High-resolution Synthetic Aperture Radar Data Research Articles

Assessment of the geolocation accuracy of YG-13A high-resolution SAR data

ABSTRACTThe YG-13A mission, launched in late 2015, is the first Chinese synthetic aperture radar (SAR) satellite to achieve sub-meter resolution. In this paper, we briefly introduce this milestone satellite and describe a method for improving YG-13A’s absolute positioning accuracy. We evaluated the plane and three-dimensional absolute positioning accuracy over five test sites. The results indicate that high-accuracy absolute positioning can now be achieved with a horizontal accuracy of 3 m or better for strip-map data and 2 m or better for sliding spot data, without regard to the elevation error. In addition, the three-dimensional absolute positioning accuracy in plane and elevation is within 3 m and 2 m, respectively, which satisfies the requirements for high-precision three-dimensional control points.

Sub-Nyquist SAR via Fourier Domain Range-Doppler Processing

Conventional Synthetic Aperture Radar (SAR) systems are limited in their ability to satisfy the increasing requirement for improved spatial resolution and wider coverage. The demand for high resolution requires high sampling rates, while coverage is limited by the pulse repetition frequency. Consequently, sampling rate reduction is of high practical value in SAR imaging. In this paper, we introduce a new algorithm, equivalent to the well-known Range-Doppler method, to process SAR data using the Fourier series coefficients of the raw signals. We then demonstrate how to exploit the algorithm features to reduce sampling rate in both range and azimuth axes and process the signals at sub-Nyquist rates, by using compressed sensing (CS) tools. In particular, we demonstrate recovery of an image using only a portion of the received signal's bandwidth and also while dropping a large percentage of the transmitted pulses. The complementary pulses may be used to capture other scenes within the same coherent processing interval. In addition, we propose exploiting the ability to reconstruct the image from narrow bands in order to dynamically adapt the transmitted waveform energy to vacant spectral bands, paving the way to cognitive SAR. The proposed recovery algorithms form a new CS-SAR imaging method that can be applied to high-resolution SAR data acquired at sub-Nyquist rates in range and azimuth. The performance of our method is assessed using simulated and real data sets. Finally, our approach is implemented in hardware using a previously suggested Xampling radar prototype.

Fast and Efficient Correction of Ground Moving Targets in a Synthetic Aperture Radar, Single-Look Complex Image

Ground moving targets distort normally-focused synthetic aperture radar (SAR) images. Since most high-resolution SAR data providers only offer single-look complex (SLC) data rather than raw signals to general users, they need to apply a simple and efficient residual SAR focusing to SLC data containing moving targets. This paper presents an efficient and effective SAR residual focusing method that is practically applicable to SLC data. The residual Doppler spectrum of the moving target is derived from a general SAR configuration and normal SAR focusing. The processing steps are simple and straightforward, with a limited size of the processing window, e.g., 64 × 64. Application results using simulation data and actual TerraSAR-X SLC data with a speed-controlled vehicle demonstrate the effectiveness of the method, which particularly improves the −3 dB width, integrated sidelobe ratio, and symmetry of the reconstructed signals. In particular, the azimuthal symmetry becomes seriously distorted when the target speed is higher than 8 m/s (or 28.8 km/h), and the symmetry is well recovered by the proposed method.

Characterizing post-construction settlement of the Masjed-Soleyman embankment dam, Southwest Iran, using TerraSAR-X SpotLight radar imagery

Geodetic monitoring of infrastructures is one of the key tasks in surveying and engineering geology. Systematic monitoring and assessment of the exterior deformation of embankment dams for safety analysis are often difficult when using classical surveying techniques due to time-consuming surveying procedures and high labour costs. Modern remote sensing techniques play an important role in efficiently assessing deformation: changes in the geometry, position and orientation of dams. In this study, we present the feasibility of effective post-construction deformation monitoring of the Masjed-Soleyman dam in Iran using high-resolution (∼1m) synthetic aperture radar (SAR) imagery in SpotLight (SL) mode from the X-band TerraSAR-X (TSX) satellite. This dam has been monitored over the last 15years using classical surveying techniques, which provide horizontal and vertical deformation measurements of the structure. We show that high-resolution X-band SAR data provide a much more detailed identification of dam deformation in the crest and downstream that is not possible to infer from classical surveying techniques with few sparse geodetic monuments. High-resolution TSX data reveal that the dam is currently subject to two different deformation regimes: one is related to the crest and its adjacent area downstream, with a maximum rate of deformation of approximately 13cm/yr in the radar line-of-sight (LOS). The other is related to the lower part of the downstream, with a maximum LOS velocity of 7cm/yr. The effect of this centimetre displacement rate has been shown through several damage features on the dam body, including minor to large dislocation cracks on the crest and a significant deformation zone on the downstream slope.

