Simulated Synthetic Aperture Radar Images Research Articles

Laplacian pyramid-based change detection in multitemporal SAR images

ABSTRACT The Synthetic Aperture Radar (SAR) has many attractive and desired features to cope with uncertain weather conditions and also provides superlative resolution in images. In this paper, a unique multiscale approach of change detection (CD) that integrates the preprocessing and CD technique of SAR imagery is discussed. Condensed anisotropic diffusion method is employed as a preprocessing tool for despeckling, and the resultant image is processed by multiscale approach in the Laplacian pyramid (LP) domain. In each layer of LP, the difference between the pyramid coefficient of two images of the same area obtained at different periods is estimated through log ratio operation. Each layer of pyramid coefficient has explicit features at particular scale. Fine scale related to a band pass layer localizes edges of the change area and the coarse scale related to a low pass layer preserves more detail information of the change region. Reconstruction of LP provides exact difference image with perfect boundary. Otsu's algorithm is used to generate binary change map to distinguish changed and unchanged pixels. The efficiency of the proposed method is tested by using simulated and real SAR image datasets. The confusion matrix parameters are used to prove competence of the proposed CD method.

SRAD-CNN for adaptive synthetic aperture radar image classification

ABSTRACTThe performance of synthetic aperture radar (SAR) image classification based on a conventional convolutional neural network (CNN) is limited by a trade-off between immunity to speckle noise and the ability to locate boundaries accurately. Difficulties regarding the accurate location of boundaries are a result of the smoothing effect of the pooling layer. To address this issue, we propose a novel framework called SRAD-CNN for SAR image classification. In this framework, we apply a filtering layer constructed according to prior knowledge of the speckle reducing anisotropic diffusion (SRAD) filter. The filtering layer can not only reduce speckle but also enhance the boundaries. The main parameter that controls the degree of filtering can be optimized adaptively by a backpropagation algorithm. Image patches adaptively filtered by the filtering layer are then put into the CNN layers to assign a label. Due to the effect of the filtering layer, for our proposed SRAD-CNN, both the speckle noise immunity and the sensitivity to boundaries are superior to those of conventional CNN.To confirm the performance of the proposed SRAD-CNN, we conducted experiments using both simulated and real SAR images. The experimental results demonstrated that the parameter of the filtering layer could be optimized adaptively for different scenes, different noise levels, and different image resolutions. The SRAD-CNN outperformed the conventional CNN in both overall classification accuracy and maintenance of boundary accuracy on images with different resolutions and noise levels with limited training samples.

Detecting dark spots from SAR intensity images by a point process with irregular geometry marks

ABSTRACTAiming at the difficulties to determine the geometric shape of dark spots, this paper proposes an Irregular Geometry Marked Point Process (IGMPP) to detect dark spots from Synthetic Aperture Radar (SAR) images. Firstly, a series of random points used to define the locations of dark spots in the SAR image domain, and an irregular polygon mark associated with each random point indicates the shape of each dark spot. Subsequently, the pixels intensities in and out of the polygons are characterized with independent and identical Gamma distributions, respectively. By Bayesian paradigm, the number, sites, and geometry parameters of polygons are modelled with a posterior distribution. In order to simulate the posterior, a Reversible Jump Markov Chain Monte Carlo (RJMCMC) strategy is developed. Then, the optimal parameters concerning the dark spots can be obtained by a Maximum A Posteriori (MAP) scheme. Experiments are performed by simulated SAR images. The experimental results show that the proposed algorithm is effectively and efficiently applied to detect the dark spots.

A Boosting SAR Image Despeckling Method Based on Non-Local Weighted Group Low-Rank Representation.

