Satellite Synthetic Aperture Radar Images Research Articles

Land subsidence in Tianjin for 2015 to 2016 revealed by the analysis of Sentinel-1A with SBAS-InSAR

It has been suggested that Tianjin, China, has significant land subsidence due to excessive extraction of water. Although it is presently under control, the land subsidence around Tianjin suburbs in recent years should not be ignored. However, existing research work on land subsidence is based on traditional synthetic aperture radar satellite images in which the research time spans are mainly before 2012. An advanced time-series method, namely small baselines subset (SBAS) technique, is applied to a total of 27 Sentinel-1A images over Tianjin acquired between May 31, 2015, and May 13, 2016, to derive the subsidence magnitude and distribution of Tianjin. Furthermore, the overall and quantitative validations of SBAS-derived results are implemented. First, the overall subsidence distribution derived by SBAS is compared with the annual report of land subsidence in Tianjin 2015, which shows the same subsidence trend and distribution. Then, 44 benchmarks and 2 continuously operating reference station datasets, i.e., CH01 and XQYY, are processed to provide a specific validation of SBAS-derived results of Sentinel-1A. Finally, through investigation, an interpretation from two aspects of groundwater extraction and geological structures of the surrounding Wangqingtuo settlement funnel area is given.

The SUMO Ship Detector Algorithm for Satellite Radar Images

Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1–100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU’s Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.

Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images

In this paper, we present a comparison between several algorithms for oil spill classifications using fully and compact polarimetric SAR images. Oil spill is considered as one of the most significant sources of marine pollution. As a major difficulty of SAR-based oil spill detection algorithms is the classification between mineral and biogenic oil, we focus on quantitatively analyzing and comparing fully and compact polarimetric satellite synthetic aperture radar (SAR) modes to detect hydrocarbon slicks over the sea surface, discriminating them from weak-damping surfactants, such as biogenic slicks. The experiment was conducted on quad-pol SAR data acquired during the Norwegian oil-on-water experiment in 2011. A universal procedure was used to extract the features from quad-, dual- and compact polarimetric SAR modes to rank different polarimetric SAR modes and common supervised classifiers. Among all the dual- and compact polarimetric SAR modes, the π/2 mode has the best performance. The best supervised classifiers vary and depended on whether sufficient polarimetric information can be obtained in each polarimetric mode. We also analyzed the influence of the number of polarimetric parameters considered as inputs for the supervised classifiers, onto the detection/discrimination performance. We discovered that a feature set with four features is sufficient for most polarimetric feature-based oil spill classifications. Moreover, dimension reduction algorithms, including principle component analysis (PCA) and the local linear embedding (LLE) algorithm, were employed to learn low dimensional and distinctive information from quad-polarimetric SAR features. The performance of the new feature sets has comparable performance in oil spill classification.

Quantitative Analysis of Shoreline Changes in Western Taiwan Coast Using Time-Series SAR Images

In this study, satellite synthetic aperture radar (SAR) image dataset spanning 15 years was acquired for temporal analysis of the shorelines changes. Focus is placed on how the natural forces impact on the coastline and sandbank changes. The waterlines were extracted from SAR images based on a wavelet correlator. Tidal and bathymetric data were used to validate the waterline extraction and were subsequently integrated for analysis. Since waterlines depend on the tide, detection of inter-tidal zones and sandbanks over long periods of time is necessary. Rapid changes of the central coast of western Taiwan are observed via spatial-temporal analysis of physical mechanisms of coastal changes within sandbanks, which have experienced an extent of size variations. These variations depict the interchanges of deposition and depletion from sand supply and the changes of waves–currents–coast interactions due to the presence of natural forces and human settlements. Human-made structures impact the current field (velocity and direction) by diverting the direction of the current from northward to southward along the coast. As a result, they indirectly affect the course of sandbank displacement. Sediment transport plays a vital role in shifting sandbars off the coast. Winter monsoons and tidal currents together have constantly exerted influence on moving barrier sandbanks southwardly and have distorted their shape.

