HH Polarization Research Articles

Joint Prediction of Sea Clutter Amplitude Distribution Based on a One-Dimensional Convolutional Neural Network with Multi-Task Learning

Accurate modeling of sea clutter amplitude distribution plays a crucial role in enhancing the performance of marine radar. Due to variations in radar system parameters and oceanic environmental factors, sea clutter amplitude distribution exhibits multiple distribution types. Focusing solely on a single type of amplitude prediction lacks the necessary flexibility in practical applications. Therefore, based on the measured X-band radar sea clutter data from Yantai, China in 2022, this paper proposes a multi-task one-dimensional convolutional neural network (MT1DCNN) and designs a dedicated input feature set for the joint prediction of the type and parameters of sea clutter amplitude distribution. The results indicate that the MT1DCNN model achieves an F1 score of 97.4% for classifying sea clutter amplitude distribution types under HH polarization and a root-mean-square error (RMSE) of 0.746 for amplitude distribution parameter prediction. Under VV polarization, the F1 score is 96.74% and the RMSE is 1.071. By learning the associations between sea clutter amplitude distribution types and parameters, the model’s predictions become more accurate and reliable, providing significant technical support for maritime target detection.

Experimental Investigation of Meter-Level Resolution Radar Measurement at Ka Band in Yellow Sea

The backscatter characteristics of ocean surfaces are of great importance in active marine remote-sensing fields. This paper presents the high spatial and temporal resolution dual co-polarized (VV and HH) and cross-polarized (HV) Ka-band sea-surface backscattering measurements taken from the Yellow Sea research platform at incidence angles ranging from 30° to 50° and in the wind speed range from 5.8 to 8.6 m/s. The experimental results show that the backscattering coefficient in HH polarization is close to (or even surpassing) that in VV polarization within a wind speed range of 7.1 to 8.6 m/s for Ka band under high resolution at medium incidence angles (30°–50°). Further analysis of the 10-ms short-time observation samples found that the sea surface echoes in VV polarization are more sensitive to wave motions, exhibiting more complex scattering characteristics such as multi-peaks and reducing scattering energy, especially at high wind speeds and large incident angles. The Doppler velocity analysis also confirms that rapid ocean wave changes can be detected within a short observation period, especially in VV polarization. The research in this article not only demonstrates the high spatial and temporal resolution capabilities of Ka-band radar for ocean surface observation but also reveals its great potential in interpreting and inversing rapidly evolving marine phenomena.

Analysis of rain effects on China-France oceanography satellite scatterometer backscatter measurements

ABSTRACT The rotating fan beam scanning observation mechanism of China-France oceanography satellite (CFOSAT) scatterometer provides more comprehensive ocean measurements. However, its data quality is often impacted by rain contamination. In this study, we analysed the effect of rain on CFOSAT SCATterometer (CSCAT) backscatter measurements by collocating CSCAT data, the Integrated Multi-satellitE Retrievals (IMERG) rain data, and the wind reanalysis from European Centre for Medium-Range Weather Forecasts (ECMWF). The research findings indicate that, rain exhibits an intensification effect on backscatter at low wind speeds, but this effect gradually diminishes with increasing wind speeds, eventually transitioning to an attenuation effect at a wind speed between 10–20 m·s−1. At extremely high wind speeds above 20 m·s−1, the rain initially increases and then decreases CSCAT normalized radar cross section (σ 0) as the rain rate increases. The sensitivity of CSCAT σ 0 to rain rate decreases as the incidence angle increases. Its sensitivity to wind speed decreases as the rain rate increases. The azimuthal modulation becomes more pronounced with higher wind speeds and smaller incidence angles. However, rain weakens the asymmetry and anisotropy. Furthermore, the HH polarization exhibits a more pronounced susceptibility to rain compared to the VV polarization.

