Synthetic Aperture Radar Image Despeckling Research Articles

A Review on SAR Image and its Despeckling

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades. The SAR images are inherently speckled in nature. Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images. Other SAR related problems are also discussed in this paper. Therefore, despeckling approaches are needed to improve the quality of SAR images. However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image. The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task. However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images. Each method has their own norms, advantages and disadvantages. This article contains a review of some major work in the field of SAR image despeckling. Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images. Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here. The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed.

Learning an SAR Image Despeckling Model Via Weighted Sparse Representation

Synthetic aperture radar (SAR) images are inherently degraded by the speckle noise due to the coherent imaging, which may affect the performance of subsequent image analysis task. To address this problem, a weighted sparse representation-based method is proposed in this article for SAR image despeckling. The homomorphic transformation is first adopted to convert multiplicative noise into additive one. Second, similar patches are grouped together to learn the adaptive dictionaries and sparse coefficients based on nonlocal self-similarity constraint. Moreover, weighted regularizations are adopted for coefficients to boost the performance. Finally, despeckling images are obtained via exponential transformation. Experimental results on synthetic and real-world SAR images demonstrate that our proposed method outperforms several state-of-the-art methods in terms of both quantitative measurements and visual quality.

Spatial and Transform Domain CNN for SAR Image Despeckling

The speckle interference seriously degrades the quality of synthetic aperture radar (SAR) image. The existing despeckling algorithms still struggle to remove noise and preserve details simultaneously. In order to enhance the noise suppression and detail restoration performance, this article specially presents a spatial and transform domain convolutional neural network (STD-CNN) model, which yields an integrated feature representation and learning framework for despeckling. In addition, an innovative feature refinement strategy is proposed to further reduce the detail loss by isolating detail features from noise features. Extensive experiments on synthetic and real SAR images demonstrate that the proposed method outperforms the existing SAR despeckling methods on both quantitative and qualitative assessments. With partial modification, the STD-CNN model can still be extended to other image restoration tasks.

SAR Image Despeckling by Deep Neural Networks: from a Pre-Trained Model to an End-to-End Training Strategy

Speckle reduction is a longstanding topic in synthetic aperture radar (SAR) images. Many different schemes have been proposed for the restoration of intensity SAR images. Among the different possible approaches, methods based on convolutional neural networks (CNNs) have recently shown to reach state-of-the-art performance for SAR image restoration. CNN training requires good training data: many pairs of speckle-free/speckle-corrupted images. This is an issue in SAR applications, given the inherent scarcity of speckle-free images. To handle this problem, this paper analyzes different strategies one can adopt, depending on the speckle removal task one wishes to perform and the availability of multitemporal stacks of SAR data. The first strategy applies a CNN model, trained to remove additive white Gaussian noise from natural images, to a recently proposed SAR speckle removal framework: MuLoG (MUlti-channel LOgarithm with Gaussian denoising). No training on SAR images is performed, the network is readily applied to speckle reduction tasks. The second strategy considers a novel approach to construct a reliable dataset of speckle-free SAR images necessary to train a CNN model. Finally, a hybrid approach is also analyzed: the CNN used to remove additive white Gaussian noise is trained on speckle-free SAR images. The proposed methods are compared to other state-of-the-art speckle removal filters, to evaluate the quality of denoising and to discuss the pros and cons of the different strategies. Along with the paper, we make available the weights of the trained network to allow its usage by other researchers.

SAR image despeckling using plug‐and‐play ADMM

The presence of speckle, a granular structure, seriously affects the visual interpretability of the synthetic aperture radar (SAR) images. Recently, model-based reconstruction techniques that use alternating direction method of multipliers (ADMM) have been widely utilised for denoising problems. Owing to the modular structure of ADMM, it is very simple to implement and also enables one to plug in any off-the-shelf denoising algorithm. However, the major limitations of these methods are mostly they have been established for Gaussian noise and the selection of a regulariser for a specific forward model is unknown. In this study, the despeckling of SAR images is addressed in the presence of multiplicative noise such as Rayleigh and Poisson. A mean filter is introduced for images corrupted by Rayleigh-based speckle, which transforms it to Nakagami distributed speckle, to improve the performance. Maximum a posteriori-based estimation involving Nakagami and Poisson distributions are applied to plug-and-play ADMM (PnP ADMM) framework, which provides enhanced despeckling ability along with the preservation of important details. The convergence analysis of the proposed PnP ADMM for multiplicative noise is also provided. The simulation studies are carried out taking several parameters for visual quality and statistical assessment to justify the effectiveness of the proposed method.