Satellite SAR observation of the sea surface wind field caused by rain cells

Rain cells or convective rain, the dominant form of rain in the tropics and subtropics, can be easy detected by satellite Synthetic Aperture Radar (SAR) images with high horizontal resolution. The footprints of rain cells on SAR images are caused by the scattering and attenuation of the rain drops, as well as the downward airflow. In this study, we extract sea surface wind field and its structure caused by rain cells by using a RADARSAT-2 SAR image with a spatial resolution of 100 m for case study. We extract the sea surface wind speeds from SAR image by using CMOD4 geophysical model function with outside wind directions of NCEP final operational global analysis data, Advance Scatterometer (ASCAT) onboard European MetOp-A satellite and microwave scatterometer onboard Chinese HY-2 satellite, respectively. The root-mean-square errors (RMSE) of these SAR wind speeds, validated against NCEP, ASCAT and HY-2, are 1.48 m/s, 1.64 m/s and 2.14 m/s, respectively. Circular signature patterns with brighter on one side and darker on the opposite side on SAR image are interpreted as the sea surface wind speed (or sea surface roughness) variety caused by downdraft associated with rain cells. The wind speeds taken from the transect profile which superposes to the wind ambient vectors and goes through the center of the circular footprint of rain cell can be fitted as a cosine or sine curve in high linear correlation with the values of no less than 0.80. The background wind speed, the wind speed caused by rain cell and the diameter of footprint of the rain cell with kilometers or tens of kilometers can be acquired by fitting curve. Eight cases interpreted and analyzed in this study all show the same conclusion.

A Depolarization Ratio Anomaly Detector to Identify Icebergs in Sea Ice Using Dual-Polarization SAR Images

Icebergs represent hazards to maritime traffic and offshore operations. Satellite synthetic aperture radar (SAR) is very valuable for the observation of polar regions, and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this paper, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross-polarization and higher cross- over copolarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. First, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high-resolution data. Subsequently, a more exhaustive analysis is carried out using dual-polarization ground-detected Sentinel-1a extra wide swath images acquired over the time span of two months. The test areas are in the east coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross-polarization channel are carried out, exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection.

Efficiency Assessment of Multitemporal C-Band Radarsat-2 Intensity and Landsat-8 Surface Reflectance Satellite Imagery for Crop Classification in Ukraine

Ukraine is one of the most developed agricultural countries in the world. For many applications, it is extremely important to provide reliable crop maps taking into account diversity of cropping systems used in Ukraine. The use of optical imagery only is limited due to cloud cover, and previous studies showed particular difficulties in discriminating summer crops in Ukraine such as maize, soybeans, sunflower, and sugar beet. This paper focuses on exploring feasibility and assessing efficiency of using multitemporal satellite synthetic-aperture radar (SAR) acquired in C-band and optical images for crop classification in Ukraine. Both optical (Landsat-8/OLI) and SAR (Radarsat-2) images are used to assess the impact of adding backscattering intensity from SAR images for classification purposes. SAR intensity information is very important due to availability of Sentinel-1 imagery over Ukraine starting March 2015. Different combinations of optical and SAR images, as well as SAR modes and polarizations, are assessed for better discrimination of crops. A committee of neural networks, in particular multilayer perceptrons (MLPs), is used to improve classification accuracy compared to several standard classifiers. It is found that using backscatter coefficients from SAR images alone provides the same performance for winter crops (wheat and rapeseed) as surface reflectance from optical images. Considering the summer crops, the major impact of adding backscatter intensity information from SAR images is in better separation of sunflower, soybeans, and maize.

Comparison of a massive and diverse collection of ensembles and other classifiers for oil spill detection in SAR satellite images

We present a comparison of the largest collection of classifiers considered until now in the literature, composed by 428 methods belonging to 41 very different families. This collection, much larger than the one in our previous work (Fernandez-Delgado et al. in J Mach Learn Res 15:3133–3181, 2014), includes 320 ensembles (varying the base and meta-classifiers), alongside with Support Vector Machines, Bayesian, Neural Networks, Discriminant Analysis, Multivariate Adaptive Regression Splines, Random Forests, Decision Trees and many others. The classifier comparison is developed on the detection of oil spills on Synthetic Aperture Radar (SAR) images taken from satellites. The SAR images have revealed very useful to surveillance maritime agencies for the detection of regular offshore operational discharges, which, despite is commonly accepted, is one of the biggest causes of hydrocarbon marine pollution, instead of tanker and oil platform catastrophes. After a segmentation of the SAR images to select oil spill candidates, classifiers use the features extracted from these candidates to discard frequent and expensives look-alikes (false positives), caused by natural phenomena. Testing experiments revealed that the RotationForest ensemble of MultilayerPerceptron base classifiers, applying Kernel PCA on the original data, achieves the best accuracy and Cohen \(\kappa\) (87.1 % and 71.0 %, respectively) with a low frequency of false positives (5.13 %).