Monitoring Cover Crop Biomass in Southern Brazil Using Combined PlanetScope and Sentinel-1 SAR Data

Precision agriculture integrates multiple sensors and data types to support farmers with informed decision-making tools throughout crop cycles. This study evaluated Aboveground Biomass (AGB) estimates of Rye using attributes derived from PlanetScope (PS) optical, Sentinel-1 Synthetic Aperture Radar (SAR), and hybrid (optical plus SAR) datasets. Optical attributes encompassed surface reflectance from PS’s blue, green, red, and near-infrared (NIR) bands, alongside the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Sentinel-1 SAR attributes included the C-band Synthetic Aperture Radar Ground Range Detected, VV and HH polarizations, and both Ratio and Polarization (Pol) indices. Ground reference AGB data for Rye (Secale cereal L.) were collected from 50 samples and four dates at a farm located in southern Brazil, aligning with image acquisition dates. Multiple linear regression models were trained and validated. AGB was estimated based on individual (optical PS or Sentinel-1 SAR) and combined datasets (optical plus SAR). This process was repeated 100 times, and variable importance was extracted. Results revealed improved Rye AGB estimates with integrated optical and SAR data. Optical vegetation indices displayed higher correlation coefficients (r) for AGB estimation (r = +0.67 for both EVI and NDVI) compared to SAR attributes like VV, Ratio, and polarization (r ranging from −0.52 to −0.58). However, the hybrid regression model enhanced AGB estimation (R2 = 0.62, p < 0.01), reducing RMSE to 579 kg·ha−1. Using only optical or SAR data yielded R2 values of 0.51 and 0.42, respectively (p < 0.01). In the hybrid model, the most important predictors were VV, NIR, blue, and EVI. Spatial distribution analysis of predicted Rye AGB unveiled agricultural zones associated with varying biomass throughout the cover crop development. Our findings underscored the complementarity of optical with SAR data to enhance AGB estimates of cover crops, offering valuable insights for agricultural zoning to support soil and cash crop management.

River ice breakup classification using dual- (HH&HV) or compact-polarization RADARSAT Constellation Mission data

Ice jams and associated flooding during river ice breakup are seasonal hazards for many northern communities. Synthetic Aperture Radar (SAR) data enable timely monitoring of river ice conditions in inclement weather. River ice types and open water are discriminated during breakup using dual-polarized (HH&HV; DPH) and compact polarimetric (CP) C-band SAR imagery from the RADARSAT Constellation Mission (RCM). Study areas comprise nineteen locations on rivers in northern Ontario, the Northwest Territories, and Alberta, Canada. Rubble ice, sheet ice, and open water areas are sampled in RCM imagery using corroborating evidence from shoreline cameras and oblique airborne photography. Samples are obtained in the incidence angle range 19° to 48°, for open water with various wind speeds and flow conditions, and for rubble ice and sheet ice with varying surface roughness, snow cover, and wetness. Discrimination relies on a primary classification of rubble ice, sheet ice, and open water, and a secondary classification of ice roughness. The primary classification model development uses a recursive-partitioning machine-learning technique. Overall accuracies for the best DPH models are 83.8% to 89.6%, depending on image noise floor. DPH models use both HH and HV polarizations for low noise floor image modes, whereas only HH is used for higher noise floor image modes. The best CP model accuracy is 90.2%, using the RL, RR, and RVRH parameters. Secondary classification associates increasing ice roughness with increasing HH backscatter for DPH data, and with increasing RH backscatter for CP data. The angular dependencies of rubble ice or sheet ice are used to normalize backscatter, which is then divided into roughness categories. The DPH and CP classification models are named IceBC-DP and IceBC-CP, respectively. Qualitative analysis illustrates good overall ice type and open water classification. Confounding situations are mixed pixel issues, whitewater and wind roughening of water, moist snow microwave absorption, and superficial water on sheet ice. The development of robust methods for monitoring ice jam formation with RCM is an operational concern for departments within the Government of Canada and within Provincial and Territorial Governments.

