TerraSAR-X Synthetic Aperture Radar Research Articles

Model-based comparisons of near-coincident TerraSAR-X and COSMO-SkyMed VV-polarized SAR measurements over sea surface with and without oil slicks

ABSTRACT This paper contrasts predicted X-band sea surface backscattering from slick-free and oil-covered sea surfaces with actual measurements acquired by the X-band satellite TerraSAR-X and COSMO-SkyMed Synthetic Aperture Radar (SAR) missions. Two SAR scenes were acquired with a temporal difference of about 36 minutes, under similar met-ocean conditions, during the North Sea’s Gannet Alpha oil spill accident. The normalized radar cross section of the slick-free sea surface is predicted using the Advanced Integral Equation Model (AIEM) while the backscatter from the oiled sea surface is predicted by the AIEM augmented with the Model of Local Balance (MLB) to include the damping effect of oil slicks. Experimental results show that X-band co-polarized numerical predictions agree reasonably well with both TSX and CSK actual measurements collected over slick-free sea surfaces. When dealing with oil-covered sea surfaces, the predicted backscattering reasonably agrees with TSX measurements, while it overestimates the CSK ones. This is likely due to the different spreading conditions of the oil imaged by the two satellite missions.

Aircraft Detection from Low SCNR SAR Imagery Using Coherent Scattering Enhancement and Fused Attention Pyramid

Recently, methods based on deep learning have been applied to target detection using synthetic aperture radar (SAR) images. However, due to the SAR imaging mechanism and low signal-clutter-noise-ratio (SCNR), it is still a challenging task to perform aircraft detection using SAR imagery. To address this issue, a novel aircraft detection method is proposed for low SCNR SAR images that is based on coherent scattering enhancement and a fusion attention mechanism. Considering the scattering characteristics discrepancy between human-made targets and natural background, a coherent scattering enhancement technique is introduced to heighten the aircraft scatter information and suppress the clutter and speckle noise. This is beneficial for the later ability of the deep neural network to extract accurate and discriminative semantic information about the aircraft. Further, an improved Faster R-CNN is developed with a novel pyramid network constructed by fusing local and contextual attention. The local attention adaptively highlights the significant objects by enhancing their distinguishable features, and the contextual attention facilitates the network to extract distinct contextual information of the image. Fusing the local and contextual attention can guarantee that the aircraft is detected as completely as possible. Extensive experiments are performed on TerraSAR-X SAR datasets for benchmark comparison. The experimental results demonstrate that the proposed aircraft detection approach could achieve up to 91.7% of average precision in low SCNR, showing effectiveness and superiority over a number of benchmarks.

The 21 July 2020 Shaziba landslide in China: Results from multi-source satellite remote sensing

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure.The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand.

Analysis of Wave Breaking on Gaofen-3 and TerraSAR-X SAR Image and Its Effect on Wave Retrieval

The main purpose of our work is to investigate the performance of wave breaking and its effect on wave retrieval in data acquired from the Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) at C-band and the German TerraSAR-X (TS-X) at X-band. The SAR images available for this study included 140 GF-3 images acquired in quad-polarization strip (QPS) mode and 50 dual-polarized (vertical-vertical (VV) and horizontal-horizontal (HH)) TS-X images acquired in stripmap (SM) mode. Moreover, these images were collocated with the waves simulated by the numeric WAVEWATCH-III (WW3) (version 5.16) model and HYbrid Coordinate Ocean Model (HYCOM) current. In particular, a few images covered the moored buoys monitored by the National Data Buoy Center (NDBC) of the National Oceanic and Atmospheric Administration (NOAA). The comparison between the WW3-simulated results and the significant wave heights (SWHs) from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data (ERA-5) showed that the correlation coefficient (COR) was 0.4–0.6 with a root mean squared error (RMSE) of about 0.2 m at SWHs of 0–4 m. The winds were inverted using VV-polarized geophysical model functions (GMFs), e.g., CSARMOD-GF for the GF-3 images and XMOD2 for the TS-X images. The Bragg resonant roughness in the normalized radar cross section (NRCS) was simulated using a radar backscattering model and the SAR-derived wind, WW3-simulated wave parameters, and HYCOM current. Then, the contribution of the non-polarized (NP) wave breaking to the SAR data was estimated by the VV-polarized NRCS, the HH-polarized NRCS, and the polarization ratio (PR) of the co-polarized Bragg resonant components in the NRCS. Because co-polarized Bragg resonant components in the NRCSs have poor results, due to the saturation for wind speeds greater than 20 m/s, the analysis of wave breaking is excluded at such conditions. The results revealed that the backscattering signal in the C-band was more sensitive to wave breaking than the backscattering signal in the X-band. Interestingly, the ratio had a linear correlation with wind speed. Moreover, the variation in the bias (inverted SWH minus WW3 simulation) showed that the bias increased as the wind speed (>8 m/s) and whitecap coverage (>0.005) increased. Following this rationale, wave retrieval during tropical cyclones should consider the influence of wave breaking.