A SAR Image Despeckling Method Based on an Extended Adaptive Wiener Filter and Extended Guided Filter

The elimination of multiplicative speckle noise is the main issue in synthetic aperture radar (SAR) images. In this study, a SAR image despeckling filter based on a proposed extended adaptive Wiener filter (EAWF), extended guided filter (EGF), and weighted least squares (WLS) filter is proposed. The proposed EAWF and EGF have been developed from the adaptive Wiener filter (AWF) and guided Filter (GF), respectively. The proposed EAWF can be applied to the SAR image, without the need for logarithmic transformation, considering the fact that the denoising performance of EAWF is better than AWF. The proposed EGF can remove the additive noise and preserve the edges’ information more efficiently than GF. First, the EAWF is applied to the input image. Then, a logarithmic transformation is applied to the resulting EAWF image in order to convert multiplicative noise into additive noise. Next, EGF is employed to remove the additive noise and preserve edge information. In order to remove unwanted spots on the image that is filtered by EGF, it is applied twice with different parameters. Finally, the WLS filter is applied in the homogeneous region. Results show that the proposed algorithm has a better performance in comparison with the other existing filters.

SAR Image Despeckling Employing a Recursive Deep CNN Prior

Synthetic aperture radar (SAR) images are inherently affected by speckle noise, for which deep learning-based methods have shown good potential. However, the deep learning-based methods proposed until now directly map low-quality images to high-quality images, and they are unable to characterize the priors for all the kinds of speckle images. The variational method is a classic model optimization approach that establishes the relationship between the clean and noisy images from the perspective of a probability distribution. Therefore, in this article, we propose the recursive deep convolutional neural network (CNN) prior model for SAR image despeckling (SAR-RDCP). First, the data-fitting term and regularization term of the SAR variational model are decoupled into two subproblems, i.e., a data-fitting block and a deep CNN prior block. The gradient descent algorithm is then used to solve the data-fitting block, and a predenoising residual channel attention network based on dilated convolution is used for the deep CNN prior block, which combines an end-to-end iterative optimization training. In the experiments undertaken in this study, the proposed model was compared with several state-of-the-art despeckling methods, obtaining better results in both the quantitative and qualitative evaluations.

SAR Image Despeckling by Noisy Reference-Based Deep Learning Method

Traditionally, clean reference images are needed to train the networks when applying the deep learning techniques to tackle image denoising tasks. However, this idea is impracticable for the task of synthetic aperture radar (SAR) image despeckling, since no real-world speckle-free SAR data exist. To address this issue, this article presents a noisy reference-based SAR deep learning filter, by using complementary images of the same area at different times as the training references. In the proposed method, to better exploit the information of the images, parameter-sharing convolutional neural networks are employed. Furthermore, to mitigate the training errors caused by the land-cover changes between different times, the similarity of each pixel pair between the different images is utilized to optimize the training process. The outstanding despeckling performance of the proposed method was confirmed by the experiments conducted on several multitemporal data sets, when compared with some of the state-of-the-art SAR despeckling techniques. In addition, the proposed method shows a pleasing generalization ability on single-temporal data sets, even though the networks are trained using finite input-reference image pairs at a different imaging area.

SAR Image Despeckling Based on Combination of Fractional-Order Total Variation and Nonlocal Low Rank Regularization

Regularization method is an effective tool for synthetic aperture radar (SAR) image despeckling. Design of the effective regularization terms describing the image priors plays a vital role in this kind of method. In this article, a new combinational regularization model for speckle reduction (CRM-SR) is proposed, in which a regularization term is elaborately designed to contain both a fractional-order total variation (FrTV) regularization and a nonlocal low rank (NLR) regularization. The new regularization model inherits both the advantages of FrTV and NLR and improves the performance of SAR despeckling and, therefore, better preserves the edges and geometrical features of the images during the despeckling process. An efficient algorithm based on alternating direction optimization is derived to solve the proposed combinational regularization model. Experimental results show that the proposed model can effectively remove SAR image speckle and preserve the geometrical features of images according to both subjective visual assessment of image quality and objective evaluation.

HDRANet: Hybrid Dilated Residual Attention Network for SAR Image Despeckling

In order to remove speckle noise from original synthetic aperture radar (SAR) images effectively and efficiently, this paper proposes a hybrid dilated residual attention network (HDRANet) with residual learning for SAR despeckling. Firstly, HDRANet employs the hybrid dilated convolution (HDC) in lightweight network architecture to enlarge the receptive field and aggregate global information. Then, a simple yet effective attention module, convolutional block attention module (CBAM), is integrated into the proposed model to constitute a residual HDC attention block through skip connection, which further enhances representation power and performance of the model. Extensive experimental results on the synthetic and real SAR images demonstrate the superior performance of HDRANet over the state-of-the-art methods in terms of quantitative metrics and visual quality.

