Multi-Angular SAR Statistical Properties Analysis and Man-Made Target Detection

3D scene reconstruction from single‐pass circular synthetic aperture radar (CSAR) data has been a research hot spot in various remote sensing applications. Among existing literature, radargrammetry techniques and model assistant methods are two dominant approaches. However, the performance of these methods is limited in practical applications: radargrammetry techniques obtain merely one height value along vertical; model assistant approaches only work when valid models are given. A single‐pass CSAR 3D scene point cloud reconstruction method is proposed. First, the connection between an object and its projections in multi‐aspect SAR images is established with no auxiliary model. On the basis of the connection, voxels are classified into inner voxels (voxels beneath the target profile) and outside voxels (voxels outside of the target profile). Discarded all the outside voxels, the remained inner voxels are combined to form the 3D point cloud of the target. It is similar to the sculpturing process from a raw material to a piece of craft. Finally, a 3D scene is retrieved through assembling all reconstructed targets into a unified coordinate system. The feasibility of the proposed method is validated by the Gotcha Volumetric SAR Data Set.

Although Synthetic Aperture Radar (SAR) can capture rich land cover information as a most important advanced technique in the field of international earth observation, the application effects still limited significantly. The reason is that the study of SAR imaging processing, SAR image processing and SAR applications are conducted respectively, the integrated study are lacked for parameter selection of SAR imaging processing and SAR image processing and targets identification of SAR application. Focusing on above science problems, the study for phase high fidelity model of high precision interferometric SAR, SAR non-stationary backscattering characteristics and cognition, SAR three-dimensional electromagnetic scattering modeling and its scattering mechanism and SAR multi-parameters optimization information extraction has been conducted. The typical natural distributed targets and man-made targets such as surface deformation, sea ice, building complex and collapsed building are selected, the method and application study oriented to SAR environmental parameters inversion are conduced, the main result and conclusion are as follows: (1) The overall theoretical frames of SAR information integrated processing was proposed; The simulation and ground validation system used to assess the effects of SAR information integrated processing was created; furthermore, the spaceborne SAR imaging algorithm with three steps focusing processing was proposed and realized the multi-modal integrated imaging processing and improved the imaging processing precision. (2) In terms of SAR information integrated processing of natural targets, focusing on phase high fidelity imaging processing of interferometric SAR surface deformation monitoring, an novel spaceborne SAR high fidelity simulation method based on stationary RCS and improved Goldstein SAR interferogram filter based on empirical mode decomposition were proposed. Focusing on multi-parameters optimization processing of sea ice types classification, the multi-channel spaceborne SAR sparse imaging method based on compressive sensing, Kalman filter for removal of scalloping and inter-scan banding in ScanSAR images and SVM sea ice classification method combined with sea ice concentration were proposed. (3) In terms of SAR information integrated processing of man-made targets, focusing on non-stationary backscattering from building complex, mitigation of azimuth ambiguities in spaceborne stripmap SAR iamges using selective restoration, SAR image despeckling by selective 3D filtering of multiple compressive reconstructed images and man-made target detection in urban areas based on a new azimuth stationary extraction method were proposed. Focusing on three-dimensional modeling and fast extraction of collapsed building, H-a-p method used for collapsed building extraction was proposed.

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method. 1

Perennial Firn Aquifers (PFA’s) facilitate meltwater storage within an ice sheet’s firn layer. They have been extensively mapped across the Greenland Ice Sheet, largely using Operation Ice Bridge and Sentinel-1 data. However, in Antarctica, observations of PFA’s are limited to the Antarctic Peninsula, where the combination of high accumulation and ablation aids in the formation and insulation of extensive sub-surface meltwater reservoirs. On ice shelves, PFA’s have the potential to drive ice-shelf damage via hydrofracture, and it is therefore crucial that we have a better understanding of their presence beyond the Antarctic Peninsula. To begin to improve our understanding for PFA’s elsewhere in Antarctica, we conduct a study on the Nivlisen Ice Shelf, an ice-shelf often characterised by extensive surface meltwater networks in Dronning Maud Land, East Antarctica. In addition to high rates of ablation, Nivlisen Ice Shelf also experiences high accumulation rates, making the ice-shelf a good candidate for the formation of PFAs. To investigate this theory, we utilise a method previously applied on the Greenland Ice Sheet, and exploit the low backscatter values returned in C-band synthetic aperture radar (SAR) data to detect potential PFA’s. C-band SAR data is obtained from Sentinel-1 and RADARSAT Constellation Mission (RCM), and is complemented with L-band SAR imagery. With both the NASA-ISRO Synthetic Aperture Radar (NISAR) and Copernicus ROSE-L satellites planned for future launches, we hope that our work will allow us to better understand the value of combined C- and L- band research for for studies of buried meltwater across both the Greenland and Antarctic Ice Sheets.  

