Synthetic Aperture Radar Scenes Research Articles

Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour

A novel method based on the curvelet transform and active contour method to automatically detect the ice edge in Synthetic Aperture Radar (SAR) imagery is proposed. The method utilizes the location of high curvelet coefficients to determine regions in the image likely to contain the ice edge. Using an ice edge from passive microwave sea ice concentration for initialization, these regions are then joined using the active contour method to obtain the final ice edge. The method is evaluated on four dual polarization SAR scenes of the Labrador sea. Through comparison of the ice edge with that from image analysis charts, it is demonstrated that the proposed method can detect the ice edge effectively in SAR images. This is particularly relevant when the marginal ice zone is diffuse or the ice is thin, and using the definition of ice edge from the passive microwave ice concentration would underestimate the ice edge location. It is expected that the method may be useful for operations in marginal ice zones, such as offshore drilling, where a high resolution estimate of the ice edge location is required. It could also be useful as a first guess for an ice analyst, or for the assimilation of SAR data.

Extrapolating Satellite Winds to Turbine Operating Heights

AbstractOcean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellite-based wind maps. Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs. The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.02°. Calculations of the corresponding wind power density and Weibull parameters are shown. Comparisons with mast observations reveal that NWP model outputs can correct successfully for long-term stability effects and also, to some extent, for the limited number of satellite samples. The satellite-based and NWP-simulated wind profiles are almost equally accurate with respect to those from the mast. However, the satellite-based maps have a higher spatial resolution, which is particularly important in nearshore areas where most offshore wind farms are built.

Late-summer sea ice segmentation with multi-polarisation SAR features in C and X band

Abstract. In this study, we investigate the potential of sea ice segmentation by C- and X-band multi-polarisation synthetic aperture radar (SAR) features during late summer. Five high-resolution satellite SAR scenes were recorded in the Fram Strait covering iceberg-fast first-year and old sea ice during a week with air temperatures varying around 0 °C. Sea ice thickness, surface roughness and aerial photographs were collected during a helicopter flight at the site. Six polarimetric SAR features were extracted for each of the scenes. The ability of the individual SAR features to discriminate between sea ice types and their temporal consistency were examined. All SAR features were found to add value to sea ice type discrimination. Relative kurtosis, geometric brightness, cross-polarisation ratio and co-polarisation correlation angle were found to be temporally consistent in the investigated period, while co-polarisation ratio and co-polarisation correlation magnitude were found to be temporally inconsistent. An automatic feature-based segmentation algorithm was tested both for a full SAR feature set and for a reduced SAR feature set limited to temporally consistent features. In C band, the algorithm produced a good late-summer sea ice segmentation, separating the scenes into segments that could be associated with different sea ice types in the next step. The X-band performance was slightly poorer. Excluding temporally inconsistent SAR features improved the segmentation in one of the X-band scenes.

Synthetic aperture radar raw signal simulation based on inverse chirp scaling algorithm

By the analysis of the imaging principle and the raw echo model of synthetic aperture radar (SAR) (Bamler et al., 1992), an efficient simulation method is proposed based on inverse imaging algorithm for SAR raw echo data (Franceschetti et al., 1995). The idea of the inverse imaging algorithm is based on chirp scaling (CS) imaging algorithm called inverse chirp algorithm (ICS) (Xiao and Pi, 2003). It converts the SAR scene greyscale into the confocal and complex image data comprising the continuous phase information, using a scene processing method (Li et al., 1996). Then, a backward method is used. It can inverse and simulate SAR scene raw echo data after azimuth decompression, range decompression and range curvature compression (Khwaja, 2006). Finally, the imaging simulation test is carried out to verify the effectiveness of this method.

Comparative analysis of Edge Detection techniques for SAR images

Paleo-shorelines and ancient lake terraces east of Lake Manyara in Tanzania were identified from the backscatter intensity of Terra SAR-X StripMap images. Because of their linear alignment, edge detector algorithms were applied to delineate these morphological structures from those Synthetic Aperture Radar scenes. Due to the physical properties of microwave signals, this application has proven to be a challenging task for edge detectors. This study compares the performance of different combinations of speckle reduction techniques and edge operator in detecting linear paleo-shorelines. The Roberts, Sobel, Laplacian of Gaussian and the Canny edge detector algorithms were applied to extract and revise those linear structures. The comparison shows that the Canny edge detector is especially suitable for images with strong speckle noise. Canny achieves relatively high accuracies compared to the other operators. The stronger the filtering and speckle noise reduction, the better the performance of the other edge detection operators, compared to the Canny edge detector. The application of a wavelet transformation reduces the presence of artifacts resulting from speckle noise and emphasizes the detection of the target features.