Deep Multi-Scale Recurrent Network for Synthetic Aperture Radar Images Despeckling

For the existence of speckles, many standard optical image processing methods, such as classification, segmentation, and registration, are restricted to synthetic aperture radar (SAR) images. In this work, an end-to-end deep multi-scale recurrent network (MSR-net) for SAR image despeckling is proposed. The multi-scale recurrent and weights sharing strategies are introduced to increase network capacity without multiplying the number of weights parameters. A convolutional long short-term memory (convLSTM) unit is embedded to capture useful information and helps with despeckling across scales. Meanwhile, the sub-pixel unit is utilized to improve the network efficiency. Besides, two criteria, edge feature keep ratio (EFKR) and feature point keep ratio (FPKR), are proposed to evaluate the performance of despeckling capacity for SAR, which can assess the retention ability of the despeckling algorithm to edge and feature information more effectively. Experimental results show that our proposed network can remove speckle noise while preserving the edge and texture information of images with low computational costs, especially in the low signal noise ratio scenarios. The peak signal to noise ratio (PSNR) of MSR-net can outperform traditional despeckling methods SAR-BM3D (Block-Matching and 3D filtering) by more than 2 dB for the simulated image. Furthermore, the adaptability of optical image processing methods to real SAR images can be enhanced after despeckling.

A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model

Despeckling is a longstanding topic in synthetic aperture radar (SAR) images. Recently, many convolutional neural network (CNN) based methods have been proposed and shown state-of-the-art performance for SAR despeckling problem. However, these CNN based methods always need many training data or can only deal with specific noise level. To solve these problems, we directly embed an efficient CNN pre-trained model for additive white Gaussian noise (AWGN) with Multi-channel Logarithm with Gaussian denoising (MuLoG) algorithm to deal with the multiplicative noise in SAR images. This flexible pre-trained CNN model takes the noise level as input, thus only a single pre-trained model is needed to deal with different noise levels. We also use a detector to find the homogeneous region automatically to estimate the noise level of image as input. Embedded with MuLoG, our proposed filter can despeckle not only single channel but also multi-channel SAR images. Finally, both simulated and real (Pol)SAR images were tested in experiments, and the results show that the proposed method has better and more robust performance than others.

Despeckling and Analysing Land Use Land Cover of Synthetic Aperture Radar Image

In Image processing, Synthetic Aperture Radar(SAR) images are inherently affected by speckle noise, which visually degrades the appearance of the images and may severely affect the quality of SAR image interpretation tasks like object detection or target detection, instance segmentation and image analysis. Hence SAR image Despeckling becomes a hot research issue. In this paper, the proposed method Total Variation (TV) Denoising is used to address this issue. It is applied to SAR imagery to decrease the noise. It is a filtering method which works efficiently. The process of decreasing the speckle noise is known as Despeckling. When there is noise in the image the actual data is affected. The actual meaning of noise is unwanted signal. Noise is an undesirable by-product in an image that disturbs the original image. On removal of noise, it results in the noise free SAR image. The Land Use Land Cover (LULC) analysis of a SAR image can be accurate when there is no noise in the SAR image. Therefore the main aim of this paper is to analyze the land use and land cover (LULC) in the despeckled high resolution image.

Sparse Representation Based Despeckling of SAR Images using STDTCWT

Synthetic Aperture Radar (SAR) is a coherent acquisition system and hence images obtained from Synthetic Aperture Radar are difficult to analyze due to the multiplicative speckle noise present in the images. Therefore, despeckling of SAR images is a primary speckle reduction task in SAR image processing. All the existing Despeckling methods focus on speckle reduction but not all methods can preserve the image edge features in SAR images. This paper propose a despeckling algorithm via sparse representation using hybrid Shearlet Transform and Dual Tree Complex wavelet transform that posses both shift invariant and direction selectivity properties in images. The experimental results show that the proposed hybrid Shearlet and Dual Tree Complex Wavelet Transform outperforms the existing methods of denoising in terms of PSNR, ENL, and EPI for MSTAR dataset images. The proposed methodology not only preserves the edges but also enhances the image visually.

SAR Image Despeckling Based on Nonlocal Low-Rank Regularization

In this paper, we propose a new synthetic aperture radar (SAR) image despeckling method based on the nonlocal low-rank minimization model. First, some similar image patches are selected for each pixel to construct the patch group matrix (PGM). Then, a new low-rank minimization model, called Fisher–Tippett distribution (FT)-weighted nuclear norm minimization (WNNM), is proposed to recover the underlying low-rank component from the PGM. Specifically, the FT-WNNM is developed by reformulating the despeckling problem as the maximizing a posterior probability problem. The new model consists of a data fidelity term and a regularization term (also called prior term). The data fidelity term is derived from the statistical distribution of SAR images in the logarithm domain, which is known as the Fisher–Tippett distribution, and the regularization term is the recent weighted nuclear norm. Then, the alternating direction method of multipliers (ADMM) is introduced to solve the corresponding optimization problem. Under ADMM framework, the resulting subproblems can be solved efficiently and the convergence can be guaranteed. Extensive experiments on both simulated and real SAR images demonstrate that the proposed method can achieve comparable or even better despeckling performance than some state-of-the-art despeckling algorithms.