Mapping landslide phenomena in landlocked developing countries by means of satellite remote sensing data: the case of Dilijan (Armenia) area

ABSTRACTLandslide detection and mapping are essential issues for reducing impact of such natural disasters, and for improving the future built-up expansion and planning strategies, especially in developing countries where a reasonable land-use design is an important concern for sustainable growth and environmental management. Armenia is a landlocked country and its urban development is strongly tied to the improvement of infrastructures, which must takes into account the environmental setting and the slope instability of the area, in order to identify risks and possible damages to settlements and economic activities. The use of satellite-based Earth Observation data has advanced significantly in the last decade and has turned out to be very useful for measuring and monitoring slow-moving surface deformation phenomena with millimetric precision. In this framework, this study aims at providing a remote sensing-based Landslide Inventory Map (LIM) and a Landslide Susceptibility Map (LSM) over Dilijan (Armenia) area, performed within the Secondary Cities Urban Development in Armenia project. In particular, LIM and LSM in the study area were produced by using ground deformation measurements derived from satellite Synthetic Aperture Radar (SAR) data, acquired by ALOS and ENVISAT sensors from 2003 up to 2010, and integrated with photo-interpretation of recent optical images and morphological analysis of Digital Elevation Model (DEM). Given the extensive presence of vegetation in the area of interest, satellite SAR images were processed to produce both SqueeSAR™ and Temporary Coherent Scatterers data, which are PSI (Persistent Scatterer Interferometry) data conceived as evolution of PSInSAR™ approach and particularly suited for non-urban and rural areas characterized by low density of coherent terrain benchmarks over time. Landslide mapping produced through this work identifies the most hazardous landslide-affected and landslide-prone areas around Dilijan city, and can be used for further estimating environmental risks for urban infrastructure development in the area.

Origins and features of oil slicks in the Bohai Sea detected from satellite SAR images

Oil slicks were detected using satellite Synthetic Aperture Radar (SAR) images in 2011. We investigated potential origins and regional and seasonal features of oil slick in the Bohai Sea. Distance between oil slicks and potential origins (ships, seaports, and oil exploitation platforms) and the angle at which oil slicks move relative to potential driving forces were evaluated. Most oil slicks were detected along main ship routes rather than around seaports and oil exploitation platforms. Few oil slicks were detected within 20km of seaports. Directions of oil slicks movement were much more strongly correlated with directions of ship routes than with directions of winds and currents. These findings support the premise that oil slicks in the Bohai Sea most likely originate from illegal disposal of oil-polluted wastes from ships. Seasonal variation of oil slicks followed an annual cycle, with a peak in August and a trough in December.

Building Damage Assessment Using High-Resolution Satellite SAR Images of the 2010 Haiti Earthquake

Damage to individual buildings in an urban area of Port-au-Prince, Haiti, from the 2010 Haiti earthquake was assessed by means of high-resolution synthetic aperture radar (SAR) intensity images and ancillary building footprints. A comparison of pre- and post-event images and a building damage inventory showed that backscattering intensity between images was more significantly changed in collapsed buildings than in less damaged buildings. The linear discriminant function, based on the difference and correlation coefficient of the images was developed to detect collapsed buildings. The result showed that almost 75% of the buildings were correctly detected by discriminant analysis. An accuracy assessment revealed the difficulty of detecting small and congested buildings because the number of image pixels was too small and the buildings were obscured by neighboring buildings and other features in the images.

Natural and unnatural oil slicks in the Gulf of Mexico

When wind speeds are 2–10 m s−1, reflective contrasts in the ocean surface make oil slicks visible to synthetic aperture radar (SAR) under all sky conditions. Neural network analysis of satellite SAR images quantified the magnitude and distribution of surface oil in the Gulf of Mexico from persistent, natural seeps and from the Deepwater Horizon (DWH) discharge. This analysis identified 914 natural oil seep zones across the entire Gulf of Mexico in pre‐2010 data. Their ∼0.1 µm slicks covered an aggregated average of 775 km2. Assuming an average volume of 77.5 m3 over an 8–24 h lifespan per oil slick, the floating oil indicates a surface flux of 2.5–9.4 × 104 m3 yr−1. Oil from natural slicks was regionally concentrated: 68%, 25%, 7%, and <1% of the total was observed in the NW, SW, NE, and SE Gulf, respectively. This reflects differences in basin history and hydrocarbon generation. SAR images from 2010 showed that the 87 day DWH discharge produced a surface‐oil footprint fundamentally different from background seepage, with an average ocean area of 11,200 km2 (SD 5028) and a volume of 22,600 m3 (SD 5411). Peak magnitudes of oil were detected during equivalent, ∼14 day intervals around 23 May and 18 June, when wind speeds remained <5 m s−1. Over this interval, aggregated volume of floating oil decreased by 21%; area covered increased by 49% (p < 0.1), potentially altering its ecological impact. The most likely causes were increased applications of dispersant and surface burning operations.

