper presents a new approach for despeckling based on nonsubsampled shearlet transform. The approach introduced here presents two major contributions: (a) Translation-invariant Nonsubsampled Shearlet Transform (NSST) is designed to get more directional subbands which help to capture the anisotropic information of SAR image, and an estimation of speckle variance based on NSST is modeled to shrink NSST coefficients; (b) NSST coefficients are divided into several classes based on NSST- Multiscale Local Coefficient Variation (NSST-MLCV), which is helpful to reduce the undesired over-shrinkage, and shrinkage factor is obtained by computing the prior ratio and the likelihood ratio through mask. This model allows us to classify the NSST coefficients into classes having different degrees of heterogeneity, which can reduce the shrinkage ratio for heterogeneity regions while suppresses speckle effectively to realize both despeckling and detail preservation. Experimental results, carried out on both artificially speckled images and true SAR images, demonstrate that the proposed filtering approach outperforms the previous filters, irrespective of the features of the underlying reflectivity.

An improved particle filter algorithm based on Markov Random Field modeling in stationary wavelet domain for SAR image despeckling

Particle filter (PF) is an effective approach to nonlinear and non-Gaussian Bayesian state estimation and has been successfully applied to wavelet-based synthetic aperture radar (SAR) image despeckling. In this paper, we propose an improved PF despeckling algorithm based on Markov Random Field (MRF) model that can preserve the edge, textural information and structural features of SAR images well. First, we show that the wavelet coefficients of SAR images which exhibit significantly non-Gaussian statistics can be described accurately by generalized Gaussian distribution (GGD) in stationary wavelet domain. Secondly, to amend the weight deviation, MRF model parameters are introduced to redefine the importance weight of the particles. At last, region-divided processing is implemented for the real time application of the proposed algorithm. The effectiveness of the proposed algorithm is demonstrated by application to simulated images and real SAR images.

SAR Image Despeckling Using Bayesian Nonlocal Means Filter With Sigma Preselection

Bayesian nonlocal (NL) (BNL) means filter, as an extension of the NL means algorithm, provides a general framework for image denoising when dealing with different noise. However, this approach makes a strong assumption that image patch itself provides a good approximation on the true parameter, which leads to the bias problem particularly under serious speckle noise. Another disadvantage of the BNL filter is that the commonly used patch preselection method cannot effectively exclude the outliers. In this letter, a new form of the BNL filter is presented for the purpose of synthetic aperture radar image despeckling, which incorporates the technique of sigma filter to cope with the bias problem. In addition, pixel preselection is adopted based on the refined sigma range, which greatly contributes to the preservation of the image details such as edges, texture, and the strong reflective scatters. Experimental results illustrate that the proposed BNL filter reaches the state-of-the-art performance on both the visual quality and evaluation indexes.

Directionlet-Based Bayesian Filter for SAR Image Despeckling

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the radar coherent wave. In this paper, we introduce a new SAR despeckling method based on directionlet transform using Bayesian MAP estimator. The directionlet coefficients of the logarithmically transformed reflectance image and the speckle image are modeled Cauchy PDF and additive Gaussian distribution, respectively. Then, we exploit this as a priori information to design a maximum a posterior (MAP) estimator. A new method for estimating the dispersion parameter of Cauchy distribution is also presented. Experiment results, carried out on both synthetic speckled image and real SAR image, show that the proposed scheme removes noise from SAR images more efficiently.

A two-stage method for oil slick segmentation

In this paper we propose a two-stage algorithm for oil slick segmentation in synthetic aperture radar (SAR) images. In the first stage, we propose a new variational model to reduce speckles in non-textured SAR images. Applications to simulated and real SAR images show that the method is well balanced in the quality of the conventional criteria. Then, in the second stage, we use the fast Chan–Vese (CV) model and the level set method to segment the oil slick in the de-speckled SAR image. The additive operator splitting (AOS) scheme is used in the numerical implementation to improve computational efficiency. Experimental results show that our two-stage algorithm is effective for oil slick segmentation in SAR images.

Dual-Tree Complex Wavelet Transform Based SAR Despeckling Using Interscale Dependence

In this paper, a dual-tree complex wavelet transform (DTCWT) based despeckling algorithm is proposed for synthetic aperture radar (SAR) images, considering the significant dependences of the wavelet coefficients across different scales. The DTCWT has the advantage of improved directional selectivity, approximate shift invariance, and perfect reconstruction over the discrete wavelet transform. The wavelet coefficients in each subband are modeled with a bivariate Cauchy probability density function (PDF) which takes into account the statistical dependence among the wavelet coefficients. Mellin transform of two dependent random variables is utilized to estimate the dispersion parameter of the bivariate Cauchy PDF from the noisy observations. This method is faster and effective when compared to that of the earlier techniques on numerical integration. Within this framework, we propose a new method for despeckling SAR images employing a maximum a posteriori estimator. Experimental results show that the proposed method based on bivariate Cauchy prior achieves better performance in terms of equivalent number of looks, peak signal-to-noise ratio, and Pratt's figure of merit.

Despeckling of TerraSAR-X Data Using Second-Generation Wavelets

This letter presents the despeckling of synthetic aperture radar (SAR) images within the bandelet and contourlet domains. A model-based approach is presented for the despeckling of SAR images. The speckle-reduced estimate is found using the first-order Bayesian inference, and the best model's parameters are estimated using the second-order Bayesian inference. Synthetic and real images are used for evaluating the qualities of the despeckling methods. The experimental results showed that the combination of Bayesian inference and bandelet transform outperforms the contourlet-based despeckling algorithm using synthetic data and objective measurements.

Wavelet-Based SAR Image Despeckling and Information Extraction, Using Particle Filter

This paper proposes a new-wavelet-based synthetic aperture radar (SAR) image despeckling algorithm using the sequential Monte Carlo method. A model-based Bayesian approach is proposed. This paper presents two methods for SAR image despeckling. The first method, called WGGPF, models a prior with Generalized Gaussian (GG) probability density function (pdf) and the second method, called WGMPF, models prior with a Generalized Gaussian Markov random field (GGMRF). The likelihood pdf is modeled using a Gaussian pdf. The GGMRF model is used because it enables texture parameter estimation. The prior is modeled using GG pdf, when texture parameters are not needed. A particle filter is used for drawing particles from the prior for different shape parameters of GG pdf. When the GGMRF prior is used, the particles are drawn from prior in order to estimate noise-free wavelet coefficients and for those coefficients the texture parameter is changed in order to obtain the best textural parameters. The texture parameters are changed for a predefined set of shape parameters of GGMRF. The particles with the highest weights represents the final noise-free estimate with corresponding textural parameters. The despeckling algorithms are compared with the state-of-the-art methods using synthetic and real SAR data. The experimental results show that the proposed despeckling algorithms efficiently remove noise and proposed methods are comparable with the state-of-the-art methods regarding objective measurements. The proposed WGMPF preserves textures of the real, high-resolution SAR images well.

Despeckling SAR images using stationary wavelet transform combining with directional filter banks

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which is due to the coherent nature of scattering phenomena. This paper presents a depeckling method for SAR images based on stationary wavelet transform combining with directional filter banks. In particular, the traditional thresholding method is substituted by a novel and efficient method, Bayesian maximum a posteriori (MAP) estimation, to achieve more satisfying results. The performances of stationary wavelet transform, contourlet transform and stationary wavelet combining with directional filter banks for despeckling SAR images are compared through an experiment. Experiment results show this method not only suppresses the speckle noise effectively, but also preserves as many target characteristics of original images as possible. Besides, the recovered SAR image’s visual effect is satisfying.