An Approach of Sea Clutter Suppression for SAR Images by Self-Supervised Complex-Valued Deep Learning

Strong reflections from the marine surface reduce the contrast between the target-of-interest and the background in synthetic aperture radar (SAR) images and severely affect the interpretation of the image. This letter proposes a framework of SAR sea clutter suppression based on a new self-supervised training strategy referred to as Clutter2Clutter (C2C), which mines self-supervised information from a large number of unlabeled SAR patches for network training. This letter also proposes a complex-valued UNet++ (CV-UNet++) network model to make full use of both amplitude and phase information of the complex SAR image, and the C2C strategy is used to train the CV-UNet++ for sea clutter suppression. Experiments on GF-3 and TerraSAR-X SAR data show that the proposed method has a better effect on suppressing sea clutter and is able to preserve the target-of-interest energy well.

Global time series and temporal mosaics of glacier surface velocities derived from Sentinel-1 data

Abstract. Consistent and continuous data on glacier surface velocity are important inputs to time series analyses, numerical ice dynamic modeling and glacier mass flux computations. Since 2014, repeat-pass synthetic aperture radar (SAR) data have been acquired by the Sentinel-1 satellite constellation as part of the Copernicus program of the EU (European Union) and ESA (European Space Agency). It enables global, near-real-time-like and fully automatic processing of glacier surface velocity fields at up to 6 d temporal resolution, independent of weather conditions, season and daylight. We present a new global data set of glacier surface velocities that comprises continuously updated scene-pair velocity fields, as well as monthly and annually averaged velocity mosaics at 200 m spatial resolution. The velocity information is derived from archived and new Sentinel-1 SAR acquisitions by applying a well-established intensity offset tracking technique. The data set covers 12 major glacierized regions outside the polar ice sheets and is generated in an HPC (high-performance computing) environment at the University of Erlangen-Nuremberg. The velocity products are freely accessible via an interactive web portal that provides capabilities for download and simple online analyses: http://retreat.geographie.uni-erlangen.de (last access: 6 October 2021). In this paper, we give information on the data processing and how to access the data. For the example region of Svalbard, we demonstrate the potential of our products for velocity time series analyses at very high temporal resolution and assess the quality of our velocity products by comparing them to those generated from very high-resolution TerraSAR-X SAR and Landsat-8 optical (ITS_LIVE, GoLIVE) data. The subset of Sentinel-1 velocities for Svalbard analyzed in this paper is accessible via the GFZ Potsdam Data Services under the DOI https://doi.org/10.5880/fidgeo.2021.016 (Friedl et al., 2021). We find that Landsat-8 and Sentinel-1 annual velocity mosaics are in an overall good agreement, but speckle tracking on Sentinel-1 6 d repeat acquisitions derives more reliable velocity measurements over featureless and slow-moving areas than the optical data. Additionally, uncertainties of 12 d repeat Sentinel-1 mid-glacier scene-pair velocities have less than half (< 0.08 m d−1) of the uncertainties derived for 16 d repeat Landsat-8 data (0.17–0.18 m d−1).