Updated landslide inventory of the area between the Furiano and Rosmarino creeks (Sicily, Italy)

ABSTRACTA 1:10,000 scale landslide inventory map has been prepared for the area between the Furiano and Rosmarino creeks, in the Nebrodi Mountains (north-eastern Sicily, Italy), a territory highly prone to slope failures, due to the local geological and geomorphological settings and intense rainfall. The landslide inventory database included within the Hydrogeological Setting Plan of the Sicily Region has been used as a starting point for this work. The updated inventory map has been compiled through a combination of conventional approaches (i.e. aerial photo-interpretation and field surveys) and new remote sensing techniques (ground deformation measurements obtained by interferometric analysis of satellite Synthetic Aperture Radar images). The new landslide inventory consists of 566 events, classified according to their typology and state of activity.

Exploitation of Amplitude and Phase of Satellite SAR Images for Landslide Mapping: The Case of Montescaglioso (South Italy)

Pre- event and event landslide deformations have been detected and measured for the landslide that occurred on 3 December 2013 on the south-western slope of the Montescaglioso village (Basilicata Region, southern Italy). In this paper, ground displacements have been mapped through an integrated analysis based on a series of high resolution SAR (Synthetic Aperture Radar) images acquired by the Italian constellation of satellites COSMO-SkyMed. Analysis has been performed by exploiting both phase (through multi-image SAR interferometry) and amplitude information (through speckle tracking techniques) of the satellite images. SAR Interferometry, applied to images taken before the event, revealed a general pre-event movement, in the order of a few mm/yr, in the south-western slope of the Montescaglioso village. Highest pre-event velocities, ranging between 8 and 12 mm/yr, have been recorded in the sector of the slope where the first movement of the landslide took place. Speckle tracking, applied to images acquired before and after the event, allowed the retrieval of the 3D deformation field produced by the landslide. It also showed that ground displacements produced by the landslide have a dominant SSW component, with values exceeding 10 m for large sectors of the landslide area, with local peaks of 20 m in its central and deposit areas. Two minor landslides with a dominant SSE direction, which were detected in the upper parts of the slope, likely also occurred as secondary phenomena as consequence of the SSW movement of the main Montescaglioso landslide.

Analysis of a landslide multi-date inventory in a complex mountain landscape: the Ubaye valley case study

Abstract. In the paper we analyse a multi-date landslide inventory prepared for a mountainous area affected by several landslide types with different degrees of activity, we attempt to quantify the uncertainties associated to the mapping, we measure the evolution of morphological indicators and estimate landslide activity and temporal occurrence. The inventory, covering the period 1956–2010, is prepared for the middle section of the Ubaye valley (southern French Alps) based on the analysis of multi-source documents (geomorphological maps, historical reports of landslide events, field surveys, orthophotographs and SAR (synthetic aperture radar) satellite images). The uncertainties derived from the expert interpretation of different sources of information, the landslide morphological features and the affected land covers are taken into account in relation to the source documents. Morphological indicators are calculated to describe quantitatively the evolution of the landslides (length, area, relative elevation, runout distance). Frequency–area density functions are computed to estimate the changes in the landslide distributions and a Poisson model is used to estimate the probability of reactivation of the observed landslides and the occurrence of new failures. The proposed multi-date inventory and the associated statistics provide additional information to the event catalogue managed by the local policy makers.

Disintegration and acceleration of Thwaites Ice Shelf on the Amundsen Sea revealed from remote sensing measurements

Thwaites Ice Shelf in the Amundsen Sea is one of the biggest ice shelves in West Antarctica and is well known for significant mass changes. In the shear zone between Thwaites Glacier Tongue and its eastern ice shelf, shear stress forced by different flow rates of the ice shelves is causing the ice to break apart. A time series analysis of remote sensing data obtained by Landsat 7 Enhanced Thematic Mapper Plus (ETM+), TerraSAR-X, and airborne synthetic aperture radar (SAR) revealed that the shear zone has extended since 2006 and eventually disintegrated in 2008. We quantified the acceleration of Thwaites Ice Shelf with time by using the feature tracking method. The buttressing loss induced by the extension of the shear zone and progressive disintegration accelerated the flow of Thwaites Glacier Tongue, which in turn increased the shear stress on its eastern ice shelf. We determine causes of disintegration in the newly formed shear zone to be oceanic basal melting and structural weakening induced by Circumpolar Deep Water intrusion beneath the eastern ice shelf since 2000. The structural weakening was examined by using the density distribution of rifts and crevasses on the ice shelf, which were well identified from high-resolution SAR and optical satellite images.