Bridge Displacements Monitoring Using Space-Borne X-Band SAR Interferometry

The development of interferometric methodologies for deformation monitoring that are able to deal with long time series of synthetic aperture radar (SAR) images made the detection of seasonal effects possible by decomposing the differential SAR phase. In the case of monitoring of man-made structures, particularly bridges, the use of high-resolution X-band SAR data allows the determination of three major components with significant influence on the SAR phase: the linear deformation trend, the height of structures over terrain, and the thermal expansion. In the case of stable metallic or (reinforced) concrete structures, this last effect can reach a magnitude comparable to or even exceeding the other phase components. In this review, we present two case studies that confirm the feasibility of InSAR techniques for bridge deformation monitoring and our original approach to refine the thermal expansion component.

New Hierarchical Saliency Filtering for Fast Ship Detection in High-Resolution SAR Images

The increasing resolution of synthetic aperture radar (SAR) images makes ship targets appear to be more structured and shaped and nevertheless contain many weak echoes in their resolution cells, which brings great challenges for accurate scene understanding. In this paper, inspired by the multilayer selective cognition property of the human visual system, we advance a new hierarchical saliency filtering method for fast and accurate ship detection in high-resolution SAR images. The saliency of targets is first explored to develop a random-forest-based hierarchical sparse model (HSM) for the selection of candidate target regions. Then, a dynamic constant-false-alarm-rate-based contour saliency model (CSM) is proposed to gradually filter out the false alarms from candidate regions and extract the target outlines for accurate detection. Because of a rapid capture of regions of interest in the HSM and dynamic false alarm removal in the CSM, our method can make efficient ship detection in high-resolution SAR images possible by working in a coarse-to-fine manner. Finally, the proposed ship detector is tested on high-resolution SAR data collected from TerraSAR and RADARSAT satellites, showing significant agreement with the ground truth. It is also compared with other classical ship detectors, in terms of both speed and accuracy, and shows superior performance, particularly in complex scenes.

Investigation on Mining Subsidence Based on Multi-Temporal InSAR and Time-Series Analysis of the Small Baseline Subset—Case Study of Working Faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield, China

High-intensity coal mining (large mining height, shallow mining depth, and rapid advancing) frequently causes large-scale ground damage within a short period of time. Understanding mining subsidence under high-intensity mining can provide a basis for mining-induced damage assessment, land remediation in a subsidence area, and ecological reconstruction in vulnerable ecological regions in Western China. In this study, the mining subsidence status of Shendong Coalfield was investigated and analyzed using two-pass differential interferometric synthetic aperture radar (DInSAR) technology based on high-resolution synthetic aperture radar data (RADARSAT-2 precise orbit, multilook fine, 5 m) collected from 20 January 2012 to June 2013. Surface damages in Shendong Coalfield over a period of 504 days under open-pit mining and underground mining were observed. Ground deformation of the high-intensity mining working faces 22201-1/2 in Bu’ertai Mine, Shendong Coalfield was monitored using small baseline subset (SBAS) InSAR technology. (1) DInSAR detected and located 85 ground deformation areas (including ground deformations associated with past-mining activity). The extent of subsidence in Shendong Coalfield presented a progressive increase at an average monthly rate of 13.09 km2 from the initial 54.98 km2 to 225.20 km2, approximately, which accounted for 7% of the total area of Shendong Coalfield; (2) SBAS-InSAR reported that the maximum cumulative subsidence area reached 5.58 km2 above the working faces 22201-1/2. The advance speed of ground destruction (7.9 m/day) was nearly equal to that of underground mining (8.1 m/day).

Using high resolution SAR data to monitor deformations at a very detailed scale

The detection and monitoring of deformation phenomena caused by natural and human-induced processes are increasingly performed through the analyses of Synthetic Aperture Radar (SAR) satellite data.The use of Differential Interferometric SAR (DInSAR) techniques provides information about the temporal evolution of different events, such as earthquakes, volcano eruptions, subsidence and landslides. The recent availability of high resolution data, from COSMO-SkyMed and TerraSAR-X missions, led to important advances on the use of DInSAR techniques to monitor different types of phenomena, working on very detailed scales. The present work is aimed to test the response of the processing of COSMO-SkyMed images, with different DInSAR techniques, to analyse the fields of displacements induced by a sinkhole affecting the Jordanian coast of the Dead Sea and a recent building in Astana, Kazakhstan.

Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data

Vegetation is an integral component of wetland ecosystems. Mapping distribution, quality and quantity of wetland vegetation is important for wetland protection, management and restoration. This study evaluated the performance of object-based and pixel-based Random Forest (RF) algorithms for mapping wetland vegetation using a new Chinese high spatial resolution Gaofen-1 (GF-1) satellite image, L-band PALSAR and C-band Radarsat-2 data. This research utilized the wavelet-principal component analysis (PCA) image fusion technique to integrate multispectral GF-1 and synthetic aperture radar (SAR) images. Comparison of six classification scenarios indicates that the use of additional multi-source datasets achieved higher classification accuracy. The specific conclusions of this study include the followings:(1) the classification of GF-1, Radarsat-2 and PALSAR images found statistically significant difference between pixel-based and object-based methods; (2) object-based and pixel-based RF classifications both achieved greater 80% overall accuracy for both GF-1 and GF-1 fused with SAR images; (3) object-based classifications improved overall accuracy between 3%-10% in all scenarios when compared to pixel-based classifications; (4) object-based classifications produced by the integration of GF-1, Radarsat-2 and PALSAR images outperformed any of the lone datasets, and achieved 89.64% overall accuracy.

Polarimetric–Anisotropic Decomposition and Anisotropic Entropies of High-Resolution SAR Images

In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

UPGRADE OF FOSS DATE PLUG-IN: IMPLEMENTATION OF A NEW RADARGRAMMETRIC DSM GENERATION CAPABILITY

Abstract. Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome “La Sapienza”, and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery.

UPGRADE OF FOSS DATE PLUG-IN: IMPLEMENTATION OF A NEW RADARGRAMMETRIC DSM GENERATION CAPABILITY

Synthetic Aperture Radar (SAR) satellite systems may give important contribution in terms of Digital Surface Models (DSMs) generation considering their complete independence from logistic constraints on the ground and weather conditions. In recent years, the new availability of very high resolution SAR data (up to 20 cm Ground Sample Distance) gave a new impulse to radargrammetry and allowed new applications and developments. Besides, to date, among the software aimed to radargrammetric applications only few show as free and open source. It is in this context that it has been decided to widen DATE (Digital Automatic Terrain Extractor) plug-in capabilities and additionally include the possibility to use SAR imagery for DSM stereo reconstruction (i.e. radargrammetry), besides to the optical workflow already developed. <br><br> DATE is a Free and Open Source Software (FOSS) developed at the Geodesy and Geomatics Division, University of Rome “La Sapienza”, and conceived as an OSSIM (Open Source Software Image Map) plug-in. It has been developed starting from May 2014 in the framework of 2014 Google Summer of Code, having as early purpose a fully automatic DSMs generation from high resolution optical satellite imagery acquired by the most common sensors. <br><br> Here, the results achieved through this new capability applied to two stacks (one ascending and one descending) of three TerraSAR-X images each, acquired over Trento (Northern Italy) testfield, are presented. Global accuracies achieved are around 6 metres. These first results are promising and further analysis are expected for a more complete assessment of DATE application to SAR imagery.

Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approaches—decision trees (DT) and random forest (RF)—in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 × 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.

Wideband Interference Mitigation in High-Resolution Airborne Synthetic Aperture Radar Data

Radio frequency interference is a major issue for synthetic aperture radar (SAR) imaging. Especially with the presence of wideband interference (WBI), the signal-to-interference ratio (SIR) of the measurements is greatly degraded, thus making it difficult to produce a high-quality SAR image. Compared with narrow-band interference (NBI), WBI occupies a larger bandwidth and is more complicated to deal with. This paper addresses the detection and mitigation of WBI in high-resolution airborne SAR data. First, a WBI-corrupted echo is characterized in the time–frequency representation by utilizing the short-time Fourier transform. In this way, the original range-spectrum WBI mitigation problem can be simplified into a series of instantaneous-spectrum NBI mitigation problems. For each instantaneous spectrum, the existence of interference signal can be identified according to the negentropy-based statistical test. Furthermore, the interference signal is mitigated by notch filtering or eigensubspace filtering. The experimental results of the simulated data, as well as real measured data sets, show that the proposed scheme is effective in suppressing the interference signal and in obtaining a high-quality image.

Unstructured Versus Structured GLRT for Multipolarization SAR Change Detection

Coherent multipolarization synthetic aperture radar (SAR) change detection exploiting data collected from $N$ multiple polarimetric channels is addressed in this letter. The problem is formulated as a binary hypothesis testing problem, and a special block-diagonal structure for the polarimetric covariance matrix is forced to design a detector based on the generalized likelihood ratio test (GLRT) criterion. It is shown that the structured decision rule ensures the constant false alarm rate property with respect to the unknown disturbance covariance. Results on both simulated and real high-resolution SAR data show the effectiveness of the considered decision rule and its superiority against the traditional unstructured GLRT in some scenarios of practical interest.

