Resolution Synthetic Aperture Radar Images Research Articles

A tool for estimating ground-based InSAR acquisition characteristics prior to monitoring installation and survey and its differences from satellite InSAR

Abstract. Synthetic Aperture Radar (SAR) acquisition can be performed from satellites or from the ground by means of a so-called GB-InSAR (Ground-Based Interferometry SAR), but the signal emission and the output image geometry slightly differ between the two acquisition modes. Those differences are rarely mentioned in the literature. This paper proposes to compare satellite and GB-InSAR in terms of (1) acquisition characteristics and parameters to consider; (2) SAR image resolution; and (3) geometric distortions that are foreshortening, layover, and shadowing. If in the case of satellite SAR, the range and azimuth resolutions are known and constant along the orbit path, in the case of GB-InSAR their values are terrain-dependent. It is worth estimating the results of a GB-InSAR acquisition that one can expect in terms of range and azimuth resolution, line of sight (LoS) distance, and geometric distortions to select the best installation location when several are possible. We developed a novel tool which estimates those parameters from a digital elevation model (DEM), knowing the GB-InSAR and the slope of interest (SoI) coordinates. This tool, written in MATLAB, was tested on a simple synthetic point cloud representing a cliff with a progressive slope angle to highlight the influence of the SoI geometry on the acquisition characteristics and on two real cases of cliffs located in Switzerland, namely one in the Ticino canton and one in the Vaud canton.

Very High Resolution Synthetic Aperture Radar Systems and Imaging: A Review

Very High Resolution Synthetic Aperture Radar Systems and Imaging: A Review

DMSC-GAN: A c-GAN-Based Framework for Super-Resolution Reconstruction of SAR Images

Synthetic Aperture Radar (SAR) imagery is significant in remote sensing, but the limited spatial resolution results in restricted detail and clarity. Current super-resolution methods confront challenges such as complex network structure, insufficient sensing capability, and difficulty extracting features with local and global dependencies. To address these challenges, DMSC-GAN, a SAR image super-resolution technique based on the c-GAN framework, is introduced in this study. The design objective of DMSC-GAN is to enhance the flexibility and controllability of the model by utilizing conditional inputs to modulate the generated image features. The method uses an encoder–decoder structure to construct a generator and introduces a feature extraction module that combines convolutional operations with Deformable Multi-Head Self-Attention (DMSA). This module can efficiently capture the features of objects of various shapes and extract important background information needed to recover complex image textures. In addition, a multi-scale feature extraction pyramid layer helps to capture image details at different scales. DMSC-GAN combines perceptual loss and feature matching loss and, with the enhanced dual-scale discriminator, successfully extracts features from SAR images for high-quality super-resolution reconstruction. Extensive experiments confirm the excellent performance of DMSC-GAN, which significantly improves the spatial resolution and visual quality of SAR images. This framework demonstrates strong capabilities and potential in advancing super-resolution techniques for SAR images.

A Survey on Techniques Used for De-Speckling of SAR Images

Because of the capacity to work at any climate circumstance and infiltration ability through the clouds and soil, SAR (Synthetic Aperture Radar) imagery has been very demanding.SAR imagery system captures images from any satellite or moving object such as aircraft etc. As per the height of a RADAR system increases the resolution of SAR images is also increased. Due to high-resolution images and multiplicative noise known as speckle, it is not easy to analyze these images. So, it has become very important to remove speckle from these images.There are great numbers of de-speckling techniques have been proposed in past. It is very difficult to identify a suitable technique for a de-speckling task. In this article, authors have proposed a literature survey of some techniques.Authors compared best techniques based on their merits and the different parameters used so far for the observation of SAR images. In recent years, the popularity of deep learning has grown tremendously. For de-speckling of SAR images, various improved CNNs and deep learning based approaches have been proposed.