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.

Potential of diffuse scatterer interferometry for monitoring CO2 storage sites in European contexts (land cover types)

This article proposes to test the feasibility of long-term surface deformation monitoring based on synthetic aperture radar (SAR) interferometry on carbon dioxide (CO2) storage sites with land cover representative of potential European injection sites (agricultural or forests with minimum built-up land cover). Because no operational injection site is currently active in Europe, a SAR data set (based on EnviSAT–ASAR spaceborne data) is simulated by combining SAR scenes acquired over a potential future European injection site with deformation measurements from SAR analysis carried out on the In-Salah (Algeria) CO2 injection demonstrator site. The study shows that under such conditions, both persistent scatterer interferometry (PSI) and diffuse scatterer (DS) interferometry appear insufficient to provide a sufficiently dense measurement network to characterize surface deformation correctly. Alternative solutions, to be investigated in further studies, include the use of data archives with shorter acquisition time spans (e.g. Sentinel-1 data when available) or installation of corner reflectors. The cost of the latter mixed space/ground solution must be evaluated with respect to conventional ground-based measurement methods in the proposed context.

A seamless multitrack multitemporal InSAR algorithm

AbstractThe zones of overlap between adjacent Synthetic Aperture Radar (SAR) satellite tracks are illuminated twice more frequently than elsewhere in the SAR scene. Here, an alternative approach is presented to combine the overlapping segments of SAR images acquired at adjacent tracks and generate accurate and high spatiotemporal resolution map of the surface deformation field. To this end, a new approach is developed to unify the datums. Effects due to the difference in look angle between two overlapping tracks and atmospheric delay are estimated and removed using Kalman and wavelet‐based filters. This approach is first tested at Hawaii Island, where tracks 200 and 429 of Envisat C‐band satellite overlap over the Kilauea south flank. The obtained time series improves the temporal sampling rate by a factor of two and comparison with GPS time series demonstrates that the presented method accurately measures the nonlinear deformation field. The advantages of this method are further demonstrated by combining SAR data sets acquired by Envisat C‐band and ALOS L‐band satellites over the San Francisco Bay Area, California. The validation test shows that the seamless combination of C‐band and L‐band time series accurately measures the surface deformation at higher resolution.

QUALITY ASSESSMENT FOR THE FIRST PART OF THE TANDEM-X GLOBAL DIGITAL ELEVATION MODEL

Abstract. TanDEM-X is an innovative synthetic aperture radar (SAR) mission with the main goal to generate a global and homogeneous digital elevation model (DEM) of the Earth’s land masses. The final DEM product will reach a new dimension of detail with respect to resolution and quality. The absolute horizontal and vertical accuracy shall each be less than 10 m in a 90% confidence interval at a pixel spacing of 12 m. The relative vertical accuracy specification for the TanDEM-X mission foresees a 90% point-to-point error of 2 m (4 m) for areas with predominant terrain slopes smaller than 20% (greater than 20%) within a 1° longitude by 1° latitude cell. The global DEM is derived from interferometric SAR acquisitions performed by two radar satellites flying in close orbit formation. Interferometric performance parameters like the coherence between the two radar images have been monitored and evaluated throughout the mission. In a further step, over 500,000 single SAR scenes are interferometrically processed, calibrated, and mosaicked into a global DEM product which will be completely available in the second half of 2016. This paper presents an up-todate quality status of the single interferometric acquisitions as well as of 50% of the final DEM. The overall DEM quality of these first products promises accuracies well within the specification, especially in terms of absolute height accuracy.