In this paper, we propose a boosting synthetic aperture radar (SAR) image despeckling method based on non-local weighted group low-rank representation (WGLRR). The spatial structure information of SAR images leads to the similarity of the patches. Furthermore, the data matrix grouped by the similar patches within the noise-free SAR image is often low-rank. Based on this, we use low-rank representation (LRR) to recover the noise-free group data matrix. To maintain the fidelity of the recovered image, we integrate the corrupted probability of each pixel into the group LRR model as a weight to constrain the fidelity of recovered noise-free patches. Each single patch might belong to several groups, so different estimations of each patch are aggregated with a weighted averaging procedure. The residual image contains signal leftovers due to the imperfect denoising, so we strengthen the signal by leveraging on the availability of the denoised image to suppress noise further. Experimental results on simulated and actual SAR images show the superior performance of the proposed method in terms of objective indicators and of perceived image quality.

Imaging Simulation for Synthetic Aperture Radar: A Full-Wave Approach

Imaging simulation of synthetic aperture radar (SAR) is one of the potential tools in the field of remote sensing. The echo signal in imaging simulation based on the point target model cannot be linked to practical scenes due to the model being a simple mathematical form, stating only the synthetic process and lacking the physical process based on electromagnetic theory. In this paper, the full-wave method is applied to include the electromagnetic effects in raw data generation, and then a refined omega-K algorithm is used to perform image focusing. According to the proposed method, the focused images not only demonstrate the difference under dielectric constant variation but also present the diversified interaction among the targets with the spacing change. In addition, the images are simulated in different observation modes and bandwidths to provide a satisfactory reference for the design of system parameters. The simulation results from the full-wave method also compare well with chamber experiments. The simulation of SAR imaging based on a full-wave method offers more complete recovery of scattering information and is useful in designing future novel SAR systems and in speckle reduction analysis.

Antistretch Edge Detector for SAR Images

In this letter, an antistretch edge detector has been proposed for synthetic aperture radar (SAR) images. Traditional detectors using anisotropy edge detection filters often incur severe edge stretch. If isotropy edge detection filters are used, the detectors usually have poor edge resolution. Hence, we skillfully fuse an anisotropy edge detection filter with an isotropy one. By embedding the fused filter into the routine ratio-based SAR edge detector, an antistretch edge detector is proposed. Benefiting from the fused edge detection filter, the proposed edge detector can obtain a good antistretch ability and keep a high edge resolution. A theoretical analysis indicates that the computational complexity of the proposed edge detector is close to conventional ratio-based edge detectors. The fusion operation will not affect the constant false alarm rate property. The receiver-operating-characteristic curves are used to objectively evaluate the proposed detector. Experimental studies show that the proposed detector has a lower false positive rate than the majority of detectors using only anisotropy filters or isotropy filters. Furthermore, the experimental results on simulated and real-world SAR images show that the proposed antistretch edge detector can obtain an accurate edge map.

A Unified Approach of Multitemporal SAR Data Filtering Through Adaptive Estimation of Complex Covariance Matrix

Speckle inherent in synthetic aperture radar (SAR) images usually complicates visual interpretation and brings difficulty to information extraction for applications. Current speckle filters are mainly developed for single SAR image or an image pair (InSAR or PolInSAR). Although some multichannel filters are proposed, they only exploit pixel intensity to identify statistically homogeneous pixels (SHPs). In this paper, we present a new unified approach to filter multitemporal SAR images by adaptively estimating complex covariance matrix-based multitemporal filtering, named CCM-MTF. The key idea is to employ generalized likelihood ratio (GLR) test on the Wishart distributed initial CCM to evaluate the similarity between two pixels. A special design is given to the initial CCM estimation, in which temporal samples are used instead of spatially neighboring samples. Then, a threshold determined by the asymptotic distribution of the logarithm of GLR test statistics at a fixed significance level is used to select spatial SHPs for the reference pixel. Subsequently, the filtering is implemented by estimation of the final CCM from original SAR scattering vector over SHP pixels, and all filtered target information channels including intensity, interferometric phase, and coherence can be explicitly derived from the final CCM. The effectiveness of the proposed CCM-MTF method is validated by experiments on both simulated and real multitemporal SAR images. Both qualitative and quantitative comparisons between CCM-MTF and four state-of-the-art SAR filters are carried out to demonstrate its advantages in terms of speckle suppression as well as detail preservation for all the three information channels.