The Effect of SAR resolution and land-cover type on the distributed scatterer density in times-series of InSAR data

ABSTRACT Distributed scatterer interferometry (DS-InSAR) is an important time-series technique of surface deformation monitoring. The accuracy of the results highly depends on the density of DS, which is mainly influenced by synthetic aperture radar (SAR) image resolution and land cover. In this work, firstly, we use split spectrum technique to enable construction of SAR images with different bandwidth (i.e. different resolution). With the land-cover map as classification basis and mean coherence as DS selection index, we quantitatively analyse the behaviour of DS pixel density on different resolution SAR images and three land-cover types. We find that the DS density increases linearly with the increase of SAR system bandwidth, but the linear coefficient decreases with the increase of mean coherence. Secondly, we extend the coherence framework proposed by Zebker in 1992 to establish a model of relationship between mean coherence and bandwidth. The DS density by three land cover and different bandwidth SAR images was predicted by this model. When the mean coherence threshold is set to 0.4, the prediction model can agree with empirical results within 50% difference, and rather closer on impervious surface.

Co-seismic landslide detection after M 7.4 earthquake on June 23, 2020, in Oaxaca, Mexico, based on rapid mapping method using high and medium resolution synthetic aperture radar (SAR) images

Co-seismic landslide detection after M 7.4 earthquake on June 23, 2020, in Oaxaca, Mexico, based on rapid mapping method using high and medium resolution synthetic aperture radar (SAR) images

Moving target inverse synthetic aperture radar image resolution enhancement based on two‐dimensional block sparse signal reconstruction

To achieve the high-resolution inverse synthetic aperture radar (ISAR) imaging of moving targets, the range Doppler method and compressed sensing technique can be used. However, those methods are generally faced with the problem of migration through range cells and basis mismatch problems, and the imaging results can be improved. To solve these problems and improve the image quality, the problem of ISAR imaging is termed as a block-sparse signal recovery problem by utilizing the block-sparse structure of the ISAR images. A localized low-rank promoting (LLP) method is introduced and extended to the complex case for the recovery of range compressed block-sparse signals. The sparse recovery problem is solved by minimizing another function, which is the surrogate function that can be solved more effectively. Based on the LLP method, the coefficients of the range compressed echo signal are reconstructed and some 2 × 2 matrices can be obtained. And then the log-determinant function is introduced to find the low rankness solutions for these matrices. Then the LLP method is also used in the cross-range domain to reconstruct the ISAR image. Experimental results show that the proposed method can recover better focused and higher quality ISAR images compared with the traditional methods.

Contourlet-CNN for SAR Image Despeckling

A multiscale and multidirectional network named the Contourlet convolutional neural network (CCNN) is proposed for synthetic aperture radar (SAR) image despeckling. SAR image resolution is not higher than that of optical images. If the network depth is increased blindly, the SAR image detail information flow will become quite weak, resulting in severe vanishing/exploding gradients. In this paper, a multiscale and multidirectional convolutional neural network is constructed, in which a single-stream structure of convolutional layers is replaced with a multiple-stream structure to extract image features with multidirectional and multiscale properties, thus significantly improving the despeckling performance. With the help of the Contourlet, the CCNN is designed with multiple independent subnetworks to respectively capture abstract features of an image in a certain frequency and direction band. The CCNN can increase the number of convolutional layers by increasing the number of subnetworks, which makes the CCNN not only have enough convolutional layers to capture the SAR image features, but also overcome the problem of vanishing/exploding gradients caused by deepening the networks. Extensive quantitative and qualitative evaluations of synthetic and real SAR images show the superiority of our proposed method over the state-of-the-art speckle reduction method.

CG-Net: Conditional GIS-Aware Network for Individual Building Segmentation in VHR SAR Images

Object retrieval and reconstruction from very high resolution (VHR) synthetic aperture radar (SAR) images are of great importance for urban SAR applications, yet highly challenging owing to the complexity of SAR data. This paper addresses the issue of individual building segmentation from a single VHR SAR image in large-scale urban areas. To achieve this, we introduce building footprints from GIS data as complementary information and propose a novel conditional GIS-aware network (CG-Net). The proposed model learns multi-level visual features and employs building footprints to normalize the features for predicting building masks in the SAR image. We validate our method using a high resolution spotlight TerraSAR-X image collected over Berlin. Experimental results show that the proposed CG-Net effectively brings improvements with variant backbones. We further compare two representations of building footprints, namely complete building footprints and sensor-visible footprint segments, for our task, and conclude that the use of the former leads to better segmentation results. Moreover, we investigate the impact of inaccurate GIS data on our CG-Net, and this study shows that CG-Net is robust against positioning errors in GIS data. In addition, we propose an approach of ground truth generation of buildings from an accurate digital elevation model (DEM), which can be used to generate large-scale SAR image datasets. The segmentation results can be applied to reconstruct 3D building models at level-of-detail (LoD) 1, which is demonstrated in our experiments.

