This paper investigates the effect of current-wave interaction on the ocean surface current (OSC) retrieval under the DCA framework using an improved Doppler radar imaging model (IDopRIM). Both numerical simulation data and a real SAR observation image in scenarios involving ocean internal waves (IW) are utilized for analyses. Experimental results suggest that the contribution of current-wave interaction to the sea surface Doppler velocity cannot be ignored for the IW ocean surface with rapidly varying currents. Under certain conditions, the relative errors of the OSC retrieval due to neglecting such a contribution could be larger than 30%.

This study proposed an improved Doppler radar imaging model (IDopRIM) to address the overestimation of the original DopRIM and produce better accuracy for investigating the current-wave interaction effect on ocean surface current (OSC) retrieval. The IDopRIM’s performance, with the root-mean-square (rms) errors mostly under 0.3 m/s, is validated against the empirical model and actual measurements. It shows a notable improvement over the original model in which the maximum deviation could be up to about 1.5 m/s at moderate-to-high wind speeds and upwind direction for HH polarization. Furthermore, this study uses both numerical simulations and real synthetic aperture radar (SAR) imagery to assess the impact of current-wave interaction on OSC retrieval, especially in scenarios involving ocean internal waves (IWs), using the Doppler centroid anomaly (DCA) method incorporated with the proposed IDopRIM. The results underscore the significance of incorporating current-wave interactions in OSC retrieval for IW conditions, revealing that neglecting these interactions can result in relative errors of over 30% in certain cases.

Empirical mode decomposition (EMD) is a commonly used method for retrieving internal wave (IW) parameters. However, the EMD-based method often suffers from the issue of mode mixing in practical retrievals. In this paper, a robust IW parameters retrieval method based on the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. The method is tested on an ERS-1 SAR image which clearly captured an IW over South China Sea. Also, the typical EMD method is adopted for comparison. The experimental results indicate that the proposed method can effectively address the issue of mode mixing, and obtain satisfying accuracy of the retrieved IW amplitudes and half-wave widths.

This paper numerically investigates the spatial ergodicity of radar scattering from randomly rough ocean surface. Based on the accurate full-wave multi-level steepest decent-sparse matrix canonical grid (MLSD-SMCG) method and Monte Carlo simulation, we simulate the L-band normalized radar scattering coefficients from one-dimensional (I-D) rough ocean surfaces with different radar illumination sizes. According to the simulation results, it is found that: For the scattering angle less than 85°, the normalized bistatic radar scattering coefficient from ocean surface with radar illumination size no less than $16\lambda$ has good spatial ergodicity. Also, the emissivities from ocean surfaces with sizes exceeding $64\lambda$ manifest spatial ergodicity if the measurement accuracy of emissivity is less than the order of magnitude of 10 −4 .

A unified model for L-band fully polarimatric ocean scattering and emission with directional asymmetries is proposed in this paper. The model framework is based on the two-scale polarimetric scattering model. Three wave spectra, i.e., the Durden-Vesecky spectrum, Elfouhaily Spectrum and KHCC03 spectrum, are employed for to account for the surface slopes of large-scale waves, spectrum of small-scale waves, and wave breaking effects, respectively. For the foam contribution for ocean emission at high wind speeds, the Anguelova and Gaiser, 2013 (AG13) foam emission model and a newly proposed foam coverage model are utilized. The model is validated against the fully polarimetric measurements of ocean scattering and emission of the Aquarius and SMAP mission. Numerical results show that the unified model can well simulate the opposite anisotropies of both ocean scattering and emission at low and high wind speeds. The model accuracies on directional properties of ocean scattering and wind-driven emission are less than of about 1 dB and 0.15 K, respectively.

Ocean front can influent the distribution of sea material and it has an impact on underwater acoustic communication. Thus, the detection of ocean front is very important. In this paper, the simple linear iterative clustering (SLIC) superpixel segmentation approach is applied to ocean front detection in SAR images, which can suppress the influence of coherent speckle noise and extract the fuzzy boundary from high-resolution SAR images. The results show that this method can extract ocean front with one pixel width and the extraction of ocean front is in good agreement with human visual interpretation. This method provides a new idea to extract ocean fronts from SAR images.