Decreased Probability of Error in Template-Matching Classification Using Aspect-Diverse Bistatic SAR

We extend the concept of monostatic aspect diversity for improved automatic target recognition (ATR) to a bistatic synthetic aperture radar (SAR) platform. We derive the probability of error with respect to the number of aspects used for a simple two-target template-matching classification system. The validity of the error prediction is confirmed using simulated bistatic SAR images. Our results demonstrate the ATR benefits of supplementing monostatic SAR images with one or more bistatic SAR images.

Synthetic aperture radar image simulation of the internal waves excited by a submerged object in a stratified ocean

ABSTRACTA submarine moving in a stratified fluid with sharp interfaces is considered. The velocity field and the waves created by a moving submarine on a sea surface are simulated with the computational fluid dynamics numerical method. The wakes in various patterns with different parameters are discussed and illustrated in both geometries and synthetic aperture radar (SAR) images. The hydrodynamic fields are utilized to obtain the electromagnetic (EM) scattering signatures of the internal waves. In order to simulate SAR images of the generated wakes, the spatial distributed EM field is necessary. For this purpose, the semi-deterministic facet scattering model is employed. The SAR images in different look directions demonstrate that the micro-internal waves on sea surface can be sensed from its Bragg component moving in the radar look direction by SAR.

Synthetic Aperture Radar Image Segmentation with Reaction Diffusion Level Set Evolution Equation in an Active Contour Model

This paper presents a method for synthetic aperture radar (SAR) image segmentation by draing upon a reaction–diffusion (RD) level set evolution (LSE) equation. The well-known RD theory consists of two main parts: reaction and diffusion terms. We first constructed the reaction term using an energy functional, which integrates the gamma statistical distribution with region–edge information from SAR images that can simultaneously suppress speckle noise and drive the active contour toward the object boundaries. Then, we used partial differential equation-based LSE to solve the proposed energy functional. Finally, a diffusion term was introduced into the LSE to ensure stability of the level set function and regularize the segmented region. The experimental results of both simulated and real SAR images showed that the proposed model has good robustness against a speckle noise as well as higher segmentation efficiency and accuracy than some existing models.

A new SAR image despeckling using directional smoothing filter and method noise thresholding

The removal of granular pattern multiplicative speckle noise is the major issue in Synthetic Aperture Radar (SAR) images. Theoretically, multiplicative noise is considered as the ratio of the standard deviation to the signal value. The proposed scheme works on db2 based 2D-Discrete Wavelet Transform (DWT) using wavelet thresholding and method noise. The scheme is designed to despeckle the simulated SAR images and real SAR images. It uses a hybrid combination of Directional Smoothing Filter (DSF), wavelet thresholding using enhanced Bayesian shrinkage rule and method noise for despeckling. After DWT decomposition, the approximate component is directed to DSF followed by method noise thresholding and detailed components are subjected to wavelet thresholding followed by method noise thresholding. The article justifies the efficient use of method noise and explains how its exact use can enhance the result of the algorithm over other efficient methods. The quality assessment of the proposed method is done by visual appearance and measures such as Peak Signal-to-Noise Ratio (PSNR), Structural similarity index metric (SSIM), Universal Image Quality Index (UIQI), Equivalent Numbers of Looks (ENL), Noise Variance (NV), Coefficient of Variation (CV), Mean Squared Error (MSE), Correlation Coefficient (CC) and preservation of mean values before and after despeckling. The effectiveness of the proposed method is demonstrated by matching it to well-known speckle noise removal methods on SAR images. The proposed method has the capability to be used in real time practical applications.