A Modified Cartesian Factorized Backprojection Algorithm Integrating with Non-Start-Stop Model for High Resolution SAR Imaging

High resolution synthetic aperture radar (SAR) imaging has extensive application value especially in military reconnaissance and disaster monitoring. The motion of the satellite during the transmission and reception of the signal introduces notable errors in the high resolution SAR spotlight mode, which will lead to a defocused SAR image if not handled. To address this problem, an accurate correct echo model based on non-start-stop model is derived to describe the property of the SAR signal in the paper. Then, in the imaging processing, an azimuth-time-varying range frequency modulation rate is used for range compression. The range history and compensation phase are also derived based on the correct echo model. Then, combining the correct echo model and Cartesian factorized backprojection (CFBP) algorithm, a modified CFBP algorithm is proposed for SAR imaging to improve the accuracy and efficiency of processing. Besides, the influence of residual error due to mismatch is analyzed in detail. In the end, the simulation experiment and Gaofen-3 (GF-3) data experiment are carried out to demonstrate the feasibility of the proposed algorithm.

A convex variational method for super resolution of SAR image with speckle noise

Super resolution (SR) is an attractive issue in image processing. In the synthetic aperture radar (SAR) image, speckle noise is a crucial problem that is multiplicative. Therefore, numerous custom SR methods considering additive Gaussian noise cannot respond to this image degradation model. The main contribution of this paper is to propose a novel variational convex optimization model for the single SAR image SR reconstruction with speckle noise that is one of the first works in this field. Employing maximum a posteriori (MAP) estimator and proposing an effective regularization based on combination of sparse representation, total variation (TV) and a novel feature space based soft projection tool to use merits of them is the main idea. To solve the proposed model, the split Bregman algorithm is employed efficiently. Experimental results for the multiple synthetic and realistic SAR images show the effectiveness of proposed method in terms of both fidelity and visual perception.

Spaceborne P-Band SAR Imaging Degradation by Anisotropic Ionospheric Irregularities: A Comprehensive Numerical Study

There has been a burgeoning prospect in developing a spaceborne P-band synthetic aperture radar (SAR) mission for its stronger penetrability through foliage and subsurface than the higher-frequency system. However, the transionospheric signals operating at P-band are more susceptible to scintillation impacts, which may bring about the decorrelation of the signal amplitude, phase, and frequency introduced by ionospheric irregularities. In this article, a comprehensive numerical model of the generalized ambiguity function (GAF) is established to evaluate SAR imaging deterioration. On the one hand, an improved two-frequency and two-position coherence function (TFTPCF) is integrated in the GAF to include the amplitude scintillation derived from the diffraction. On the other hand, the anisotropic characteristics of the irregular structure is introduced into TFTPCF by adopting the Rino’s 2-D spectrum. Furthermore, the ambiguous resolution is redefined for a more strict numeration. Numerical analyses about the ionospheric coherence and an ambiguous resolution are performed to investigate their sensitivity to scintillation parameters. Results show that this model is capable of depicting more comprehensive effects of the anisotropic irregular ionosphere, which may exhibit a rod-like structure, including elongation by anisotropic scale, rotation by geomagnetic heading, and projection by geomagnetic inclination. At last, the signal-level simulations are operated to verify the effectiveness of numerical conclusions, which further confirm that the structural configuration of anisotropic irregularities has a complicated effect on spaceborne P-band SAR image resolution.