The goal of this research was to evaluate the effect of Arctic climate change on the surrounding land area. In this research, we investigated a method to monitor the ice condition of shallow lakes in the Arctic region using C-band synthetic aperture radar (SAR) data and provide a strategy to evaluate the effect of climate change on the Arctic land area. A method to monitor the ice condition of these lakes was investigated using the SAR data acquired by Sentinel-1 from 2015 to 2018. We confirmed that the C-band SAR data from Sentinel-1 could monitor the ice condition of shallow lakes in northern Alaska. The backscattering threshold between floating and grounded ice was -14.8 to -16.8 dB depending on the incidence angle. We could also successfully monitor the growth of floating ice by using the interferometric SAR approach.

Circular synthetic aperture radar (CSAR) has attracted much attention in the field of high-resolution SAR imaging. However, the CSAR image focusing is affected by the motion deviations of platform. In the processing of experimental CSAR data to deal with motion errors, the main way is using setup calibrators, which restricts its widespread applications. In this paper, based on the estimation of motion errors, an autofocus CSAR imaging strategy is proposed without using any setup calibrator. The first step is to split the entire aperture into several subapertures, the second step is to process the data in subapertures with an autofocus backprojection method, and the last step is to obtain the final CSAR image by merging the subimages obtained from the subaperture processing. The CSAR data processing results prove that the proposed strategy can remove the motion errors accurately and acquire well-focused CSAR images.

Over the past decade, synthetic aperture radar (SAR) sensors have demonstrated their ability to provide accurate wind speed and direction measurements in the ocean. Although SAR capability to image the sea surface at a suitable spatial resolution, SAR wind field retrieval in marine coastal areas still remains an open issue. The aim of this work is to assess the potential and limits of the Bayesian approach to the C-band NRCS SAR inversion problem for the retrieval of wind fields in marine coastal areas. A dataset of 139 SAR images gathered by the European ERS-2 and ENVISAT satellites during 2007 was analyzed to estimate the sea surface wind vectors over three selected coastal sites in the Mediterranean Sea, offshore Puglia region (Italy). The sites were instrumented to monitor marine and environmental parameters, including wind intensity and direction for further comparison with SAR wind estimations. Results discussed in this study show that the statistical approach in the retrieval of SAR wind speed can be applied in coastal areas with performances comparable with those obtained off-shore. In contrast, the SAR inversion procedure was not able to retrieve wind direction with the same accuracy.

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.

Drained thermokarst lake basins accumulate significant amounts of soil organic carbon in the form of peat, which is of interest to understanding carbon cycling and climate change feedbacks associated with thermokarst in the Arctic. Remote sensing is a tool useful for understanding temporal and spatial dynamics of drained basins. In this study, we tested the application of high-resolution X-band Synthetic Aperture Radar (SAR) data of the German TerraSAR-X satellite from the 2009 growing season (July–September) for characterizing drained thermokarst lake basins of various age in the ice-rich permafrost region of the northern Seward Peninsula, Alaska. To enhance interpretation of patterns identified in X-band SAR for these basins, we also analyzed the Normalized Difference Vegetation Index (NDVI) calculated from a Landsat-5 Thematic Mapper image acquired on July 2009 and compared both X-band SAR and NDVI data with observations of basin age. We found significant logarithmic relationships between (a) TerraSAR-X backscatter and basin age from 0 to 10,000 years, (b) Landat-5 TM NDVI and basin age from 0 to 10,000 years, and (c) TerraSAR-X backscatter and basin age from 50 to 10,000 years. NDVI was a better indicator of basin age over a period of 0–10,000 years. However, TerraSAR-X data performed much better for discriminating radiocarbon-dated basins (50–10,000 years old). No clear relationships were found for either backscatter or NDVI and basin age from 0 to 50 years. We attribute the decreasing trend of backscatter and NDVI with increasing basin age to post-drainage changes in the basin surface. Such changes include succession in vegetation, soils, hydrology, and renewed permafrost aggradation, ground ice accumulation and localized frost heave. Results of this study show the potential application of X-band SAR data in combination with NDVI data to map long-term succession dynamics of drained thermokarst lake basins.