A User-Oriented Methodology for DInSAR Time Series Analysis and Interpretation: Landslides and Subsidence Case Studies

Recent advances in multi-temporal Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) have greatly improved our capability to monitor geological processes. Ground motion studies using DInSAR require both the availability of good quality input data and rigorous approaches to exploit the retrieved Time Series (TS) at their full potential. In this work we present a methodology for DInSAR TS analysis, with particular focus on landslides and subsidence phenomena. The proposed methodology consists of three main steps: (1) pre-processing, i.e., assessment of a SAR Dataset Quality Index (SDQI) (2) post-processing, i.e., application of empirical/stochastic methods to improve the TS quality, and (3) trend analysis, i.e., comparative implementation of methodologies for automatic TS analysis. Tests were carried out on TS datasets retrieved from processing of SAR imagery acquired by different radar sensors (i.e., ERS-1/2 SAR, RADARSAT-1, ENVISAT ASAR, ALOS PALSAR, TerraSAR-X, COSMO-SkyMed) using advanced DInSAR techniques (i.e., SqueeSAR™, PSInSAR™, SPN and SBAS). The obtained values of SDQI are discussed against the technical parameters of each data stack (e.g., radar band, number of SAR scenes, temporal coverage, revisiting time), the retrieved coverage of the DInSAR results, and the constraints related to the characterization of the investigated geological processes. Empirical and stochastic approaches were used to demonstrate how the quality of the TS can be improved after the SAR processing, and examples are discussed to mitigate phase unwrapping errors, and remove regional trends, noise and anomalies. Performance assessment of recently developed methods of trend analysis (i.e., PS-Time, Deviation Index and velocity TS) was conducted on two selected study areas in Northern Italy affected by land subsidence and landslides. Results show that the automatic detection of motion trends enhances the interpretation of DInSAR data, since it provides an objective picture of the deformation behaviour recorded through TS and therefore contributes to the understanding of the on-going geological processes.

Performance Analysis and Validation of Waterline Extraction Approaches Using Single- and Dual-Polarimetric SAR Data

In this study, the performance of two waterline extraction approaches is analyzed using dual-polarization Cosmo-SkyMed (CSK) Synthetic Aperture Radar (SAR) data and ancillary ground truth information. The single-polarization approach is based on multiscale normalized cuts segmentation; while, the dual-polarization one exploits the inherent peculiarities of the CSK PING PONG incoherent dual-polarimetric imaging mode together with a tailored scattering model to perform land/sea discrimination. The two approaches are applied to the actual CSK SAR data collected over the coastal area of Shanghai, China. To provide a detailed and complete validation of the two approaches, we carried out several field surveys collecting in situ ancillary information including Global Positioning System (GPS) data and tidal information. Experimental results show that 1) both approaches provide satisfactory results in extracting waterline from CSK SAR data in the intertidal flat under low-to-moderate wind conditions and under a very broad range of incidence angles; 2) the accuracy of the waterline extracted by both approaches decreases in case of water within the intertidal flat; 3) the single-polarization approach is unsupervised when the land/sea contrast ratio is high. However, it needs manual supervision to correct the extracted waterline when the land/sea contrast is low or in complex areas. A typical CSK scene is processed in about 25 min; 4) the dual-polarization approach is unsupervised and very effective: a typical CSK SAR scene is processed in seconds.

A Space View of Radar Archaeological Marks: First Applications of COSMO-SkyMed X-Band Data

With the development of Synthetic Aperture Radar (SAR) in terms of multi-band, multi-polarization and high-resolution data, space radar remote sensing for archaeology has become a potential field for research. Nevertheless, the archaeological detection capability of this technology has so far not been fully assessed. This paper is a pioneering effort to assess the potential of satellite SAR X-band data in the detection of archaeological marks. We focus on the results obtained from a collaborative contribution jointly carried out by archaeologists and remote sensing experts in order to test the use of COSMO-SkyMed data in different contexts and environmental conditions. The methodological approaches we adopted are based on two different feature-enhancement procedures: (i) multi-temporal analysis performed to reduce noise and highlight archaeological marks; (ii) single-date analysis to assess the ability of the single SAR scene to detect archaeological features like with optical remote sensing. Results from multi-temporal data analysis, conducted using 40 scenes from COSMO-SkyMed X-band Stripmap data (27 February to 17 October 2013), enable us to detect unknown archaeological crop, soil, and shadow marks representing Luoyang city, dating from the Eastern-Han to Northern-Wei Dynasties. Single-date analyses were conducted using COSMO-SkyMed Spotlight scenes acquired for Sabratha (Libya) and Metapontum (southern Italy). These case studies were selected because they are characterized by diverse superficial conditions (desert and Mediterranean area) and archaeological marks (crop, soil and shadow). The results we obtained for both of them show that even a single SAR X-band acquisition is a feasible and effective approach for archaeological prospection. Overall, the methodological approach adopted demonstrated that both multi-temporal and single-date analysis are suitable for the enhancement of archaeological and palaeoenvironmental features.