Reducing out-of-band radar cross-section of metasurface-based radome composites via beam-scattering mechanism

In this work, a metasurface-based radome composite (MRC) with customized electromagnetic (EM) functions is proposed. By employing a beam-scattering mechanism, high transmission at 2–4 GHz and significant radar cross-section (RCS) reduction at 8–18 GHz are achieved. The MRC prototype features a sandwiched construction and is fabricated using quartz fiber-reinforced composite (QFRC) skins, polymethacrylimide (PMI) foam cores, and silver-based frequency selective surface (FSS) screens. The calculation, simulation, and measured results demonstrated that the transmission of the planar specimen is larger than −1 dB at 2–4 GHz and the RCS reduction is less than −10 dB at 8–18 GHz. The measured results also verified that in the HH and VV polarizations, the mono-static RCS of a square pyramid (SP) specimen at specific directions from 8 GHz to 12 GHz is significantly reduced. It is believed that our finding has great application potential in stealth radome technology.

Assessment of C-Band Polarimetric Radar for the Detection of Diesel Fuel in Newly Formed Sea Ice

There is a heightened risk of an oil spill occurring in the Arctic, as climate change driven sea ice loss permits an increase in Arctic marine transportation. The ability to detect an oil spill and monitor its progression is key to enacting an effective response. Microwave scatterometer systems may be used detect changes in sea ice thermodynamic and physical properties, so we examined the potential of C-band polarimetric radar for detecting diesel fuel beneath a thin sea ice layer. Sea ice physical properties, including thickness, temperature, and salinity, were measured before and after diesel addition beneath the ice. Time-series polarimetric C-band scatterometer measurements monitored the sea ice evolution and diesel migration to the sea ice surface. We characterized the temporal evolution of the diesel-contaminated seawater and sea ice by monitoring the normalized radar cross section (NRCS) and polarimetric parameters (conformity coefficient (μ), copolarization correlation coefficient (ρco)) at 20° and 25° incidence angles. We delineated three stages, with distinct NRCS and polarimetric results, which could be connected to the thermophysical state and the presence of diesel on the surface. Stage 1 described the initial formation of sea ice, while in Stage 2, we injected 20L of diesel beneath the sea ice. No immediate response was noted in the radar measurements. With the emergence of diesel on the sea ice surface, denoted by Stage 3, the NRCS dropped substantially. The largest response was for VV and HH polarizations at 20° incidence angle. Physical sampling indicated that diesel emerged to the surface of the sea ice and trended towards the tub edge and the polarimetric scatterometer was sensitive to these physical changes. This study contributes to a greater understanding of how C-band frequencies can be used to monitor oil products in the Arctic and act as a baseline for the interpretation of satellite data. Additionally, these findings will assist in the development of standards for oil and diesel fuel detection in the Canadian Arctic in association with the Canadian Standards Association Group.

Analytical Coherent Detection in High-Resolution Dual-Polarimetric Sea Clutter with Independent Inverse Gamma Textures

Polarization diversity has been widely used in maritime radars to improve target detection performance. Full utilization of the polarimetric characteristics of sea clutter is the key to designing effective polarimetric detectors. For high-resolution maritime radars, the HH-HV dual-polarization is an affordable and effective mode to monitor small targets, owing to the simple configuration of single-polarimetric transmit and dual-polarimetric reception and lower clutter powers at the HH and HV polarizations. Enlightened by the analytical coherent detector in compound-Gaussian clutter with inverse Gamma texture, this paper investigates dual-polarimetric coherent detection in dual-polarimetric compound-Gaussian clutter with independent inverse Gamma distributed textures. The analytical dual-polarimetric near-optimum coherent detector is derived, which is a fusion of the generalized likelihood ratio test linear threshold detectors (GLRT-LTDs) at the two polarizations. For short, it is referred to as the P-GLRT-LTD. It is proven that the P-GLRT-LTD is of constant false alarm rate with respect to the Doppler steering vector, scale parameters of textures, and speckle covariance matrices. Moreover, the thresholds of the P-GLRT-LTD are given analytically. Experiments using simulated sea clutter data with the estimated scale and shape parameters from the two measured intelligent pixel processing radar (IPIX) datasets and two measured IPIX datasets with test targets are made to compare P-GLRT-LTD with other existing dual-polarimetric coherent detectors. The results show that the P-GLRT-LTD attains the same detection performance as the existing best-performance detector. The P-GLRT-LTD has a lower computational cost than the existing best-performing one.