The Sentinel-1 Mission: New Opportunities for Ice Sheet Observations

The Sentinel satellite constellation series, developed by the European Space Agency, represents the dedicated space component of the European Copernicus program, committed to long-term operational services in a wide range of application domains. Here, we address the potential of the Sentinel-1 mission for mapping and monitoring the surface velocity of glaciers and ice sheets. We present an ice velocity map of Greenland, derived from synthetic aperture radar (SAR) data acquired in winter 2015 by Sentinel-1A, the first satellite of the Copernicus program in orbit. The map is assembled from about 900 SAR scenes acquired in Interferometric Wide swath (IW) mode, applying the offset tracking technique. We discuss special features of IW mode data, describe the procedures for producing ice velocity maps, and assess the uncertainty of the ice motion product. We compare the Sentinel-1 ice motion product with velocity maps derived from high resolution SAR data of the TerraSAR-X mission and from PALSAR data. Beyond supporting operational services, the Sentinel-1 mission offers enhanced capabilities for comprehensive and long-term observation of key climate variables, such as the motion of ice masses.

An overview of satellite synthetic aperture radar remote sensing in archaeology: From site detection to monitoring

Abstract In the last two decades, archaeology has benefited from the development of earth observation (EO) technologies, including optical multispectral, LiDAR and synthetic aperture radar (SAR) remote sensing. The latter is gaining the attention of an expanding community of scientists and archaeologists due to the increasing availability of multi-platform, multi-band, multi-polarization and very high-resolution satellite SAR data. It is increasingly becoming an important tool in archaeology owing to specific characteristic of its operational modalities, e.g. all-weather, penetration, polarization and interferometry. However, compared to other EO technologies, SAR is encountering more difficulties in realizing its full potential for archaeological applications due to the greater complexity of data processing and interpretation tools. In this paper, SAR-based approaches for the reconnaissance of archaeological signs and SAR interferometry for the monitoring of cultural heritage sites are discussed. Ways and means to reduce complexity of data processing and interpretation tools are also explored.

Invariant Rules for Multipolarization SAR Change Detection

This paper deals with coherent (in the sense that both amplitudes and relative phases of the polarimetric returns are used to construct the decision statistic) multipolarization synthetic aperture radar (SAR) change detection assuming the availability of reference and test images collected from $N$ multiple polarimetric channels. At the design stage, the change detection problem is formulated as a binary hypothesis testing problem, and the principle of invariance is used to come up with decision rules sharing the constant false alarm rate property. The maximal invariant statistic and the maximal invariant in the parameter space are obtained. Hence, the optimum invariant test is devised proving that a uniformly most powerful invariant detector does not exist. Based on this, the class of suboptimum invariant receivers, which also includes the generalized likelihood ratio test, is considered. At the analysis stage, the performance of some tests, belonging to the aforementioned class, is assessed and compared with the optimum clairvoyant invariant detector. Finally, detection maps on real high-resolution SAR data are computed showing the effectiveness of the considered invariant decision structures.

Phenological tracking og agricultural feilds investigated by using dual polarimetry tanDEM-X images

Abstract. Remote sensing plays a key role in monitoring and assessing environmental changes. Because of its special imaging characteristics such as high-resolution, capabilities to obtain data in all weather conditions and sensitivity to geometrical and dielectric properties of the features, Synthetic Aperture Radar (SAR) technology has become a powerful technique to detect small scale changes related to earth surface.SAR images contain the information of both phase and intensity in different modes like single, dual and full polarimetric states which are important in order to extract information about various targets. In this study we investigate phenological changes in an agricultural region using high-resolution X-band SAR data. The case study is located in Doroud region of Lorestan province, west of Iran. The purpose is to investigate the ability of copolar and interferometric coherence extracted from TanDEM-X dual polarimetry (HH/VV) in bistatic StripMap mode for tracking the phenological changes of crops during growing season. The data include 11 images acquired between 12.06.2012 and 02.11.2012 and 6 images acquired between 30.05.2013 and 04.08.2013 in the CoSSC format. Results show that copolar coherence is almost able to follow phenological changes but interferometric coherence has a near constant behaviour with fluctuations mainly related to baseline variations.

Brief Communication: Rapid mapping of landslide events: the 3 December 2013 Montescaglioso landslide, Italy

Abstract. We present an approach to measure 3-D surface deformations caused by large, rapid-moving landslides using the amplitude information of high-resolution, X-band synthetic aperture radar (SAR) images. We exploit SAR data captured by the COSMO-SkyMed satellites to measure the deformation produced by the 3 December 2013 Montescaglioso landslide, southern Italy. The deformation produced by the deep-seated landslide exceeded 10 m and caused the disruption of a main road, a few homes and commercial buildings. The results open up the possibility of obtaining 3-D surface deformation maps shortly after the occurrence of large, rapid-moving landslides using high-resolution SAR data.