Flood detection from multi-temporal SAR data using harmonic analysis and change detection

Flood mapping from Synthetic Aperture Radar (SAR) data has attracted considerable attention in recent years. Most available algorithms typically focus on single-image techniques which do not take into account the backscatter signature of a land surface under non-flooded conditions. In this study, harmonic analysis of a multi-temporal time series of >500 ENVISAT Advanced SAR (ASAR) scenes with a spatial resolution of 150m was used to characterise the seasonality in backscatter under non-flooded conditions. Pixels which were inundated during a large-scale flood event during the summer 2007 floods of the River Severn (United Kingdom) showed strong deviations from normal seasonal behaviour as inferred from the harmonic model. The residuals were classified by means of an automatic threshold optimisation algorithm after masking out areas which are unlikely to be flooded using a topography-derived index. The results were validated against a reference dataset derived from high-resolution airborne imagery. For the water class, accuracies > 80% were found for non-urban land uses. A slight underestimation of the reference flood extent can be seen, mostly due to the lower spatial resolution of the ASAR imagery. Finally, an outlook for the proposed algorithm is given in the light of the Sentinel-1 mission.

SAR Image Categorization Using Parametric and Nonparametric Approaches Within a Dual Tree CWT

This letter presents synthetic aperture radar (SAR) image classification based on feature descriptors within the discrete wavelet transform (DWT) domain using parametric and nonparametric features. The DWT enables an efficient multiresolution description of SAR images due to its geometric and stochastic features. A 2-D DWT, a real 2-D oriented dual tree wavelet transform (2-D RODTWT) and an oriented dual tree complex wavelet transform (2-D ODTCWT) were used for the estimation of subband features. First and second moments, entropy, coding gain, and fractal dimension were used for the nonparametric approach. A parametric approach considers a Gauss Markov Random Field model for feature extraction. A database with 2000 images representing 20 different classes with 100 images per class was used for classification efficiency assessment. Several SAR scenes were divided into small patches with dimension of 200 × 200 pixels. 10% and 20% of the test images per class were used during the learning stage. Supervised learning using a support vector machine was used for all experiments. The experimental results showed that the proposed methods had superior performances compared with (GLCM) and log comulants of Fourier transform. Amongst the proposed methods, the nonparametric features within oriented dual tree complex wavelet transform gave the best results for classes when categorizing SAR images.

InSAR Coherence Estimation for Small Data Sets and Its Impact on Temporal Decorrelation Extraction

A novel coherence estimation method for small data sets is presented for interferometric synthetic aperture radar (SAR) (InSAR) data processing and geoscience applications. The method selects homogeneous pixels in both the spatial and temporal spaces by means of local and nonlocal adaptive techniques. Reliable coherence estimation is carried out by using such pixels and by correcting the bias in the estimated coherence caused by the non-Gaussianity in high-resolution SAR scenes. As an example, the proposed method together with coherence decomposition is applied to extract the temporal decorrelation component over an area in Macao. The results show that the proposed algorithms work well over various types of land cover. Moreover, the coherence change with time can be more accurately detected compared to other conventional methods.

Quad‐polarization SAR features of ocean currents

AbstractA methodology is demonstrated to exploit the polarization sensitivity of high‐resolution radar measurements to interpret and quantify upper ocean dynamics. This study particularly illustrates the potential of quad‐polarization synthetic aperture radar (SAR) measurements. The analysis relies on essential characteristics of the electromagnetic scattering mechanisms and hydrodynamical principles. As the relaxation scale of centimeter‐scale ocean surface scatters is typically small, radar signal anomalies associated with surface manifestations of the upper ocean dynamics on spatial scales exceeding 100 m are mostly dominated by nonresonant and nonpolarized scatters. These “scalar” contributions can thus efficiently trace local breaking and near‐breaking areas, caused by surface current variations. Using dual copolarized measurements, the polarized Bragg‐type radar scattering is isolated by considering the difference (PD) between vertically and horizontally polarized radar signals. The nonpolarized (NP) contribution associated with wave breaking is then deduced, using the measured polarization ratio (PR) between polarized signals. Considering SAR scenes depicting various surface manifestations of the upper ocean dynamics (internal waves, mesoscale surface current features, and SST front), the proposed methodology and set of decompositions (PD, PR, and NP) efficiently enable the discrimination between surface manifestation of upper ocean dynamics and wind field variability. Applied to quad‐polarized SAR images, such decompositions further provide unique opportunities to more directly assess the cross‐polarized (CP for HV or VH) signal sensitivity to surface roughness changes. As demonstrated, such an analysis unambiguously demonstrates and quantitatively evaluates the relative impact of breakers on cross‐polarized signals under low to moderate wind conditions.