Group Sparse Representation Based Dictionary Learning for SAR Image Despeckling

Since the sparse representation coefficients of synthetic aperture radar (SAR) images often appear in clusters with intrinsic structure, traditional sparse representation theory cannot capture this property. In this paper, the concept of group sparse representation (GSR) is utilized to exploit the intrinsic structure of SAR images. Different from traditional patch-based sparse representation theory, GSR is able to sparsely represent images in the domain of group which contains the image patches with similar structure. Based on the multiplicative speckle noise model, a novel dictionary learning algorithm based on GSR (GSR-DL) for SAR image despeckling is proposed. The proposed algorithm mainly consists of three steps. First, in order to realize the recovery of despeckled SAR image by the GSR model, a mean filter is included in the modeling process. Second, the proposed GSR-DL algorithm is used to calculate the optimal dictionary and group sparse representation coefficients. Third, the despeckled SAR image is reconstructed by the learned dictionary and coefficients. The experimental results on SAR images manifest that the proposed GSR-DL algorithm achieves a better performance than other state-of-the-art despeckling algorithms.

Adaptive pseudo-p-norm regularization based De-speckling of SAR images

ABSTRACTA novel variational model is proposed to de-speckle the multiplication noise in the synthetic aperture radar (SAR) image to provide a high-quality interpretation of SAR data. The model consists of the data fidelity term and the adaptive pseudo-p-norm regularization term. The data fidelity term ensures the convexity of the model and avoids the nonlinear image transformation. With an improved ratio of exponentially weighted averages (ROEWA) operator and the edge detection image, the exponent value of the pseudo-p-norm regularization term varies with the intensity of the edge adaptively, which results in a better retention of the image geometric structure while removing the speckle noise. Compared with the total variation (TV) regularization, the staircase-like artifacts can be restrained more effectively. Experiments demonstrate that the proposed method are more competitive both visually and quantitatively than that from the existing nonlocal mean-based and variational-based methods.

Distributed compressed sensing for despeckling of SAR images

Speckle noise is one of the critical disturbances that present in the radar imagery. This noise degrades the quality of synthetic aperture radar (SAR) images and needs to be reduced before using SAR images. This paper investigates a novel method for despeckling of SAR images in the distributed compressed sensing (DCS) framework. A linear matrix-based formulation is developed for the received SAR raw data and a compressive measurement and partitioning (CMP) scheme is proposed to collect and partition the SAR data into some data subsets. Then, a DCS-based despeckling method is proposed, in which, all data subsets are jointly considered in image formation and noise reduction. One of the important features of the proposed method is that the image formation and speckle reduction are done jointly. Finally, experimental results are provided to show the effectiveness of the proposed method in comparison to the previous ones.

Despeckling of SAR Images by Finding the Expected Values Using the Probability Distribution of Speckle

Synthetic aperture radar (SAR) images are contaminated by multiplicative speckle noise, which reduces the contrast and resolution of the images. To improve the quality and the performance of quantitative image analysis, speckle reduction is a prerequisite for SAR images. In this paper, a new method is proposed by calculating the expected value of all the pixel elements in the window with respect to the centre pixel. The weighted mean of all the expected values in the window replaces the centre pixel. The weights are calculated according to the height of the probability distribution. Thus, the expected value which has higher probability has more weightage. The proposed method is applied to the air-borne and space-borne SAR images. By comparing with some well-known filters, the obtained results demonstrate that the proposed method is able to reduce the noise effectively with accurately preserving edges and fine details of the images.

SAR image despeckling using a dilated densely connected network

ABSTRACTVarious type of deep convolution neural network (CNN) have been successfully applied to synthetic aperture radar (SAR) image despeckling. However, the model is limited by the length of network structure and expensive computation. In this paper, we proposed a novel deep learning-based approach learning the mapping from noisy image to noisy-free image with a dilated densely connected network (SAR-DDCN). Compared with the recently proposed despeckling methods, our method can achieve superior performance with less computational cost. Specifically, we replace standard convolution with dilated convolution, which could enlarge the receptive field and keep the size of feature maps at the same time. In addition, densely connected network are added to the network to alleviate the problem of gradient vanishing. Extensive experiments on synthetic and real SAR image show that the proposed method achieve better performance with respect to state-of-the-art-methods on both quantitative measurements and visual results.