Utilizing Multi-Temporal ALOS/PALSAR Backscatter Data to Detect Deforestation in the Indonesian Tropical Forest

Deforestation detection mapping has been analyzed using Synthetic Aperture Radar (SAR) data from the ALOS PALSAR satellite. SAR is a promising tool for deforestation mapping in tropical forest regions because microwave energy can penetrate clouds. This study examined the effect of SAR polarization on the accuracy of deforestation mapping and the seasonal variation of radar backscattering values in Riau Province, Indonesia. Image differencing was used to determine the threshold value for distinguishing between forest and non-forest areas. The difference between HV (horizontal transmit-vertical receive) and HH (horizontal transmit-horizontal receive) polarization was used as the discriminant variable. The correlation between radar backscattering value and accumulated rainfall was also analyzed to assess the effect of seasonal change. The findings demonstrated that the accuracy of deforestation detection mapping using HV polarization outperforms that achieved with HH polarization. Moreover, seasonal variations in rainfall were observed to influence radar backscatter signatures in bare soil, acacia, and oil palm, though no such influence was detected in forested areas. The accuracy of deforestation detection mapping, employing the thresholding method, reached a rate of 92.21%. During the period from November 2007 to July 2009, forested areas in Riau experienced a reduction, decreasing to 6.77%, and further decreasing to 6.08% by October 2010.

Sea ice drift vector extraction based on feature matching using CS-1 Images

The study emphasizes the crucial role of sea ice drift in Arctic climate research and the protection of human activities. Employing C-SAR/01 (CS-1) imagery as the primary data source, the study assesses the effectiveness of the ORB feature matching algorithm for extracting Arctic Sea ice drift vectors. Additionally, it investigates the spatial distribution variations in the drift vectors extracted from the HH and HV polarization channels. The accuracy of the extracted drift vectors is validated using manually extracted sea ice drift data. Experimental results reveal a significant disparity in the number of drift vectors extracted from CS-1 HV polarization images compared to HH polarization images. The sea ice drift vectors extracted using the Oriented fast and Roasted Bried (ORB) operator demonstrate an average velocity error of less than 0.27 cm/s (0.31 km/d) and an average direction error of less than 5.66° in HV polarization images.

Inversion of Forest above Ground Biomass in Mountainous Region Based on PolSAR Data after Terrain Correction: A Case Study from Saihanba, China

Accurate retrieval of forest above ground biomass (AGB) based on full-polarization synthetic aperture radar (PolSAR) data is still challenging for complex surface regions with fluctuating terrain. In this study, the three-step process of radiometric terrain correction (RTC), which includes polarization orientation angle correction (POAC), effective scattering area correction (ESAC), and angular variation effect correction (AVEC), is adopted as the technical framework. In the ESAC stage, a normalized correction factor is introduced based on local incidence angle and radar incidence angle to achieve accurate correction of PolSAR data information and improve the inversion accuracy of forest AGB. In order to verify the validity and robustness of this research method, the full-polarization SAR data of ALOS-2 and the ground measured AGB data collected in the Saihanba research area in 2020 were used for experiments. Our findings revealed that in the ESAC phase, the introduction of the normalized correction factor can effectively eliminate the ESA phenomenon and improve the correlation coefficients of the backscatter coefficient and AGB. Taking the data of 25 July 2020 as an example, ESAC increases the correlation coefficients between AGB and the backscattering coefficients of HH, HV, and VV polarization channels by 0.343, 0.296, and 0.382, respectively. In addition, the RTC process has strong robustness in different AGB statistical models and different date PolSAR data.