A note on radar signatures of hydrometeors in the melting layer as inferred from Sentinel-1 SAR data acquired over the ocean

As important man-made targets, bridges have been a major focus of Synthetic Aperture Radar (SAR) image interpretation, and many researchers have developed methods for bridge detection. The core frameworks of these methods are analogical, a river is first extracted, and a water bridge is detected based on the positional relationship between the river and bridge. However, existing bridge detection methods relying on river extraction; cannot be utilized detect land bridges. This is because the background environment under a bridge is land, not river, which has different scattering characteristics and shape layouts. As such, the traditional method for extracting rivers is not suitable for extracting land background, and it is impossible to locate a bridge based on prior knowledge of its location of. To resolve this problem, in this study, we propose a land bridge detection method based on polarized Circular SAR (CSAR) images. In our proposed method, the Circular Polarization Entropy (CPE) of an observed scene is introduced to separate possible bridge targets from a land background (In our experiment, the average CPE of the bridge is 0.4018, and that of the land background is 0.7819; thus there is a clear difference between the bridge and background). False targets are removed based on the difference in the polarization entropy variance features of the bridges and other ground objects; and the size characteristics of the bridges. Finally, accurate extractions of land bridges are obtained based on the geometric characteristics of the bridges. Experimental results based on real airborne L-band polarized CSAR data verify the correctness of the theoretical analysis and effectiveness of the proposed method.

Soil moisture retrieval over irrigated grassland using X-band SAR data

This paper describes a modeling approach to interpret the C-band synthetic aperture radar (SAR) data from wheat canopies as provided by European Remote Sensing (ERS) satellites, RADARSAT, and the forthcoming Environmental Satellite/Advanced Synthetic Aperture Radar (ENVISAT/ASAR) satellite. At a first step, the results of a first-order modeling were compared to ERS data and scatterometer data over the growing season at two different test sites. The prediction by first-order approach was in disagreement with the data from stem extension stage to soft ripening stage. The first-order approach was found to overestimate the attenuation at vertical (V) polarization, resulting in a predicted backscattering coefficient one order of magnitude lower than that observed by the SAR system. To improve the prediction, a multiple-scattering modeling based on numerical solution of multiple-scattering Foldy-Lax equation was used. The multiple-scattering modeling provides better backscatter estimates at vertical-vertical (VV) polarization for both test sites. Then, the model is used to derive the prevailing interactions mechanisms at horizontal-horizontal (HH) and VV polarizations and 23/spl deg/ and 40/spl deg/ of incidence angle. Finally, the retrieval of crop parameters from C-band SAR data is addressed.

Climatological and hydrological investigations in mountainous regions frequently require delineation of the areas covered by snow and glacial ice. Active microwave sensors can discriminate snow and glacier from other targets, are effective under all weather conditions, and have a spatial resolution compatible with the topographic variation in alpine regions. Using data acquired with the NASA AIRSAR, which operates at three frequencies and four combinations of polarization, we examine the usage of synthetic aperture radar (SAR) to map snow- and glacier-covered areas. In order to assess the available SARs that operate from satellites, we analyze single-frequency, single-polarization data, and we compare our results with images from the Landsat Thematic Mapper obtained under clear conditions only a few days after the SAR flight. Single polarization, C-band (5.3 GHz) SAR data can discriminate between areas covered by wet snow from those that are ice-free, but do not easily separate glacier ice from snow and rock. L-band SAR (1.25 GHz) data cannot discriminate between snow and glacier ice. The accuracy (74%) of SAR is high enough to justify its use as the data source in areas that are too cloud-covered to obtain reliable data from the thematic mapper.

A study was conducted to assess the potential of C-band synthetic aperture radar (SAR) data to determine the snow water equivalent (SWE). A multitemporal (three winters) SAR data set was obtained using the Convair-580 from the Canada Centre for Remote Sensing (CCRS) over a watershed in the Appalachian Mountains in Southern Quebec, Canada. The SAR data were relatively calibrated using extended targets (coniferous stands). Extensive ground measurements were done simultaneously to each of the seven flights, in order to measure the snow cover characteristics (depth, density, SWE, liquid water content, temperature, and dielectric profiles) as well as the soil characteristics (moisture, temperature). To estimate the SWE of a given snowpack, a model which links the scattering coefficient to the physical parameters of the snow cover and the underlying soil has been developed. The model is based on the ratio of the scattering coefficient of a field covered by snow to the scattering coefficient of a field without snow. The analysis has revealed that volume scattering from a shallow dry snow cover (SWE<20 cm) is undetectable. The backscattering power is dominated by soil surface scattering, the latter varying with the decrease of liquid water content in the surface layer with decreasing soil temperature below 0/spl deg/C. Then, the scattering ratio decreases proportionally to the dielectric constant of the soil in winter. Furthermore, a unique relationship for three acquisition dates has been found between the thermal resistance, R, of the snow pack and the backscattering power ratio. Then, the spatial distribution of the power ratio should depict the spatial distribution of R, given spatially uniform climatological conditions over the study area. Since linear relationships between SWE and R have been observed, it should be possible to estimate the SWE of shallow dry snow cover with C-band SAR data using few ground truthing data in an open area when the soil is frozen.