Resolution Synthetic Aperture Radar Research Articles

Application of Satellite-Based Remote Sensing for the Management of Pavement Infrastructure Assets

The long-term monitoring of transportation infrastructure assets at a lower cost and with short mobilization time is of significant interest to both state and federal transportation agencies in the U.S. Because of the significant improvement in spatial and temporal resolution of synthetic aperture radar (SAR) remote sensing systems and a notable reduction in the cost of data acquisition, SAR has now become a viable method to provide economic and rapid condition assessment of transportation assets. A research study was developed and performed to comprehensively perform the inspection and characterization of a pavement surface based on the amplitude of backscattering of an X-band radar. In situ characterization of the test site was first performed using traditional inertial profilers and aerial photogrammetry with unmanned aerial vehicle (UAV) surveys. The results from these in situ methods were compared with the corrected amplitude of the SAR data, which indicated that the distribution of surface roughness values computed from the inertial profiler, UAV, and SAR exhibited similar probability densities at various segmental lengths considered in this study. This suggested that the problematic areas that are evident during in situ characterization can be delineated and quantified based on the normalized radar cross section of the pavement surface. Overall, the outcome of this research exhibits the potential of SAR for future transportation asset management undertakings, and the systematic framework developed as a part of this research could be of significant interest to engineers and transportation practitioners.

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

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

A Review on Speckle Reduction Techniques in SAR images

Synthetic Aperture Radar (SAR) is satellite imagery that has multiple applications in variegated fields but is often corrupted by single dependent multiplicative speckle noise. Its multiplicative nature decreases scope for image perception, recognition & limits SAR images applications. Thus, increasing the need for effective & astute SAR image despeckling techniques that not only excise speckle noise but also preserve SAR imageries features, details, and resolution quality. This study analyses various research literature & techniques namely, Adaptive Speckle Reduction Filter, Conditional Averaging filter, Speckle Reduction Filter, Anisotropic diffusion, Speckle Reduction Filter and Speckle Reduction Filter from theoretical, quantitative & qualitative aspects using indexes like SSIM, and RMSE to discover the comparatively superior approach.

Observed Surface Wind Field Structure of Severe Tropical Cyclones and Associated Precipitation

Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, this study assessed the surface wind fields from high spatial resolution Synthetic Aperture Radar (SAR) observations, the fifth generation ECMWF reanalysis for the global climate and weather (ERA5) data and the Tropical Cyclone Winds and Inflow Angle Asymmetry (TCIAA) wind model. The results showed that SAR data are sufficient to reveal the surface wind field near a TC center and can accurately describe TC intensity and size under severe TC conditions. Then, a new, improved statistical wind structure model was set up using ERA5 data alone based on the assessment. In addition, the warm sea surface (SST > 26.5 °C) produced stronger TC wind fields and heavier precipitation. When the SST was higher (lower), the heavy rainfall was located on the left (right) side of the TC track and the strong positive correlation between wind speed and precipitation increased as the SST decreased.

Improved Analytical Formula for the SAR Doppler Centroid Estimation Standard Deviation for a Dynamic Sea Surface

The existing formulas for the synthetic aperture radar (SAR) Doppler centroid estimation standard deviation (STD) suffer from various limitations, especially for a dynamic sea surface. In this study, we derive an improved version of these formulas through three steps. First, by considering the ocean wavenumber spectrum information, a new strategy for determining the number of independent samples of the sea wave velocity field is adopted in the new formula. This is contrary to the method used in the existing formulas, where the number of SAR geometric resolution cells is taken as the number of samples assuming that adjacent SAR resolution cells are statistically uncorrelated. Second, the pulse repetition frequency and Doppler bandwidth are decoupled in the new formula, unlike in the existing formulas where they are unchangeably related to each other. Third, the effects of thermal noise and Doppler aliasing are jointly quantified in a mathematically exact manner instead of being treated separately, as in the existing formulas. Comprehensive SAR raw data simulations for the ocean surface show that the new formula has a better performance in predicting the Doppler centroid estimation STD than the existing formulas.