TanDEM-X: 10 Years of Formation Flying Bistatic SAR Interferometry

On June 21, 2010, the TanDEM-X mission was launched and opened a new era in spaceborne radar remote sensing. The first formation flying radar system was built by extending the TerraSAR-X synthetic aperture radar (SAR) mission by a second, TerraSAR-X-like satellite TanDEM-X. The resulting large single-pass SAR interferometer features flexible baseline selection, enabling the acquisition of highly accurate cross-track interferograms not impacted by temporal decorrelation and atmospheric disturbances. The primary objective of the mission was the generation of a global Digital Elevation Model (DEM) with unprecedented accuracy (12-m horizontal resolution and 2-m relative height accuracy). The main mission phase for DEM data acquisition has been finished in 2014; the processing of the global TanDEM-X DEM was concluded in September 2016. The final DEM product is well within specifications and features an extremely low percentage of void areas. It is of fundamental importance for a wide range of commercial and scientific applications. But the scientific exploitation of TanDEM-X is not limited to the DEM. TanDEM-X has unique capabilities, including along-track interferometry, and new bistatic and multistatic SAR techniques, that support numerous secondary mission objectives. Indeed, some of these experiments were directly performed during the DEM acquisition phase, when suitable satellite formation geometries were available. Moreover, regular acquisitions over selected super test sites enabled multitemporal analyses. A dedicated science phase after the DEM acquisitions included up to 4 km cross-track baselines, operation in the so-called Dual-Receive Antenna mode, as well as a period in pursuit monostatic flight formation. Comparisons of the TanDEM-X DEM with that of SRTM, or among multitemporal TanDEM-X data, revealed dramatic, ongoing, changes in Earth's topography, especially over ice and forests. In the last 3.5 years the mission has further acquired data for a global change layer showing the height changes relative to the first global DEM dataset. The so-called “Change DEM” is planned for release in 2021. Despite being well beyond their design lifetime, both satellites are still fully functional and have enough consumables for several additional years. Therefore, bistatic operations continue with a focus on changes in the cryosphere, biosphere, and densely populated urban areas.

Mineral Oil Slicks Identification Using Dual Co-polarized Radarsat-2 and TerraSAR-X SAR Imagery

This study is devoted to a generalization of C-band Radarsat-2 and X-band TerraSAR-X synthetic aperture radar (SAR) data in the form of a diagram serving to easily identify mineral oil slicks (crude oil and emulsions) and separate them from the other oil slicks. The diagram is based on the multi-polarization parameter called Resonant to Non-resonant signal Damping (RND) introduced by Ivonin et al. in 2016, which is related to the ratio between damping within the slick of the short waves and wave breakings. SAR images acquired in the North Sea during oil-on-water exercises in 2011–2012 containing three types of oil spills (crude oil, emulsion, and plant oil) were used. The analysis was performed under moderate sea conditions (wind speeds of 2–6 m/s and sea wave heights of less than 2 m), the incidence angles of 27°–49°, and the signal-to-noise ratio (SNR) of −3 to 11 dB within slicks. On the diagram plane, created by the RND parameter and the Bragg wave number, the mineral oil samples form a well-outlined zone, called a mineral oil zone. For C-band data, the plant oil samples were clearly distinguished from the mineral oils in the diagram. Determination of the confidence level for the detection of mineral oils versus plant oil was proposed using the mineral oil zone boundaries. The mineral oil data with SNR within slicks better than 2 dB lay within this zone with a confidence level better than 65%. The plant oil data with the same SNR lay outside this zone with a confidence level of better than 80%. For mineral oil with SNR of −3 dB, the confidence level is 55%.

Analysis of the Multipath Scattering Effects in High-Resolution SAR Images

Due to multipath (MP) scattering, many special phenomena and details about the targets in high-resolution synthetic aperture radar (SAR) images are hard to understand. In this letter, a more elaborate improved model combined with the synthetic aperture progress is presented, to analyze the MP scattering. We explore the defocusing and displacement phenomena of MP scattering through signal modeling and time–frequency relationship analysis. The theoretical equations for the displacement of double scattering are derived, and the equation of phase error which causes the defocusing is also given. The SAR raw data simulation of MP scattering and range-Doppler (RD) imaging results validates the correctness of the analysis. From the equations, the effects of MP scattering of different height and different orientation angle in high-resolution SAR images are given. Finally, based on the simulation results, the new interpretation about the relationship between the moustache effect in high-resolution TerraSAR-X SAR images and the height of the structure is obtained.