Suppression of Clutter in Multichannel SAR GMTI

In this paper, the results of moving-target detection in multichannel UHF-band synthetic aperture radar (SAR) data are shown. The clutter suppression is done using finite-impulse response (FIR) filtering of multichannel SAR in combination with a two-stage fast-backprojection algorithm to focus the moving target using relative speed. The FIR filter coefficients are chosen with the use of space-time adaptive processing filtering. Two parameters are used for target focusing, target speed in range and in azimuth. When the target is focused, both speed parameters of the target are found. In the experimental results, two channels were used in order to suppress clutter. In the resulting SAR images, it is obvious that very strong scatterers and the forest areas have been suppressed in comparison to the moving target in the image scene. The gain obtained can be measured using signal-to-clutter-and-noise-ratio gain, which is about 19 dB. Another way to measure the signal processing gain is the ability to suppress the strongest reflecting object in the SAR scene. The gain of the target in relation to this object is 25 dB. This shows that using UHF-band SAR ground moving target indication (GMTI) for suppressing forest and increasing the target signal can work.

A Nine-Year Climatology of Arctic Sea Ice Lead Orientation and Frequency from AMSR-E

We infer the fractional coverage of sea ice leads (as concentration) in the Arctic from Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) brightness temperatures. The lead concentration resolves leads of at least 3 km in width. We introduce a new algorithm based on the progressive probabilistic Hough transform to automatically infer lead positions and orientations from daily AMSR-E satellite observations. Because the progressive probabilistic Hough transform often detects an identical lead several times the algorithm clusters neighboring leads that belong to one lead position. A first comparison of automatically detected lead positions and orientations with manually detected lead positions and orientations reveals that 57% of the reference leads are correctly determined. Around 11% of automatically detected leads are located where no reference lead occurs. The automatically detected lead orientations are distributed slightly differently from the reference lead orientations. A second comparison of automatically detected leads in the Fram Strait to leads in a wide swath mode Advanced Synthetic Aperture Radar scene shows a good agreement. We provide an Arctic-wide time series of lead orientations for winters from 2002 to 2011. For example, while a lead orientation of 110° with respect to the Greenwich meridian prevails in the Fram Strait, lead orientations in the Beaufort Sea are more isotropically distributed. We find significant preferred lead orientations almost everywhere in the Arctic Ocean when averaged over the entire AMSR-E time series.

DEM and SAR image based flood feature extraction techniques to map the deep and shallow flood inundated regions of known as well as remote disaster regions

It is viable to differentiate the deep and shallow flood inundated regions through a new flood feature extraction techniques named as ‘Digital Elevation Model (DEM) and Synthetic Aperture Radar (SAR) image based flood feature extraction model’. The proposed model has been built mainly on the top of DEM of the disaster region without adopting standard multi-layer GIS techniques. To meet the time related factors of flood early warning system the image clustering operations has been automated at three different levels which bifurcates the input datasets and extracts the much required end results such as deep flooded regions, shallow flood inundated regions and non-flooded regions. The model has been tested with SAR flood images of known geographical region as well as remote geographical region. The proposed model can be automated against the input SAR sensor image and corresponding DEM of the respective SAR scene of any part of the world.

Dual-Polarimetric TerraSAR-X SAR Data for Target at Sea Observation

A physical dual-polarimetric model to observe man-made metallic targets at sea in dual-polarimetric coherent X-band synthetic aperture radar (SAR) data is proposed. The model exploits the intrinsic different symmetry properties of man-made targets and sea surface and is tested over actual StripMap TerraSAR-X HH-HV and VV-VH dual-polarimetric SAR data and colocated ground truth measurements. Then, an operational physically based filter to observe targets at sea is proposed. The filter is very attractive in terms of both detection performances and processing time. A typical SAR scene is processed in seconds by a conventional PC processor.