Motion Error Estimation and Compensation of Airborne Array Flexible SAR Based on Multi-Channel Interferometric Phase

Airborne array synthetic aperture radar (SAR) has made a significant breakthrough in the three-dimensional resolution of traditional SAR. In the airborne array SAR 3D imaging technology, the baseline length is the main factor restricting the resolution. Airborne array flexible SAR can increase the baseline length to improve the resolution and interference performance by mounting antennae on the wing. The existing research lacks results obtained using flexible actual data processing and specific motion compensation methods. Thus, this paper proposes a motion error estimation and compensation method for an airborne array flexible SAR based on a multi-channel interferometric phase. Firstly, a flexible channel motion compensation model is established based on the multi-channel interference phase of airborne array flexible SAR. Then, based on the rigid multi-channel data, combined with the ground control points, the least square method, and the global optimal search algorithm, the accurate rigid baseline length and the central incidence angle are obtained. Finally, according to the multi-channel interference phase inversion of the flexible motion error and combined with the motion compensation model, the flexible data are compensated in the time domain. The actual results indicate that, compared with traditional motion compensation methods, our method can obtain accurate flexible compensation data. This study improves the interference performance of multi-channel data of airborne array flexible SAR and lays a solid foundation for the high-precision 3D reconstruction of airborne array flexible SAR.

Validation of the Effective Resolution of SMAP Enhanced Resolution Backscatter Products

NASA's Soil Moisture Active Passive (SMAP) mission originally included both passive and active L-band measurement capability. It was the first satellite instrument to provide global L-band radar observations of normalized radar cross section ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sigma ^{o}$</tex-math></inline-formula> ) at multiple resolutions. The SMAP radar collected high resolution ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 1–3 km) synthetic aperture radar (SAR) measurements over most of the Earth's land mass. It simultaneously collected low resolution 6×30 km “slice” and full-footprint 29×35 km measurements. The SMAP radar operated for 83 days from day of the year 103 to 186 in 2015, before the transmitter failed. The SMAP radar was designed to make vegetation roughness measurements in support of the SMAP primary mission to measure soil moisture, but the radar data is useful for a variety of applications, particularly in the polar regions. Unfortunately, limitations in the data download volume precluded downlink of high resolution data over Antarctica, sea ice in the polar regions, and various islands. Nonetheless, low resolution slice and footprint data were collected and downlinked over these areas. To better exploit this low resolution data, this paper employs image reconstruction techniques to create twice-daily enhanced resolution SMAP radar images from the slice and footprint measurements. To validate the resolution enhancement, the enhanced resolution data are compared to SAR results over Greenland and South America. The new data set is provided to the science community to support cryosphere and climate studies.

A New Concept of Contiguous-Swath SAR Imaging with High Resolution: Strip-Spot SAR

The study offers a Synthetic Aperture Radar (SAR) imaging concept called Strip-Spot SAR, which uses linear Multiple-Input Multiple-Output (MIMO) radar arrays, which are becoming increasingly attractive for short-range sensing in a variety of growing application sectors. The concept specifically employs Digital Beam-Forming (DBF) techniques, which are enabled in such systems to give contiguous azimuth imaging, as in Stripmap SAR, but with the fine spatial resolution of a Spotlight SAR. Its fundamental concepts are analytically derived and experimentally validated under laboratory conditions using calibrated and real targets. Finally, the performance and limitations of the concept are investigated.

Two-dimensional bidirectional principal component collaborative projection feature for SAR vehicle target recognition

With the continuous improvement in the resolution of synthetic aperture radar (SAR), there are many problems in the interpretation of high-resolution SAR images, such as a large amount of data and low efficiency of target recognition. In this paper, a novel SAR target recognition method based on a two-dimensional bidirectional principal component cooperative representation projection feature ((2D)2PCA-CRP) is proposed. First, (2D)2PCA is used to project the image into the low-dimensional feature space, and the redundant information in the high-resolution SAR image is filtered while considering the spatial structure. Then, the spatial global separability feature and local structure feature of the target in the high-resolution SAR image are extracted by CRP to form the (2D)2PCA-CRP feature. Finally, based on this feature, the nearest neighbour classifier is used to complete the target recognition experiments on MSTAR data. The experiments of this study are divided into three parts using standard operation condition (SOC) samples, type change samples and radar incidence angle change data. The experimental results show that the proposed feature achieves better target recognition performance in high-resolution SAR images.