QUANTITATIVE ASSESSMENT OF RICE CROP DAMAGE POST TITLI CYCLONE IN SRIKAKULAM, ANDHRA PRADESH USING GEO-SPATIAL TECHNIQUES

Abstract. Mapping the extent of damage due to natural calamities remains one of the thrust areas in monitoring resource inventory through geo-spatial techniques. The effect of the cyclone ‘Titli’ and heavy rains during first fortnight of October 2018 in Srikakulam district, Andhra Pradesh State has been demonstrated using geo-spatial technology in terms of flood inundated rice area and corresponding yield and production loss. The pre- and post-cyclone (5 and 13 October 2018) flood inundation maps were generated using Sentinel-1A and TerraSAR-X Synthetic Aperture Radar (SAR) data respectively. The pre-cyclone rice area estimates were derived from multi-temporal Sentinel-1A SAR data, while yield forecast is based on the combination of satellite observations and yield simulation using ORYZA crop growth model. An intensive ground truth data collection had been carried out for the validation of satellite-derived rice area estimation of pre-cyclone event. An accuracy assessment has been carried out for district, mandal and village level. An overall accuracy of 96% with kappa coefficient 0.92 has been achieved. With the help map flood inundation and rice area maps, mandal-wise flood affected rice area and corresponding yield loss have been estimated. The post-cyclone ground truth data had been collected for quantitative assessment of crop damaged area. An overall accuracy of the flood affected rice map was 85% with kappa coefficient 0.70. It was estimated that rice crop damage assessment with SAR data indicated 53312 ha out of 205174 ha were affected and corresponding estimated yield as well as production are 0.8 t/ha and 189160 t respectively.

Refocusing Moving Ship Targets in SAR Images Based on Fast Minimum Entropy Phase Compensation.

Moving ship targets appear blurred and defocused in synthetic aperture radar (SAR) images due to the translation motion during the coherent processing. Motion compensation is required for refocusing moving ship targets in SAR scenes. A novel refocusing method for moving ship is developed in this paper. The method is exploiting inverse synthetic aperture radar (ISAR) technique to refocus the ship target in SAR image. Generally, most cases of refocusing are for raw echo data, not for SAR image. Taking into account the advantages of processing in SAR image, the processing data are SAR image rather than raw echo data in this paper. The ISAR processing is based on fast minimum entropy phase compensation method, an iterative approach to obtain the phase error. The proposed method has been tested using Spaceborne TerraSAR-X, Gaofeng-3 images and airborne SAR images of maritime targets.

New Approaches for Robust and Efficient Detection of Persistent Scatterers in SAR Tomography

Persistent scatterer interferometry (PSI) has the ability to acquire submeter-scale digital elevation model (DEM) and millimeter-scale deformation. A limitation to the application of PSI is that only single persistent scatterers (SPSs) are detected, and pixels with multiple dominant scatterers from different sources are discarded in PSI processing. Synthetic aperture radar (SAR) tomography is a promising technique capable of resolving layovers. In this paper, new approaches based on a novel two-tier network aimed at robust and efficient detection of persistent scatterers (PSs) are presented. The calibration of atmospheric phase screen (APS) and the detection of PSs can be jointly implemented in the novel two-tier network. A residue-to-signal ratio (RSR) estimator is proposed to evaluate whether the APS is effectively calibrated and to select reliable PSs with accurate estimation. In the first-tier network, a Delaunay triangulation network is constructed for APS calibration and SPS detection. RSR thresholding is used to adjust the first-tier network by discarding arcs and SPS candidates (SPSCs) with inaccurate estimation, yielding more than one main network in the first-tier network. After network adjustment, we attempt to establish reliable SPS arcs to connect the main isolated networks, and the expanded largest connected network is then formed with more manual structure information subtracted. Furthermore, rather than the weighted least square (WLS) estimator, a network decomposition WLS (ND-WLS) estimator is proposed to accelerate the retrieval of absolute parameters from the expanded largest connected network, which is quite useful for large network inversion. In the second-tier network, the remaining SPSs and all the double PSs (DPSs) are detected and estimated with reference to the expanded largest connected network. Compared with traditional two-tier network-based methods, more PSs can be robustly and efficiently detected by the proposed new approaches. Experiments on interferometric high resolution TerraSAR-X SAR images are given to demonstrate the merits of the new approaches.

