Speckle In Synthetic Aperture Radar Images Research Articles

MRDDANet: A Multiscale Residual Dense Dual Attention Network for SAR Image Denoising

Synthetic aperture radar (SAR), due to its inherent characteristics, will produce speckle noise, which results in the deterioration of image quality, so the removal of speckle in SAR image is very important for the subsequent high-level image processing. In order to balance the relationship between denoising and texture preservation, we propose a multiscale residual dense dual attention network (MRDDANet) for SAR image denoising. This algorithm can effectively suppress the speckle while fully retaining the texture details of the image. In MRDDANet, shallow features are extracted from the noisy images by multiscale modules with different kernel sizes, and then, the extracted shallow features are mapped to the residual dense dual-attention network to obtain the deep features of SAR image. Finally, the final denoising image is generated through global residual learning. MRDDANet has advantages of both multiscale blocks and residual dense dual attention networks. The dense connection can fully extract features in the image, and the dual-channel attention enables MRDDANet to pay more attention to noise information, which is beneficial to remove noise and keep the details of the original image at the same time. Compared with state-of-the-art algorithms, the results of the experiment indicate that our method not only improves various objective indicators but also shows great advantages in visual effects.

SAR image despeckling using deep CNN

Synthetic aperture radar (SAR) images are contaminated with noise called speckle that is multiplicative in nature. The presence of speckles in SAR images makes it impossible to understand and interpret for extensive range of applications. However, certain characteristics of SAR and the ability to function irrespective of weather conditions, are making such images worth processing in order to be able to extract relevant information. A despeckling model is proposed that uses deeper convolutional neural networks, which was never used before, as far as authors are concerned, for diminishing speckle in noisy SAR images. Multiple skip connections from the ResNet model are also employed in authors' proposed architecture. In order to maintain uniformity, a formula to be followed while applying skip connections is also derived. A hybrid loss function is developed to train the network more consistently to achieve the desired output. Experiments on simulated SAR images using the NWPU-RESISC benchmark are conducted and tested on real TerraSAR-X images and compared with the state-of-the-art techniques. Results show that the proposed method achieved considerable improvements compared to state-of-the-art methods with PSNRs 27.02, 24.60, and 22.01 for looks 10, 4, and 1, respectively.

Synthetic Aperture Radar Image Speckle Noise Suppression and Mine Target Detection

Synthetic Aperture Radar (SAR) provides a comprehensive, all-weather target monitoring function and is widely used in the military and industrial fields of defense. This article mainly studies a landmine target detection technology based on synthetic aperture radar image processing, shallow buried mines. The detection and identification of military radar has important military significance, which can reduce the casualties caused by landmine explosions. This article first introduces the basic imaging principle of synthetic aperture radar, then introduces the noise processing of SAR images based on the Gamma filter, and finally targets the targets of mine SAR images Features were identified and analyzed.

A Gamma Distribution-Based Fuzzy Clustering Approach for Large Area SAR Image Segmentation

Synthetic aperture radar (SAR) image segmentation is a challenge due to its inherent speckle. Gamma distribution is believed to be an appropriate statistical model to describe the characteristics of speckle in SAR images. In this letter, a fuzzy clustering algorithm based on gamma distribution for SAR image segmentation is proposed, in which the Stirling equation is used to approach the gamma function in the dominator of gamma distribution under the assumption of mean field theory to make the shape parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> derivable. Then, the value range of the estimated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> is demonstrated to meet the requirement of gamma distribution by Jensen’s inequality. Experimental results show that the proposed method gives promising results in SAR image (including large area SAR image) segmentation and effectively suppresses the influence of speckle.

SAR Image Speckle Reduction Based on Nonconvex Hybrid Total Variation Model

Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called Fisher-Tippett (FT) distribution-ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method.

Product Dictionary Learning-Based SAR Target Configuration Recognition

Dictionary construction is a key factor for the sparse representation- (SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. The proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. The accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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.