Ship detection using polarimetric synthetic radar (PolSAR) imagery attracts a lot of attention in recent years. Most notably, the detector polarimetric notch filter (PNF) has been demonstrated to be effective for ship detection in PolSAR imagery, which gives excellent performances. In this work, a mathematical form of one new PNF (NPNF) based on physical mechanisms of targets and clutter is further developed for partial targets. The different mechanisms have been revealed based on the projection matrix. The experimental results including simulated and measured data demonstrate that the NPNF exhibits a better performance than the original PNF.

In order to detect ships from the imagery of polarimetric synthetic aperture radar (PolSAR), a neighborhood polarimetric covariance matrix (for simplicity, we call it [$N$] hereinafter) was recently constructed. However, its calculation process is time-consuming and the backscattering heterogeneity near ship edges is also not well considered. For curing these shortcomings, we here propose two novel superpixel-based neighborhood polarimetric covariance matrices. In brief, the first matrix denoted by [SN] uses the simple linear iterative clustering (SLIC) to yield superpixels, whereas in the second matrix denoted by [GN], the gradient operator Sobel is adopted to obtain superpixels. Based on these two different kinds of superpixels, then, two different feature vectors$v_{\text {SN}}$and$v_{\text {GN}}$are separately built to compute [SN] and [GN]. Experiments performed on the real PolSAR datasets show that, compared to [$N$], [SN] and [GN] can improve the performance of the polarimetric whitening filter (PWF) more significantly and the time consumptions of calculating [SN] and [GN] are both much less.

Synthetic aperture radar (SAR) plays an essential role in earth observation and projection due to its capability to penetrate clouds, which makes it possible to monitor terrestrial surfaces under all weather conditions. Multiplicative noise often occurs in the SAR signal, hampering the retrieval of information from SAR imagery. Convolutional neural networks (CNNs) have been used in many computer vision tasks and are helpful in image denoising. However, current CNN-based denoising approaches inevitably lead to a “washed out” effect that loses spatial details. Another limitation is that most typical CNN-based denoising models require a noise-free image for training. To address these issues, we propose a novel end-to-end self-supervised SAR denoising model: Enhanced Noise2Noise (EN2N), which can be trained without a noise-free image. To enhance the quality of the result images, the perceptual features from a pre-learned CNN are introduced to restore the spatial details by a hybrid loss function. Experiments show that our proposed method outperforms the typical denoising methods in terms of noise reduction and feature preservation based on image quality metrics. Also, the new hybrid loss could enhance the spatial details significantly. The good performance maintains the robustness throughout time, which reduces the uncertainty in time-series SAR caused by random noise. Benefiting from optimization of graphics processing unit (GPU) and multi-threading, the proposed method has higher computation efficiency than traditional methods. This study demonstrates the great potential of using our self-supervised deep learning approaches for SAR image denoising in the future.

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method.

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available athttps://github.com/huizhangyang/BSF.

Superpixel segmentation approaches for polarimetric synthetic aperture radar (SAR) images have only been studied in recent years. Simple linear iterative clustering (SLIC) is a simple and efficient superpixel segmentation method, first proposed for optical images. It basically includes three implementation steps, i.e., initialization, local$k$-means clustering, and postprocessing. The challenge of applying SLIC to polarimetric SAR images lies in constructing the effective spatial and feature similarity and proposing the efficient segmentation procedure. In this study, to address both issues, we modify the SLIC clustering function to adapt the characteristics of polarimetric statistical measures. A new initialization method is proposed, which exploits the image gradient information to produce robust cluster centers. Furthermore, in an effort to give a comprehensive comparison and provide a fair assessment of the feature similarities for polarimetric SAR imagery, four classic statistical distances, among which two were not studied along with the SLIC previously, are embedded in the modified clustering function. The proposed method is validated by comparing with state-of-the-art SLIC-based algorithms and also the Ncut and TurboPixel algorithms. Experiments on extensive polarimetric SAR data sets show that the proposed method can significantly improve the segmentation results, with producing better boundary adherence and compact as well as uniform superpixels. We also obtain distinct conclusions that are different from the existing studies when investigating the performances of the statistical measures.