Monitoring Landslide Activity by Radar Interferometry Using Trihedral Corner Reflectors

In this paper we describe the application of differential radar interferometry for monitoring landslide slopes in the Sochi region under conditions of strong temporal decorrelation of the reflected signals from the underlying surface in images of the TerraSAR-X synthetic aperture radar. Measurements of the movement of the landslide surface became possible due to the use of steadily reflecting objects, e.g., specially designed trihedral corner reflectors with square plates having cut-off external corners. Radiophysical properties of the corners and the criteria for choosing their size and geometry are described in detail. The sensitivity of the position of the phase center of the signal scattered by a corner reflector to undesirable atmospheric phenomena (precipitation occurring inside the corner) is mentioned and substantiated. Based on an analysis of the results of processing four TerraSAR-X images, we show the possibility of measurements with millimeter accuracy in the X-band of the surface displacements of the observed landslide slope with the corner reflectors installed on its surface.

A Component-Based Multi-Layer Parallel Network for Airplane Detection in SAR Imagery

In this paper, a component-based multi-layer parallel network is proposed for airplane detection in Synthetic Aperture Radar (SAR) imagery. In response to the problems called sparsity and diversity brought by SAR scattering mechanism, depth characteristics and component structure are utilized in the presented algorithm. Compared with traditional features, the depth characteristics have better description ability to deal with diversity. Component information is contributing in detecting complete targets. The proposed algorithm consists of two parallel networks and a constraint layer. First, the component information is introduced into the network by labeling. Then, the overall target and corresponding components are detected by the trained model. In the following discriminative constraint layer, the maximum probability and prior information are adopted to filter out wrong detection. Experiments for several comparative methods are conducted on TerraSAR-X SAR imagery; the results indicate that the proposed network has a higher accuracy for airplane detection.

Time-Series 3-D Mining-Induced Large Displacement Modeling and Robust Estimation From a Single-Geometry SAR Amplitude Data Set

This paper presents a novel method for modeling and robustly estimating the time-series 3-D mining-induced large displacements from a single imaging geometry (SIG) synthetic aperture radar (SAR) amplitude data set using the offset-tracking (OT) technique (hereafter referred to as the OT-SIG). It first generates multitemporal observations of 3-D mining-induced displacements from the single-geometry SAR amplitude data set with the assistance of a prior model. Then, a functional relationship between mining-induced time-series 3-D displacements and the multitemporal 3-D deformation observations generated is constructed. Finally, the time-series 3-D displacements are robustly estimated based on the constructed function model using the M-estimator. The proposed OT-SIG provides a robust and cost-effective tool for retrieving time-series 3-D mining-induced large displacements, relaxing the basic requirement of the traditional method that at least two different viewing geometries’ SAR data are needed. Finally, we tested the proposed OT-SIG with descending TerraSAR-X SAR amplitude data set over the Daliuta coal mining area in China. The results show that the root-mean-square errors (RMSEs) of OT-SIG-estimated time-series displacements are about 0.22 and 0.11 m in the vertical and horizontal directions, respectively. These RMSEs are around 5.7% and 10.9% of the maximum in situ deformation measurements in the corresponding directions, which can meet the accuracy requirements of practical applications.

Physical-based soil moisture retrieval method over bare agricultural areas by means of multi-sensor SAR data

ABSTRACTThe main objective of this research is to develop, test and validate soil moisture retrieval method based on multi-source SAR (Synthetic Aperture Radar) data for bare agricultural areas. The Radardat-2, TerraSAR-X and Sentinel-1A SAR data were applied to retrieve soil moisture content in combination with the integral equation model (IEM) or calibrated integral equation model (CIEM). A straightforward inversion scheme was developed, which does not require the prior knowledge of surface roughness. The soil moisture content can be directly estimated using a look-up table (LUT) optimization method with multi-source SAR data as inputs. For validation purpose, in situ soil moisture content was measured during the period of SAR data acquisitions. The effectiveness and reliability of the soil moisture retrieval methods were evaluated based on the in situ measurements and cost function distribution graph. The experimental results indicate that the developed approach provided accurate soil moisture estimates with root mean square errors (RMSE) ranging from 0.047 cm3 cm−3 to 0.079 cm3 cm−3 over the experimental areas. The distribution graphs of the cost function demonstrate the uniqueness and convergence of the estimated results based on multi-source SAR data. Either IEM or CIEM was employed to estimate soil moisture content, more accurate results were obtained with Radarsat-2, TerraSAR-X and Sentinel-1A data as inputs. The experimental results preliminary illustrate that the multi-source SAR data are promising for soil moisture retrieval over bare agricultural areas. The novelty of the presented research can be summarized as two aspects. Firstly, the multi-sensor SAR with different incidence angle, different frequency and different polarization were combined to estimate soil moisture content by means of the physical-based methods. The combination of the multi-sensor SAR data can effectively solve the ill-posed problem of soil moisture retrieval using physical models. Secondly, the CIEM was utilized to establish the soil moisture retrieval model, which transforms the three unknown parameters to two unknown parameters. Furthermore, the convergence and uniqueness of the estimated soil moisture were validated through distribution graphs of the cost function.

Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately.

Investigating the Influence of Variable Freshwater Ice Types on Passive and Active Microwave Observations

Dual-polarized airborne passive microwave (PM) brightness temperatures (Tb) at 6.9 GHz H/V, 19 GHz H/V and 37 GHz H/V and spaceborne active microwave (AM) X-band (9.65 GHz VV, VH) backscatter (σ0) are observed coincident to in situ snow and lake-ice measurements collected over two lakes near Inuvik, Canada. Lake-ice thickness is found to be positively correlated with 19 GHz V emission (R = 0.67) and negatively with 19 GHz H emission (R = −0.79), indicating surface ice conditions influence microwave interaction. Lake ice types are delineated from TerraSAR-X synthetic aperture radar (SAR) images using the iterative region growing with semantics (IRGS) segmentation algorithm implemented in the MAGIC (MAp Guided Ice Classification) system. The spatial extent of derived ice type classes correspond well to in situ observations. The overall magnitude of emission at 19 GHz H and X-band VH σ0 increase with the scattering potential of associated ice types (grey/rafted ice). Transects of 6.9 GHz PM and 19 GHz PM exhibit positive relationships with VH σ0 over freshwater lake ice, with the greatest R coefficients at H-pol (R = 0.64, 0.46). Conversely, 6.9 GHz Tb and 19 GHz Tb exhibit negative R coefficients in regions of brackish water due to tubular bubble and brine inclusions in the ice. This study identifies congruency between PM and AM scattering mechanisms over lake ice for the purpose of identifying the influence of ice types on overall microwave interaction within the lake-ice system.

Evaluation of satellite-derived building height extraction by CFD simulations: A case study of neighborhood-scale ventilation in Hong Kong

Computational fluid dynamics (CFD) techniques, such as large-eddy simulation (LES), are widely used in urban ventilation studies. Unfortunately, realistic building data required in CFD studies are not always available. In this study, LES of urban ventilation is used to assess building height extraction from different satellite images. As a case study, three sets of digital elevation data extracted from satellite images in an urban area of Mong Kok, Hong Kong, are assessed. Simulations of velocity ratio from the LES model are first validated by wind tunnel measurements, yielding a recommended local roughness length of 0.02m. Wind characteristics in urban elevations extracted from two single satellite images and the fused result are then compared with those from realistic data in the same area, using identical LES settings. It is found that building height data retrieved from the WorldView-2 optical (stereo) images are of poor quality, as they underestimate higher buildings, which are deemed to be more important for pedestrian-level velocity ratios. The TerraSAR-X synthetic aperture radar (SAR) image and the fused results of SAR and stereo images are sufficient for CFD simulations of urban ventilation. This fusion provides a slight improvement over SAR images alone in reproducing urban geometric parameters, and it is superior in simulating more accurate wind profiles in the urban canopy.

Mixture Statistical Distribution Based Multiple Component Model for Target Detection in High Resolution SAR Imagery

This paper proposes an innovative Mixture Statistical Distribution Based Multiple Component (MSDMC) model for target detection in high spatial resolution Synthetic Aperture Radar (SAR) images. Traditional detection algorithms usually ignore the spatial relationship among the target’s components. In the presented method, however, both the structural information and the statistical distribution are considered to better recognize the target. Firstly, the method based on compressed sensing reconstruction is used to recover the SAR image. Then, the multiple component model composed of a root filter and some corresponding part filters is applied to describe the structural information of the target. In the following step, mixture statistical distributions are utilised to discriminate the target from the background, and the Method of Logarithmic Cumulants (MoLC) based Expectation Maximization (EM) approach is adopted to estimate the parameters of the mixture statistical distribution model, which will be finally merged into the proposed MSDMC framework together with the multiple component model. In the experiment, the aeroplanes and the electrical power towers in TerraSAR-X SAR images are detected at three spatial resolutions. The results indicate that the presented MSDMC Model has potential for improving the detection performance compared with the state-of-the-art SAR target detection methods.