SAR Image Speckle Filtering With Context Covariance Matrix Formulation and Similarity Test

Speckle filtering of synthetic aperture radar (SAR) image is a necessary pre-processing for many subsequent applications. The challenge lies in how to adaptively select a sufficient number of similar pixels for an unbiased estimator generation. A novel SAR speckle filter is proposed and the core idea contains two aspects. Firstly, a context covariance matrix representation is developed within a local neighborhood to characterize the contexture information. Then, the Wishart statistic test is extended to examine the similarity of context covariance matrices. The extended similarity test indicator derived from context covariance matrices is verified to be sensitive for similar pixel localization. Thereafter, a sample averaging estimator is adopted based on the similar samples determined by the context covariance matrices similarity test (the proposed method is named as the CCM+SimiTest). Furthermore, a fast similarity test computation scheme is established which can handle large images smoothly even with a normal laptop. Intensive experimental studies with Radarsat-2, MiniSAR and ALOS-2 datasets are carried out. Comparisons with several state-of-the-art methods from both subjective and objective viewpoints demonstrate the superiority of the proposed method.

Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

Aiming at the problem of the difficulty of high-resolution synthetic aperture radar (SAR) image acquisition and poor feature characterization ability of low-resolution SAR image, this paper proposes a method of an automatic target recognition method for SAR images based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN). First, the threshold segmentation is utilized to eliminate the SAR image background clutter and speckle noise and accurately extract target area of interest. Second, the low-resolution SAR image is enhanced through SRGAN to improve the visual resolution and the feature characterization ability of target in the SAR image. Third, the automatic classification and recognition for SAR image is realized by using DCNN with good generalization performance. Finally, the open data set, moving and stationary target acquisition and recognition, is utilized and good recognition results are obtained under standard operating condition and extended operating conditions, which verify the effectiveness, robustness, and good generalization performance of the proposed method.

A STATISTICAL TEXTURE FEATURE FOR BUILDING COLLAPSE INFORMATION EXTRACTION OF SAR IMAGE

Abstract. Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G0 distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Enhancement of SAR images using fuzzy shrinkage technique in curvelet domain

The synthetic aperture radar (SAR) images are mainly affected by speckle noise. Speckle degrades the features in the image and reduces the ability of a human observer to resolve fine detail, hence despeckling is very much required for SAR images. This paper presents speckle noise reduction in SAR images using a combination of curvelet and fuzzy logic technique to restore speckle-affected images. This method overcomes the limitation of discontinuity in hard threshold and permanent deviation in soft threshold. First, it decomposes noise image into different frequency scales using curvelet transform, and then applies the fuzzy shrinking technique to high-frequency coefficients to restore noise-contaminated coefficients. The proposed method does not use threshold approach only by proper selection of shrinking parameter the speckle in SAR image is suppressed. The experiment is carried out on different resolutions of RISAT-1 SAR images, and results are compared with the existing filtering algorithms in terms of noise mean variance (NMV), mean square difference (MSD), equal number of looks (ENL), noise standard deviation (NSD) and speckle suppression index (SSI). A comparison of the results shows that the proposed technique suppresses noise significantly, preserves the details of the image and improves the visual quality of the image.

A novel thresholding technique in the curvelet domain for improved speckle removal in SAR images

Speckle noise is the grainy salt-and-pepper pattern present in radar imagery caused by the interaction of out-of-phase waves with a target. Speckle degrade the spatial resolution, contrast of the image, decreases object detectability and causes inconvenience to the SAR (Synthetic Aperture Radar) image interpreter. Reduction of speckle can be carried out for proper interpretation and object detection. In this work, an improved thresholding technique in the curvelet domain is proposed to reduce the speckle in SAR images and preserve the details of the image. The results of denoised images are compared visually and statistically. The performance of the proposed technique is better compared with improved thresholding technique in the wavelet domain, curvelet denoising and curvelet cycle spin technique.