The wind direction asymmetries of the fully polarimetric ocean emissivity at L-band are studied using a semi-theoretical approach. The model is tuned and validated against Aquarius and SMAP observations, which incorporates the two-scale polarimetric ocean emission model, an improved directional wave spectrum, the AG13 foam emissivity model and a novel foam coverage model. Comparisons of model simulations to satellite data show that the overall accuracy of model could reach 0.2 K. The average standard deviations of the differences of polarimetric brightness temperature between observations and model are less than about 0.16 K for wind speeds up to 15 m/s. The negative and positive upwind-crosswind (UC) asymmetries of L-band ocean emission and the transition of the phase signatures in terms of wind speed are well illustrated for all polarizations by the proposed model. The directional variations of L-band brightness temperature for the third Stokes parameter show less sensitivity to wind speed with the increase of observation angles.

The effects of ocean spectrum truncation on the sea clutter distribution properties in numerical simulations are studied using a recently developed full-wave method, i.e., the multilevel steepest decent - sparse matrix canonical grid (MLSD-SMCG) method and the KHCC03 spectrum. Two types of ocean surface profiles are generated for Monte Carlo simulations based on the full and truncated spectra at the wind speed of 10 m/s. The surface profiles generated by the truncated spectrum have lengths about 1/6 of those using full spectrum. 1000 realizations are conducted for each type of profiles. The simulations are illustrated at L-band (1.4 GHz) and the incidence angle is 40°. For the simulated far-field scattering fields and normalized radar cross sections (NRCS), we use the K-distribution model to fit the probability density functions (PDF) of the amplitude and backscatter of clutters. It is found that spectrum truncation has non-negligible effects on the distribution characteristics of sea clutter in the numerical simulations, particularly for the amplitude distributions. The fitted PDF indicates that the simulated sea clutter using truncated spectrum has more small values compared with that using full spectrum.

Launched on June 5th, 2019, Bufeng-1 twin satellites that carry the Chinese first generation spaceborne GNSS-R instruments started using reflected GNSS signals to perform earth observation. By utilizing the Bufeng-1 Normalized Bistatic Radar Cross Section (NBRCS), the preliminary results show that BuFeng-1 has a high agreement compared with ECMWF ERA5 or ASCAT observations in the low-to-moderate wind speed range. In this paper, the measurements of Bufeng-1 will be aligned with the hurricanes observed by Stepped Frequency Microwave Radiometer (SFMR) mounted on NOAA aircraft during 2019 Hurricane Season in a certain spatial-temporal matchup criteria. Then, a power function relationship between Bufeng-1 NBRCS and wind speeds under severe sea states is illustrated in the first time. This study is presented to discuss the limitations and issues for the future research of new version of wind speed product of Bufeng-1 mission.

In a recent work, a neighborhood polarimetric covariance matrix [N] was proposed to detect ships from polarimetric SAR (PolSAR) imagery. However, its computational complexity is extremely high. Besides, the heterogeneity surrounding ship edges is also not well considered in [N]. To cure these draw-backs, we construct a novel superpixels-based neighborhood polarimetric covariance matrix [SN] in this paper. Specifically, the simple linear iterative clustering (SLIC) is first used to obtain superpixels. Then, the vector vmeancorresponding to the mean value of superpixel is further computed so as to characterize the neighborhood information of each pixel in superpixel. Finally, by combining the original scattering vector v and vmeantogether, the vector t12is built to calculate [SN]. The experiment tested on one L-Band ALOS PolSAR imagery shows that i) the polarimetric whitening filter derived from [SN] (i.e., PWFSN) has a better detection performance than that derived from [N] (i.e., PWFN); ii) the calculation process of [SN] takes much less time than that of [N].