A Comparison of Numerically Modelled Iceberg Backscatter Signatures with Sentinel-1 C-Band Synthetic Aperture Radar Acquisitions

Use of machine learning to develop algorithms for distinguishing iceberg and vessel targets requires large validated data sets that are often costly, time consuming and, in some cases, inaccessible. Generating electromagnetic (EM) backscatter models of iceberg and ship targets can be a vital step in developing a robust iceberg/ship classification algorithm. In this work, EM backscatter models for icebergs are developed using an EM backscatter modelling tool called GRECOSAR and compared with ground truth data. The imaging scene consists of iceberg targets surrounded by the ocean surface. The 3D computer aided design models of the icebergs were obtained using LiDAR and multi-beam sonar data collected during a field program off the coast of Salvage, Newfoundland and Labrador, Canada. While profiling the iceberg targets, a synthetic aperture radar (SAR) image from Sentinel-1A was captured and compared with the simulated SAR images. Comparisons made in terms of total radar cross section (TRCS) and the SAR signature of the targets generally indicate credible simulations. Simulated SAR images were generated at low and high dielectric conditions to mimic cold and melt iceberg surfaces. Variability of the TRCS and morphology as a function of target orientation highlights the usefulness of EM modelling in developing robust iceberg/ship classifiers.

A Robust Fuzzy C-Means Algorithm Based on Bayesian Nonlocal Spatial Information for SAR Image Segmentation

The fuzzy c-means (FCM) algorithm and many improved algorithms incorporating spatial information have been proven to be effective in image segmentation. However, these methods are not adaptable to process synthetic aperture radar (SAR) images owing to the intrinsic speckle noise. Our solution, which enables the effective segmentation of SAR images by guaranteeing noise-immunity and edge detail preservation simultaneously, is to propose a robust FCM algorithm based on Bayesian nonlocal spatial information (RFCM $\_$ BNL). The nonlocal idea considers more useful information for generating an auxiliary image. We measure the similarity between patches by utilizing a dedicated noise model for SAR images, and then apply it to the Bayesian formulation. Then we derive a new statistical distance, which is insensitive to speckle noise. Additionally, we ensure that the algorithm is robust to outliers by employing the entropy of the local gray-level histogram to control the extent to which the nonlocal spatial information term is adaptive to pixels. Experiments on simulated and real SAR images show that RFCM $\_$ BNL obtains the best result for SAR image segmentation compared with seven other fuzzy clustering algorithms.

Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering

ABSTRACTChange detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods.

Synthetic aperture radar image segmentation using non‐linear diffusion‐based hierarchical triplet Markov fields model

Triplet Markov fields (TMF) model is widely used to deal with non-stationary synthetic aperture radar (SAR) images. However, its ability to capture global information remains limited due to the non-causal property. A hierarchical TMF model is proposed in this study based on the non-linear diffusion (ND) strategy, which is denoted as ND-hierarchical TMF (HTMF). ND is adopted to generate multiscale decomposition according to local image content, and that is superior to traditional wavelet decomposition in reflecting hierarchical nature of image structure and detailed features. The auxiliary field in ND-HTMF is redefined and initialised on the finest scale to characterise edge information and that enhances the prior modelling ability for non-stationary local image features. The multiscale likelihood and multiscale causal prior energy functions are then defined respectively in bottom-up and top-down procedures to capture local and global information for performing segmentation. Segmentation experiments on simulated and real SAR images demonstrate the effectiveness of ND-HTMF in both edge characterisation accuracy and robustness against speckle noise.

An Advanced Multiscale Edge Detector Based on Gabor Filters for SAR Imagery

The ratio of averages is a robust edge detector which provides the property of constant false alarm rate for synthetic aperture radar (SAR) imagery. However, the rectangular window used in the calculation of local mean may cause numerous false maxima. The size of the processing window also has a significant effect on the detection performance, but it is difficult to determine the optimum window size. In this letter, we first propose a new ratio-based detector that is constructed by the Gabor odd filter. The scale of the proposed detector is related to the size of the processing window. Then, edge strength maps extracted by multiscale detectors are combined using an edge tracking algorithm to form a final response. We used the receiver operating characteristic curves to evaluate the performance of the proposed detector. The experimental results on simulated and real-world SAR images show that the proposed multiscale edge detector yields an accurate and consecutive edge response.