Synthetic aperture radar image despeckling based on adaptive iterative risk estimator

Some classical filters, such as bilateral filter, nonlocal means (NLM) filter, and locally adaptive regression kernel, have proven to have a good performance in image denoising. However, there is one shortcoming, i.e., they cannot control the denoising strength very well. As for synthetic aperture radar (SAR) images, due to the special multiplicative noise, the denoising process becomes more complicated. A diffusion iterative filter can enhance the performance of a kernel in the denoising process but will lose some latent details and important targets from the underlying SAR image. In contrast, an iterative boosting filter can preserve these latent details of the SAR image well, although the improvement of the kernel performance is not very desired. By adopting the advantages of diffusion and boosting of the two iterative methods, an adaptive iterative risk estimator minimum mean square error (Min-MSE) method is proposed, which is mainly based on the Min-MSE to adaptively get the optimal iterative method and the corresponding optimal iterative number. The analysis of the experimental results and the comparison with some other state of the art methods demonstrate that our proposed method can improve the performance of an NLM filter and effectively suppress the SAR image speckle.

English

In the past two decades, many speckle reduction techniques have been developed for removing speckle and retaining edge details in Synthetic Aperture Radar(SAR) images. Most of the standard algorithms use a defined filter window to estimate the local noise variance of a speckle image and perform the individual unique filtering process.The primary goal of speckle reduction is to remove the speckle without losing much detail contained in an image. To achieve this goal, we make use of a mathematical function known as the wavelet transform and apply multiresolution analysis to localize an image into different frequency components or useful sub-bands and effectively reduce the speckle in the sub-bands according to the local statistics within the bands. The main advantage of the wavelet transform is that the image fidelity after reconstruction is visually lossless.In this project, we will study and investigate the application of using the Daubechies wavelet with denoising techniques to remove speckle in SAR images. We combine the wavelet shrinkage denoising techniques with different wavelet basis and decomposition levels on the individual sub-bands to achieve the best acceptable speckle reduction while maintaining the fidelity of the image. The analysis of the statistical results will be calculated using matlab to demonstrate the advantages and disadvantages of using complex wavelet shrinking techniques over standard speckle filters.

SAR Image Segmentation via Hierarchical Region Merging and Edge Evolving With Generalized Gamma Distribution

This letter proposes a novel segmentation algorithm for synthetic aperture radar (SAR) images based on hierarchical region merging and edge evolving. To cope with the influence of speckle in SAR images, a statistical stepwise criterion, the loss of log-likelihood function (LLF) of image partition, is utilized for region merging. For this merging procedure, precise distributions of image partitions are essential, and we employ the generalized gamma distribution (GΓD) for modeling SAR images. Besides, the traditional region merging methods often suffer from the initial image partition that may lead to coarse segment shapes. It motivates us introducing a novel edge evolving scheme into the segmentation algorithm. It consists of two iterative steps: the evolution of edge pixels with a maximum likelihood (ML) criterion and that with a maximum a posterior (MAP) criterion using a Markov random field (MRF) model. The performance of the proposed algorithm is validated on two actual SAR images from the AIRSAR and EMISAR systems.

Study on Synthetic Aperture Radar Image Denoising Algorithm

In synthetic aperture radar (SAR) inherent speckle will affect the legibility of image details; give the image target detection adverse effects. In order to reduce the SAR image speckles noise, this article provided an improved algorithm based on median filter and wavelet semi-soft threshold shrinkage. First, reduced the SAR image speckle with median filtering method, then, with the filtered image, filtering the image with wavelet half soft threshold value contraction algorithm to noise, Simulation results show that the algorithm based on median filtering and improved algorithm of wavelet half soft threshold shrinkage of SAR image can better remove the speckle noise of the SAR image, while keep better edge, in Equivalent Number of Looks (ENL) and edge keep ability (FOM) aspects, it would be better than median filtering.