Novel closed-loop deployable mechanisms and integrated support trusses for planar antennas of synthetic aperture radar

Synthetic aperture radar (SAR) is indispensable electronic equipment in modern life, which has been widely used in the field of geological exploration, disaster monitoring, and environmental observation. With the increasing complexity of earth observation tasks, a higher receiving resolution of SAR is required, which needs the area of the radar antenna to be increased. However, there is a contradiction between the small launch space and the large working area. The deployable antenna mechanism is expected to meet the challenge. In this paper, a graphical type synthesis method is proposed to carry out the mechanism design of deployable mechanisms. According to the structural analysis and design requirements, the articulated form is adopted to carry sub-planes of planar antennas and provide one kinematic chain of the deployable mechanism. Then, different designed kinematic chains are synthesized to construct novel closed-loop deployable mechanisms. Mobility verification of a selected closed-loop deployable mechanism is carried out by screw theory to demonstrate the validity of type synthesis results. By sharing the provided kinematic chain and symmetrical design of two deployable mechanisms, deployable units with the driving unit are proposed as basic modules of support trusses for planar antennas. As a result, three novel deployable support trusses integrated by a series of deployable units and assembly mechanisms are proposed as the platforms for planar antennas. The deployable mechanisms and integrated support trusses proposed in this paper provide candidates for planar antennas of SAR.

Daily High-Resolution Land Surface Freeze/Thaw Detection Using Sentinel-1 and AMSR2 Data

High-resolution surface freeze/thaw (F/T) information is valuable for hydrological, frost creep and gelifluction/solifluction, and climate prediction studies. Currently, large-scale, high-resolution F/T detection is restricted by low spatial resolution of passive microwave remote sensing sensors or low temporal resolution of synthetic aperture radar (SAR) data. In this study, we propose a new method for detecting daily land surface F/T state at 1 km spatial resolution by combining the Sentinel-1 radar and the Advanced Microwave Scanning Radiometer 2 (AMSR2) with leaf area index (LAI) data. A non-linear relationship is established between the 1 km F/T index from Sentinel-1 with 1 km F/T index from AMSR2 (FTI) and 1 km LAI data. The 1 km FTI is a disaggregation of the 25 km FTI obtained from AMSR2. This non-linear relationship is then applied to daily 1 km FTI and LAI data to predict the 1 km daily F/T index, based on which the F/T status is detected with grid-cell-based F/T thresholds. The overall accuracy of this daily 1 km F/T is more than 88.1% when evaluated with the in situ 5 cm soil temperature over China and Canada. This study is valuable for detecting daily, high-resolution F/T status and is helpful for studies related to disaster and climate prediction.

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.

Time-Domain Autofocus for Ultrahigh Resolution SAR Based on Azimuth Scaling Transformation

For ultra-high resolution synthetic aperture radar (SAR), azimuth spectrum aliasing limits the application of frequency-domain autofocus algorithms. Therefore, time-domain autofocus algorithms are often used for ultra-high resolution SAR imaging. However, the azimuth deramping operation in current time-domain autofocus algorithms may introduce an additional azimuth-dependent phase. This phase can be regarded as a part of the phase error, which significantly reduces the estimation accuracy of the phase error. To address this issue, this article proposes a new time-domain autofocus algorithm based on azimuth scaling transformation for ultra-high resolution SAR. In this algorithm, we first adopt the azimuth scaling operation to avoid the azimuth-dependent phase so that the estimation accuracy of error can be greatly improved. Then, for the azimuth-dependent shifts caused by the azimuth scaling operation, we adopt the alignment processing to remove them in azimuth-time domain. Finally, we can estimate the error accurately from the aligned signal. The simulation and measured data were processed to verify the effectiveness of the algorithm.

Merchant ship classification in high-resolution SAR image based on joint superstructure scattering features

ABSTRACT Ship classification has long been a challenging task in synthetic aperture radar (SAR) applications. With the improvement of SAR resolution, it has gradually become possible based on high-resolution SAR images. This letter presents a joint feature representation method for characterizing the superstructure scattering features of merchant ships, and then a hierarchical scheme for classifying merchant ships in high-resolution SAR images based on integrated macro and detailed superstructure features is proposed. Comprehensive experiment on three types of high-resolution SAR samples demonstrates that the proposed features and method have the ability to improve the performance of merchant ship classification in 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