Analytical Modeling of Rough Surface SAR Images Under Small Perturbation Approximation

In high-resolution synthetic aperture radar (SAR) images, the spatial pattern of a homogenous region conveys rich information regarding the specific scatterers being imaged. Analytical modelings of both scattering and imaging processes are critical to better interpret a specific scatterer’s appearances in SAR imagery and thereafter quantitatively retrieve its physical parameters. Small-scale rough surface scattering represents one of the mostly common scattering mechanisms and yet its SAR image characteristics have not been well studied. This becomes more critical in millimeter-wave/terahertz regime as smooth surfaces would become slightly rough under millimeter/submillimeter wavelengths. In this paper, we recast the small perturbation method (SPM) approximation of rough surface scattering under the deterministic finite-length surface condition. By ignoring evanescent waves, a simplified SPM solution for rough facet is derived as well as the analytical form of its SAR image under the conventional setup. Then, we reformulate the scattering imaging process of rough facet as a signal processing chain that further reveals the underlying mechanism of rough surface as imaged by radar. The proposed method is extensively validated against the method of moments in terms of both scattering coefficients and imaging patterns. It is found that under the conventional SPM validity condition, the error of scattering coefficients is less than 1.5 dB, while the correlation between SPM-calculated and MoM-calcualted SAR images is larger than 0.9. The proposed model of rough facet SAR image could serve as the theoretical basis for parameter inversion and surface reconstruction. Preliminary cases of the inversion of rough surface spectrum using both simulated and real SAR image patches are presented.

SAR imaging simulation of ship-generated internal wave wake in stratified ocean

Simulation of synthetic aperture radar (SAR) imaging of ship-generated internal wave wake in stratified ocean is conducted through the combination of scattering and modulation effects under the background of wind waves. With the derived surface velocity of internal wave wake from the velocity potential, a reconstruction of the wave elevation is implemented by means of the frequency decomposition technique. Based on the hydrodynamic model, the facet scattering model is presented for the fully polarimetric backscattering and the velocity bunching imaging model is applied to the SAR image simulation of surface waves with an embedded internal wave wake. Numerical simulations show that the wave patterns of internal wave wake, which modulates the distribution of Bragg waves on sea surface, are closely dependent on ship speed. And the conditions are classified for a good visibility of internal wave wake in SAR image. Besides, the great agreement with measurement strongly verifies the validity of the simulation.

A Study of Ship Rotation Effects on SAR Image

Imaging distortions induced by each particular rotation of a moving ship are quantitatively investigated through numerical simulations. Two sets of dynamics are considered in the model, namely a ship with pitch, yaw, and roll rotations, and the time evolutions of its wake. To construct high-resolution synthetic aperture radar (SAR) images in spotlight mode, the time-varying scattering from the electrically very large scene is computed by a parallel quasi-stationary algorithm, in which the physical optics (PO) method is used to compute the scattering from the ship, and a PO phase correction of the two-scale model is used to take account of the Doppler effects caused by the wake. Reasonable agreement is obtained when comparisons are made between the simulated and real SAR images. A range of imaging distortions are observed and analyzed, such as the displacement, rotation, stretching/compressing, and broaden/narrow of the ship image. A systematic analysis shows that these distortions can be characterized by four main types of transformations, namely translation, rotation, scaling, and shearing. This paper presents quantitative insights into the data interpretation and signature classification of ship on SAR image.

A Study on Simulation of High Resolution SAR Image of Complex Ground Target

SAR (Synthetic Aperture Radar) is used to detect the complex ground target, which can obtain the image of the military target. There is a great difference between the SAR image and the traditional optical image, and SAR image responses the distribution characteristics of the ground target microwave scattering which produces great challenges in the interpretation of the SAR image. A simulator method based on electromagnetic scattering characteristics of the target was presented to obtain SAR image of targets on the ground. According to the turntable imaging model, the method that classification interpolation of digital elevation model of the real ground simulated was used to build the ground structure. And the target’s electromagnetic scattering and the RCS (Radar Cross-Section) were computed by the graphical electromagnetic computing (GRECO) method. The accelerating ray-tracing method was applied to compute the interaction between the target and ground. A SAR simulation imaging system was built to simulate the SAR image of a tank target placed in the ground. The simulation result indicated that the simulated image fits the scattering characteristics of the actual object and it could provide recognition template for SAR image interpolation with the ground SAR image simulation based on electromagnetic scattering characteristics.