High-Resolution Mapping of Mangrove Species Height in Fujian Zhangjiangkou National Mangrove Nature Reserve Combined GF-2, GF-3, and UAV-LiDAR

Mangroves as an important blue carbon ecosystem have a unique ability to sequester and store large amounts of carbon. The height of mangrove forest is considered to be a critical factor in evaluating carbon sink capacity. However, considering the highly complicated nature of the mangrove system, accurate estimation of mangrove species height is challenging. Gaofen-2 (GF-2) panchromatic and multispectral sensor (PMS), Gaofen-3 (GF-3) SAR images, and unmanned aerial vehicle-light detection and ranging (UAV-LiDAR) data have the capability to capture detailed information about both the horizontal and vertical structures of mangrove forests, which offer a cost-effective and reliable approach to predict mangrove species height. To accurately estimate mangrove species height, this study obtained a variety of characteristic parameters from GF-2 PMS and GF-3 SAR data and utilized the canopy height model (CHM) derived from UAV-LiDAR data as the observed data of mangrove forest height. Based on these parameters and the random forest (RF) regression algorithm, the mangrove species height result had a root-mean-square error (RMSE) of 0.91 m and an R2 of 0.71. The Kandelia obovate (KO) exhibited the tallest tree height, reaching a maximum of 9.6 m. The polarization features, HH, VV, and texture feature, mean_1 (calculated based on the mean value of blue band in GF-2 image), had a reasonable correlation with canopy height. Among them, the most significant factor in determining the height of mangrove forest was HH. In areas where it is difficult to conduct field surveys, the results provided an opportunity to update access to acquire forest structural attributes.

Radar signatures of sea lions at K‐band and W‐band

AbstractThe millimetre wave radar signatures of sea lions collected from three animals in the outdoor seal pool available at the Sea Mammal Research Unit in St Andrews in the Autumn of 2021 is reported. The objective is to study the radar amplitude and Doppler signatures of the animals when their full body or part thereof is above water, which is important for the application of autonomous marine navigation. The data was collected using 24 GHz (K‐band) and 77 GHz (W‐band) Frequency Modulated Continuous Wave radars with linear polarisation. It has been demonstrated that the sea lions were very clearly detected by the radars with Signal to Noise Ratio greater than 30 dB at a range of 40 m. The calculated modal radar cross section (RCS) of the sea lions in HH polarisation at 24 and 77 GHz vary from −48 to −26 dBsm and −48 to −28 dBsm respectively, corresponding to the different body parts and the amount of exposure to the radar beam. In VV polarisation, the modal RCS value range is from −49 to −26 dBsm and −49 to −22 dBsm respectively. The corresponding maximum RCS and the Cumulative Distribution Function results are also reported.

Glacier melt detection at different sites of Greenland ice sheet using dual-polarized Sentinel-1 images

ABSTRACT Synectic Aperture Radar (SAR) backscatter coefficient is sensitive to glacier surface physical characteristic changes, including the states of melting and refreezing, but it is also sensitive to incidence angle variation. This study explores the capability of monitoring Greenland Ice Sheet (GrIS) melting status with Sentinel-1 dual-polarized images by referring to Automatic Weather Station (AWS) records. Sentinel-1 SAR images at five coastal regions of the GrIS are obtained from 2017 to 2021. The backscatter coefficients are normalized to an incidence angle of 30° with an empirical model. Time series of five backscatter coefficients profiles covering AWS illustrates different patterns of the ice surface dielectric constant dynamics in different elevations. The wet snow radar zone shows clear backscatter coefficients decreasing during the melting seasons, but the bare ice radar zone behaves more complexly during the melting seasons. The numbers of melting days at different elevations are also derived for each profile based on −3 dB backscatter coefficient decrease of HH and/or HV polarization, showing the heterogeneous ablation processes over the GrIS. The daily maximum 2 m air temperature on two consecutive days (before and on the SAR acquisition day) exceeds 0°C, and the daily average 2 m air temperature exceeds −0.5°C on the SAR acquisition day that was recorded by the AWS finds good agreements with the −3 dB decrease of the backscatter coefficients, suggesting the GrIS surface melting can be well captured by dual-polarized Sentinel-1 C-band SAR images. The overall agreement and Kappa coefficients are mostly better than 0.85 and 0.70, respectively, for HH images and 0.80 and 0.60, respectively, for HV images, suggesting a better performance of the co-polarized image. High temporal resolution and wide-swath SAR sequence imagery provide suitable data sources for monitoring glacier surface melting-refreezing stats; further analysis is requested to quantitatively link the volume of melting with backscatter coefficient and other SAR data sources.