A Kernel Clustering Algorithm With Fuzzy Factor: Application to SAR Image Segmentation

The presence of multiplicative noise in synthetic aperture radar (SAR) images makes segmentation and classification difficult to handle. Although a fuzzy C-means (FCM) algorithm and its variants (e.g., the FCM_S, the fast generalized FCM, the fuzzy local information C-means, etc.) can achieve satisfactory segmentation results and are robust to Gaussian noise, uniform noise, and salt and pepper noise, they are not adaptable to SAR image speckle. This letter presents a kernel FCM algorithm with pixel intensity and location information for SAR image segmentation. We incorporate a weighted fuzzy factor into the objective function, which considers the spatial and intensity distances of all neighboring pixels simultaneously. In addition, the energy measures of SAR image wavelet decomposition are used to represent the texture information, and a kernel metric is adopted to measure the feature similarity. The weighted fuzzy factor and the kernel distance measure are both robust to speckle. Experimental results on synthetic and real SAR images demonstrate that the proposed algorithm is effective for SAR image segmentation.

Typhoon eye extraction with an automatic SAR image segmentation method

Spaceborne microwave synthetic aperture radar (SAR), with its high spatial resolution (10–100 m), large area coverage, and day/night imaging capability, has been used as an important tool for typhoon monitoring. Since the microwave signal can penetrate through clouds, SAR images reveal typhoon morphology at the sea surface. Within the region of a typhoon eye, wind speed and the associated sea surface roughness are usually low. Therefore, the typhoon eye can be well distinguished as dark areas in SAR images. However, automatic typhoon eye extraction from SAR images is hampered by SAR image speckle noise and other false-alarm dark features contained in an image. In this study, we propose an image processing approach to extract typhoon eyes from SAR images. The three-step image processing includes: (1) applying an extended non-local means image denoizing algorithm to reduce image speckle noise; (2) applying a top-hat transform to denoized imagery to enhance the contrast; and (3) using a labelled watershed to segment the typhoon eye. Experimental results from analysing three Environmental Satellite SAR typhoon images show that our approach provides fast and efficient SAR image segmentation for typhoon eye extraction. Typhoon eyes are segmented correctly, and their edges are well detected. Our experimental results are comparable to manually extracted typhoon eye information. Fine-tuning of this approach will provide an automatic tool for typhoon eye information extraction from SAR images.

A NOVEL MINEFIELD DETECTION APPROACH BASED ON MORPHOLOGICAL DIVERSITY

Battlefleld surveillance is a common application of synthetic aperture radar (SAR), in which minefleld detection is a challenging task. In this paper, a novel minefleld detection approach is proposed via the morphological diversities between targets and background. Firstly, SAR image speckle is suppressed efiectively by total variation, and targets edges are preserved well. Secondly, a nonlinear transform is introduced to map the special distributed targets, e.g., landmines, into spot targets. Lastly, the modiflcation of morphological component analysis is adopted to improve the signal- to-clutter ratio and separate the spot targets from image. The performance of the proposed approach is validated by using the data acquired over an airship mounted SAR system.

Sar Image Denoising Based on Multifractal and Regularity Analysis

Synthetic Aperture Radar (SAR) images are corrupted by multiplicative speckle noise, which is due to random interference of electromagnetic waves. The noise degrades the quality of images and makes them hard to be interpreted, analyzed and classified. It appears sensible to reduce speckle in SAR images, while the structural features and textural information are not lost. This paper applies the framework of multifractal and regularity analysis to SAR image enhancement and denoising. The method does not make explicit assumptions about the model of the noise, but rather supposes that image denoising is equivalent to increasing the Hölder exponent at each point. The image is characterized via its multifractal spectrum, which mode yields the most frequent Hölder exponent. This manipulation leads to a smooth image while preserving the useful information in the signal. In order to evaluate the restoration result, Equivalent Number of Look (ENL) and edge save index (ESI) are used as criterion. Better result is obtained when regularity increase equal 0.5 (δ=0.5).