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

A Novel Motion Compensation Scheme for Airborne Very High Resolution SAR

Due to the atmospheric turbulence, the motion trajectory of airborne very high resolution (VHR) synthetic aperture radars (SARs) is inevitably affected, which introduces range-variant range cell migration (RCM) and aperture-dependent azimuth phase error (APE). Both types of errors consequently result in defocused images, as residual range- and aperture-dependent motion errors are significant in VHR-SAR images. Nevertheless, little work has been devoted to the range-variant RCM auto-correction and aperture-dependent APE auto-correction. In this paper, a precise motion compensation (MoCo) scheme for airborne VHR-SAR is studied. In the proposed scheme, the motion error is obtained from inertial measurement unit and SAR data, and compensated for with respect to both range and aperture. The proposed MoCo scheme compensates for the motion error without space-invariant approximation. Simulations and experimental data from an airborne 3.6 GHz bandwidth SAR are employed to demonstrate the validity and effectiveness of the proposed MoCo scheme.

Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.

Mapping typical salt-marsh species in the Yellow River Delta wetland supported by temporal-spatial-spectral multidimensional features

The native salt marsh plants of the Yellow River Delta wetland such as Suaeda salsa and Phragmites australis, providing significant habitats for rare waterfowl, are the key to conserve biodiversity and enhance habitats of this critical wetland. These plants are undergoing severe degradation due to rapid invasion of Spartina alterniflora, which has been a major growing threat to the livelihood of waterfowl and the sustainability of the Yellow River Delta wetland. Monitoring the spatial pattern of salt marsh species is fundamental to the conservation and restoration of the ecological functions in the Yellow River Delta wetland. The development of remote sensing technologies is making a leap forward, particularly the high resolution synthetic aperture radar (SAR), which holds the potential to map heterogeneous wetland regardless of weather. In this study, we developed an innovative framework to map the distribution of salt marsh species with the integration of optical (Sentinel-2) and SAR (Sentinel-1) images. Within this framework, a comprehensive set of features including spectral, spatial and temporal features were considered, and the best feature combination was selected and applied in a random forest classification model to obtain the final map. The results show that the temporal-spectral features combined with the spatial-temporal features of the SAR data can effectively improve the separability of Suaeda salsa, Phragmites australis, and Spartina alterniflora. Compared with using optical or SAR data alone, the combination of optical and SAR data improved the kappa coefficient and the overall classification accuracy by 0.10–0.19 and 6.04–11.61%, respectively. The spatial distribution of the two main native plants and the invasive plant can facilitate ecological restoration of the Yellow River Delta wetland. The framework developed by this study can be efficiently replicated and transferred by similar studies. Our approach lays a solid foundation for intelligent monitoring and management of coastal wetland.

Passive Synthetic Aperture Radar Imaging Using Kalman Reflection Coefficients Estimation Algorithm with DVB-T Signal

In this paper, a new method is presented for image formation in a Multistatic Passive Synthetic Aperture Radar (SAR), whose receiver is moving and transmitters are stationary in a multistatic structure, and the signal used to create the image is DVB-T. The proposed method uses multiple transmitters of opportunity to improve the range resolution of a Passive SAR. First, a linear observation model is developed for multistatic structure of the Passive SAR system. Then, the image of the underlying scene is obtained through a new Kalman-based estimation algorithm. In this algorithm, range migration error is completely corrected, and the use of Kalman recursive filter reduces the computational complexity of the reflection coefficients estimation algorithm. Experimental results indicate that the proposed algorithm is effective.

Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery

High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were examined. Using the backscattering values of the S1 imagery, the SVM classifier achieved a mean overall accuracy (OA) of 63.14%, and a Kappa coefficient (Kappa) of 0.50. Using the SVM classifier with a Lee filter with a window size of 5×5 (Lee5) for speckle reduction, mean values of 73.86% and 0.64 for OA and Kappa were achieved, respectively. An additional increase in the LCC was obtained with texture features calculated from a grey-level co-occurrence matrix (GLCM). The highest classification accuracy obtained for the extracted GLCM texture features using the SVM classifier, and Lee5 filter was 78.32% and 0.69 for the mean OA and Kappa values, respectively. This study improved LCC with an evaluation of various radiometric and texture features and confirmed the ability to apply an SVM classifier. For the supervised classification, the SVM method outperformed the RF and XGB methods, although the highest computational time was needed for the SVM, whereas XGB performed the fastest. These results suggest pre-processing steps of the SAR imagery for green infrastructure mapping in urban areas. Future research should address the use of multitemporal SAR data along with the pre-processing steps and ML algorithms described in this research.