AN ENERGY SEGMENTATION METHOD OF HIGH-RESOLUTION SAR IMAGE BASED ON MULTIPLE FEATURES

Abstract. To achieve the optimal image segmentation, an energy segmentation method based on multiple features and blocks for high resolution Synthetic Aperture Radar (SAR) image is proposed in this paper. First of all, a feature vector of pixel is formed with the texture feature extracted by curvelet transform and means function, the boundary feature extracted by curvelet transform and Canny Operator, and the original spectral feature; a feature set is formed by all feature vectors of pixels in the image. The feature vector is considered as segmentation basis, and its domain is partitioned by regular tessellation. On the partitioned image domain, a label variable is assigned to a regular block; each homogeneous region is fitted by one or more regular blocks; Obviously, a label field is formed by all the label variables of regular blocks. The model of label field is built by using energy function of neighborhood relationship. The feature set is considered as a realization of a random filed of multiple features (multiple features field for short). A heterogeneous energy function is used to establish the model of multiple features field. Then the established models of the label field and multiple features field are combined to define global energy function of image segmentation, and non-constrained Gibbs probability distribution is used to describe the global energy function to build the energy segmentation model based on multiple features. Further, a RJMCMC algorithm is designed to simulate from the model to segment SAR image. To verify the feasibility and superiority of the proposed approach, real SAR images are tested.

基于区域性IHS_NSST的国产高分合成孔径雷达和光学图像融合

基于区域性IHS_NSST的国产高分合成孔径雷达和光学图像融合

Aircraft Detection for HR SAR Images in Non‐homogeneous Background Using GGMD‐Based Modeling

In the problem of aircraft detection for High resolution (HR) Synthetic aperture radar (SAR) images, the background areas commonly contain multiple land cover types, such as runways and grassland. The conventional Constant false alarm rate (CFAR) detection in these non-homogeneous backgrounds with homogeneous assumption leads to unreliable detection results. This paper constructs a one-stage detection method based on the Generalized gamma mixture distribution (GGMD), which is regarded as a competitive and applicable model for combining the advantages of the Generalized gamma distribution (GGD) and the Finite mixture model (FMM). In order to evaluate the availability of the proposed algorithm, HR SAR images for aircraft detection from different product types and with various resolutions are examined. Compared with the CFAR algorithms based on the Gamma distribution, the GGD, and the gamma mixture distribution, the proposed algorithm demonstrates its availability and effectiveness for aircraft detection in HR SAR images in non-homogeneous background.

An adaptive machine learning approach to improve automatic iceberg detection from SAR images

Iceberg distribution, dispersion and melting patterns are fundamental aspects in the balance of heat and freshwater in the Southern Ocean; yet these features are not fully understood. This lack of understanding is, in part, due to the difficulties in accurately identifying icebergs in different environmental conditions. To improve the understanding, reliable iceberg detection tools are necessary to achieve a detailed picture of iceberg drift and disintegration patterns, an thus to gain further information on the freshwater input into the Southern Ocean. Here, we present an accurate automatic large-scale iceberg detection method using an alternative machine learning architecture applied to high resolution Synthetic Aperture Radar (SAR) images. Our method is based on the concept of adaptability and focuses on improving the performance of identifying icebergs in ambiguous environmental contexts with wide radiometric, textural, size and shape variability. The fundamentals of the method are centred on superpixel segmentation, ensemble learning and incremental learning. The method is applied to a dataset containing 586 ENVISAT Advanced SAR images acquired during 2003–2005 (Weddell Sea region) and to the Radarsat-1 Antarctic Mapping Project (RAMP) mosaic, covering the Antarctic wide near-coastal zone. These images cover scenes under heterogenous backscattering signatures for all seasons with variable meteorological, oceanographic and acquisition parameters (e.g. band, polarization). Our method is highly adaptable to distinguish icebergs from ambiguous objects hidden in the images. The average false positive rate and miss rate are 2.3 ± 0.4% and 3.3 ± 0.4%, respectively. Overall, 9512 icebergs with sizes varying from 0.1 to 4567.82 km2 are detected with average classification accuracy of 97.5 ± 0.6%. The results confirm that the method presented here is robust for widespread iceberg detection in the Antarctic seas.

Impact of Image Artifact and Solution to the Image Quality Issues in Real Time SAR Images

Basically, the Synthetic Aperture Radar (SAR) images are often degraded due to three factors namely noise, blur and artifact. The noise is the undesirable fluctuation in a random portion of the image and is often detracts from the image. The blur will reduce the object visibility. According to the recent literatures the most dangerous effect which appear in real time images are artifacts. The shadowing effect is the best example to depict the image artifact. The presence of shadows mostly affects the vital information of an image. In the shadowing effect, the portion of the object is totally obscured or hidden from the image. In this paper, we focus the impact of image artifact such as shadow in real time images and we focus how to detect the shadowing effect. Further, this paper is devoted to removal of shadows from very high resolution (VHR) SAR images and aerial view Images.