Development of Methods for Detection and Monitoring of Fire Disturbance in the Alaskan Tundra Using a Two-Decade Long Record of Synthetic Aperture Radar Satellite Images

Using the extensive archive of historical ERS-1 and -2 synthetic aperture radar (SAR) images, this analysis demonstrates that fire disturbance can be effectively detected and monitored in high northern latitudes using radar technology. A total of 392 SAR images from May to August spanning 1992–2010 were analyzed from three study fires in the Alaskan tundra. The investigated fires included the 2007 Anaktuvuk River Fire and the 1993 DCKN178 Fire on the North Slope of Alaska and the 1999 Uvgoon Creek Fire in the Noatak National Preserve. A 3 dB difference was found between burned and unburned tundra, with the best time for burned area detection being as late in the growing season as possible before frozen ground conditions develop. This corresponds to mid-August for the study fires. In contrast to electro-optical studies from the same region, measures of landscape recovery as detected by the SAR were on the order of four to five years instead of one.

Persistent Scatterer Pair Interferometry: Approach and Application to COSMO-SkyMed SAR Data

Persistent scatterer interferometry is a widely used technique to detect and monitor slow terrain movements, with millimetric accuracy, from satellite synthetic aperture radar (SAR) data. We have recently proposed a method, named persistent scatterer pair (PSP), aimed at overcoming some limitations of standard techniques. The PSP method is characterized by the fact of exploiting only the relative properties of neighboring pairs of points for both detection and analysis of persistent scatterers (PSs), intended in the general sense of scatterers that exhibit interferometric coherence for the time period and baseline span of the acquisitions, including both point-like and distributed scatterers. Thanks to the pair-of-point approach, the PSP technique is intrinsically not affected by artifacts slowly variable in space, like those depending on atmosphere or orbits. Moreover, by exploiting a very redundant set of pair-of-point connections, the PSP approach guarantees extremely dense and accurate displacement and elevation measurements, both in correspondence of structures and when the backscattering is weak or distributed as in the case of natural terrains. In all cases, the measurements keep the full resolution of the input SAR images. In this work, the qualifying characteristics of the PSP technique are described, and several application examples and validation tests based on COSMO-SkyMed data are reported, which demonstrate the validity of the proposed approach.

Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring

The aim of this study was to assess the ability of optical images, SAR (Synthetic Aperture Radar) images and the combination of both types of data to discriminate between grasslands and crops in agricultural areas where cloud cover is very high most of the time, which restricts the use of visible and near-infrared satellite data. We compared the performances of variables extracted from four optical and five SAR satellite images with high/very high spatial resolutions acquired during the growing season. A vegetation index, namely the NDVI (Normalized Difference Vegetation Index), and two biophysical variables, the LAI (Leaf Area Index) and the fCOVER (fraction of Vegetation Cover) were computed using optical time series and polarization (HH, VV, HV, VH). The polarization ratio and polarimetric decomposition (Freeman–Durden and Cloude–Pottier) were calculated using SAR time series. Then, variables derived from optical, SAR and both types of remotely-sensed data were successively classified using the Support Vector Machine (SVM) technique. The results show that the classification accuracy of SAR variables is higher than those using optical data (0.98 compared to 0.81). They also highlight that the combination of optical and SAR time series data is of prime interest to discriminate grasslands from crops, allowing an improved classification accuracy.

Shoreline movement monitoring based on SAR images in Shanghai, China

In this article, a divide-and-combine approach and multiscale normalized cut techniques are combined to extract water–land boundaries from synthetic aperture radar (SAR) images. A validation study shows that waterline extraction from SAR using the proposed algorithm is consistent with that derived from Landsat optical remote-sensing imagery. Analysis of six European Remote Sensing-1/2 (ERS-1/2) satellite and Envisat SAR images acquired at spring tides in Lingang New City, a new land-reclamation city in the suburb of Shanghai, China, between 1993 and 2005 shows the shoreline movement of the study region. We find that the shoreline had minimum seaward movement in the region due to natural forces between 1993 and 1996. Between 1996 and 1999, the shoreline moved more in the eastern and slower in the western part due to artificial impelling siltation and deposition of silt in the Yangtze River. Later, between 1999 and 2005, the shoreline significantly changed following commencement of a tidal flat reclamation project.