Sea Clutter Feature Prediction and Parameters Inversion Using Deep Learning Model

The characteristics of sea clutter in real marine environments in different sea areas play a vital role in military industry such as radar detection, remote sensing, SAR imaging, and situational awareness. In this paper, a deep neural network sea clutter model is proposed based on the sea clutter big data under the real marine environment to study the characteristics of sea clutter and parameter inversion. Based on the ERA-Interim reanalysis (2015-2017), a database of marine environmental elements in China's offshore waters was established, and a spatiotemporal prediction model for marine environmental elements was proposed to improve missing values. Considering the scattering mechanism of sea surface at different scales comprehensively, a large database of sea clutter time series of multi-scale real surface is established and comparison with the experimental data. Aiming at the coastal waters of China, the LSTM model and deep neural network (DNN) are used to establish the correlation model between marine environmental elements and sea clutter characteristics, and the prediction and parameter inversion of sea clutter characteristics based on sea clutter big data are studied. The results shown that the coefficients of determination of the predicted fitted curves for the HH and VV polarization amplitudes reach 0.9249 and 0.8872, respectively. The inversion of wave heights in different sea areas is the lowest in the South China Sea (accuracy rate of 78%) and the highest in the East China Sea (accuracy rate is 90%). The results of this paper can help improve the ability of sea surface remote sensing and sea clutter suppression.

Design of Ultrawideband RCS Reduction Metasurface Using Space Mapping and Phase Cancellation

An efficient optimizing method of ultra-wideband radar cross-section (RCS) reduction metasurface involve unit amplitude differences and mutual coupling effects is proposed in the letter. Multiple unit phase cancellation was used to broaden RCS-reduction bandwidth. The genetic algorithm was utilized to further design and select the optimal combination from various tiles. To improve the optimization efficiency and accuracy, the space mapping (SM) technique was exploited to refine the design process, which involves the interactions of scattering fields among tiles and subarrays in wideband. Meanwhile, the constraint of wide-angle oblique incidences is also considered in wideband low RCS design. An ultra-wideband RCS reduction checkboard meatsurface can be accomplished by fast optimization of low-cost coarse model and few parameter mapping courses between fine and coarse model. A 10dB RCS reduction for HH and VV polarizations under normal and other oblique incidence angles can be achieved from 5 to 40 GHz. Good agreements between simulation and experiment results are obtained.

Composite Backscatter Characteristics of Conductive/Dielectric Ships and Sea Surfaces with Breaking Waves under High Sea Conditions.

When a radar detects marine targets, the radar echo is influenced by the shape, size and dielectric properties of the targets, as well as the sea surface under different sea conditions and the coupling scattering between them. This paper presents a composite backscattering model of the sea surface and conductive and dielectric ships under different sea conditions. The ship scattering is calculated using the equivalent edge electromagnetic current (EEC) theory. The scattering of the sea surface with wedge-like breaking waves is calculated using the capillary wave phase perturbation method combined with the multi-path scattering method. The coupling scattering between ship and sea surface is obtained using the modified four-path model. The results reveal that the backscattering RCS of the dielectric target is significantly reduced compared with the conducting target. Furthermore, the composite backscattering of the sea surface and ship increases significantly in both HH and VV polarizations when considering the effect of breaking waves under high sea conditions at low grazing angles in the upwind direction, especially for HH polarization. This research offers valuable insights into optimizing radar detection of marine targets in varying sea conditions.