Assessing and refining the satellite-derived massive green macro-algal coverage in the Yellow Sea with high resolution images

During over the past 10 years, the massive green macro-algal bloom has regularly occurred in the Yellow Sea, the spatial coverage of which is mainly derived by the remote sensing community from satellite images with moderate/low resolution (30-m–1000-m), such as the 250-m-resolution MODIS (Moderate Resolution Imaging Spectroradiometer). In this paper, the MODIS estimates are compared for the first time with the concurrent high resolution (3-m) airborne Synthetic Aperture Radar (SAR) data. We find that the MODIS results are overestimated by more than a factor of 3 when each algae pixel is assumed to be pure (i.e. 100% algae cover), whereas the overestimation is significantly reduced to 1.14 when the pure pixel assumption is abandoned and the genuine (fractional) algae coverage is derived with the linear pixel un-mixing method. These results, together with the re-sampling processing of the high resolution images, indicate that the mixed pixel effect, that is inherent with images with moderate and low resolutions, is the key factor for the satellite extraction of the macro-algae coverage, and these findings are further confirmed by the satellite data with different resolutions. Besides, significant correlations (R2 > 0.9) are found between the macro-algae coverage from 3-m resolution SAR images and those from concurrent satellite images with various resolutions (30-m–1000-m) under the pure pixel assumption, which provides an alternative statistics-based method (in addition to the linear pixel un-mixing) for the accurate macro-algae coverage extraction from satellite images with coarse resolution (e.g. HJ-1 CCD, AQUA MODIS, COMS GOCI). This new method is independently validated with high resolution optical images, and applied to derive the annual maxima of the massive green macro-algal bloom areas (fractional coverage) in the Yellow Sea from 2007 to 2016, which ranges from 45.6 to 732.9-km2 with an average of 247.9 ± 199.3-km2.

Self-paced stacked denoising autoencoders based on differential evolution for change detection

Due to the existence of speckle noise in synthetic aperture radar images, the traditional unsupervised change detection methods do not need any prior information whereas cannot preserve details well. In order to improve change detection performance, change detection methods exploiting supervised classifier have been investigated recently. These methods require reliable labeled samples to train a robust classifier and these samples are always unavailable for image change detection. In this paper, we put forward a novel self-paced stacked denoising autoencoders model to address this issue. In the proposed model, stacked denoising autoencoders are adopted as the supervised classifier, and then self-paced learning is employed to improve it. During iterations, each training sample is associated with a weight and stacked denoising autoencoders are implemented to learn these weighted samples. Furthermore, in the original self-paced learning, it is difficult to determine the pace parameter for acquiring the desired classification performance. Therefore differential evolution is employed to acquire an appropriate pace parameter sequence. Experiments on five real synthetic aperture radar image datasets demonstrate the feasibility and availability of the proposed model. Compared with several other change detection methods, the proposed model is more robust to the speckle noise and can achieve better performance on high resolution synthetic aperture radar images.

Discrimination of Ship’s Bow in SAR Image

Discrimination of ship’s bow is a great application for marine surveillance in synthetic aperture radar (SAR) image. The real ship contour is approximately elliptical and the bow’s curvature is significantly larger than the stern’s curvature. The increasing resolution of SAR image makes ship target appear to be more structured and shaped which makes discrimination of ship’s bow possibly in SAR image. In this paper, we advance a method for discrimination of ship’s bow which includes two important parts. The first part, we use the gradient vector flow (GVF) snake model with elliptical constraint (EC) to get a more realistic ship contour for curve fitting. The second part, we use the least squares quadratic polynomial curve fitting to fit the two ends of the contour along the ship’s main shaft. The bigger absolute value of second order polynomial fitting coefficients, the more curved the rigid curve. The more curved end is identified as the ship’s bow. Experiments performed on detected small-size ship slices which also called regions of interest (ROI).These ROI slices which come from RADARSAT-2 data and one ROI slice contains only one ship target. Experiment results illustrate the effective performance of proposed method on discrimination for the ship’s bow in SAR image.