Sea Clutter Measurement Test and Amplitude Characteristics Analysis in the South China Sea Nearshore Area

To address the problem of sea clutter amplitude distribution characteristics varying with bandwidth, this paper obtains the raw data of clutter under different polarization methods of HH and VV in X and Ka bands based on the sea clutter measurement experiment carried out in June 2022 in the near-shore waters of South China Sea. For the data in X-band HH polarization mode, the goodness-of-fit test was conducted based on the classical models of Rayleigh, Weibull, log-normal and K distribution for the amplitude distribution characteristics of the sea clutter, and the variation of the estimated parameters of the distribution model with the bandwidth was analyzed. The test data show that: 1, with the increase of bandwidth sea clutter amplitude decreases; 2, with the increase of bandwidth Weibull distribution model parameters are decreasing trend, k distribution parameters are increasing trend; 3, the test conditions, the offshore sea clutter amplitude distribution is the optimal K distribution. The conclusions of this paper can provide a comparison and reference for the subsequent study on the characteristics of sea clutter distribution.

Effects of Wind Wave Spectra, Non-Gaussianity, and Swell on the Prediction of Ocean Microwave Backscatter with Facet Two-Scale Model

The image intensity of high-resolution synthetic aperture radar (SAR) is closely related to the facet scattering distribution. In this paper, the effects of wind wave spectra, non-Gaussianity of the sea surface, and swell on the distribution of the facet normalized radar cross section (NRCS) simulated by the facet two-scale model (TSM) are analyzed by comparing the simulated results with the Sentinel-1 SAR data, the Advanced Scatterometer (ASCAT) data, and the geophysical model function (GMF) at the wind speed range of 3–16 m/s, the wind direction range of 0°–360°, and the incidence angle range of 30°–50° under VV and HH polarizations. The results show that the Apel spectrum achieves a more consistent mean NRCS and NRCS distribution with the reference data at low incidence angles, while the composite spectra perform better at high incidence angles under VV polarization. Under HH polarization, the Apel spectrum always has a better performance. The upwind–downwind asymmetry of backscattering can be predicted well by the modified TSM, which is constructed by incorporating bispectrum correction into the conventional TSM. The distribution of the scattering simulated by the modified TSM deviates from the Gaussian distribution significantly, which is in good agreement with the Sentinel-1 data. Additionally, the introduction of swell widens the spread of the NRCS distribution, and the fluctuation range of the NRCS profile considering swell is larger than that without swell. All these changes caused by the introduction of swell make the distribution of the facet scattering more consistent with the Sentinel-1 data. Moreover, the scattering image patterns and scattering image spectrum of the Sentinel-1 data can be successfully simulated at various sea states with the consideration of swell.

Study on Ship Kelvin Wake Detection in Numerically Simulated SAR Images

To improve ship safety and increase ship concealment, we introduce a nonconvex regularization with a Cauchy-based penalty for discussing the influence of ship parameters and speckle noise in numerically simulated SAR images. First, the Kelvin wake geometry was modeled based on the classic theory of ship wave generation. Second, the scattering echo of the Kelvin wake was calculated using the two-scale method (TSM). Then, using the range-Doppler algorithm (RDA), the scattering echo data obtained by the TSM were processed to obtain the Kelvin wake in SAR images. Finally, the wake was reconstructed in the Radon domain using the Cauchy proximal splitting based on the forward–backward algorithm. The simulation results showed that Kelvin wakes were more easily detected in HH polarization with a large pitch angle and X-band, based on which the influence of ship parameters and speckle noise on the detection of ship wake in numerically simulated SAR images was discussed at different wind speeds. The research conclusions are of value to the development of ship wake stealth technology and the improvement of ship safety.