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

Electromagnetic scattering and emission from 1-D rough surfaces with multiple elevations are studied using full-wave simulations. Both the root-mean-square (rms) heights and the surface length are large compared to the wavelength. A novel multilevel steepest decent-sparse matrix canonical grid (MLSD-SMCG) method is proposed to address limitations in the original SMCG. The uniform Nystrom method and neighborhood impedance boundary condition (NIBC) are also incorporated in solving the dual surface integral equations (SIEs) of the method of moments (MoM). Simulation results are illustrated at L-band for soil and ocean surfaces. The surface rms heights and lengths are up to 1.43 and 243.8 m corresponding to 6 and 1024 wavelengths at 1.26 GHz, respectively. For ocean surfaces, the wind speeds up to 20 m/s are considered, and the entire spectrum is included to capture all relevant surface length scales. Numerical results indicate the proposed approach is computationally efficient and accurate. Energy conservation checks in simulations are at $10^{-4}$ for ocean scattering and emission. Also, the effects of wind-driven roughness on ocean emissivity are further investigated using the proposed approach in terms of wind speed and observation angle for both polarizations.

To achieve the demand of earth observation with high spatiotemporal resolution and large scale, a novel optical imaging system is planning to equip on Chinese next generation geo-stationary earth orbit (GEO) satellite. The primary mirror of camera is made of a set of thin films and adopts the diffraction imaging mechanism. A series of ground experiments have been carried out using a thin-film camera with 80 mm aperture for the technical verification. The inherent chromatic aberration due to diffraction imaging appears in the obtained data. To address the issue, a color restoration algorithm by matching, tailoring and linearly stretching the histograms is proposed in this paper. Experimental results show the proposed approach has good performances in color restoration of the diffractive optical images. The effectiveness and robustness of the algorithm are also assessed with various color deviation indexes.

We employ the second-order small slope approximation (SSA-II) and the method of moment (MoM) to investigate the roughness scale effect on ocean radar scattering in both three- (3-D) and two-dimensional (2-D) cases. Various roughness scales of the ocean surface are represented by truncating the Kudryavtsev wave spectrum. Criteria of full spectrum truncation are proposed for the numerical simulations of ocean scattering. Numerical results are illustrated in fully bistatic configuration at L- and C- bands. It is found that short waves with wavenumber larger than 316 rad/m have little effects on ocean scattering. The large-scale waves put more effects on scattering in the forward directions, particularly for large incidence angles. For numerical simulations of ocean scattering with incidence angle less than 60°, using small surface profiles with size about 1/6 of those accounting for full spectrum yields result with errors less than 2dB.

Accurate calculations of electromagnetic scattering and emission from ocean surfaces with large sizes and root-mean-square (RMS) heights are still a challenge for the fullwave numerical methods. The widely used sparse matrix canonical grid (SMCG) method is only efficient for small and moderate roughness surfaces. This is because of the limitation of approximations in the far-field expressions using Taylor series expansion. In this paper, a novel multilevel steepest decent-sparse matrix canonical grid (MLSD-SMCG) method is proposed to address this issue. The proposed approach is implemented with the method of moment (MoM) in solving the dual surface integral equations (SIEs). Simulation results are illustrated at L-band for ocean surfaces with wind speed up to 20 m/s. The entire spectra are involved to capture all scales of waves in simulations. Thus the surface root-mean-square (RMS) heights and lengths are up to 3.82 and 1024 wavelengths of 1.26 GHz, respectively. Numerical results indicate the proposed approach is computationally efficient and accurate. Energies in simulations are conserved to the order of 10-4for ocean scattering and emission at various wind speeds.

The effects of sea surface temperature (SST) on ocean radar backscattering and wind retrieval are investigated using the second-order small slope approximation (SSA-II) model. Impact factors are parameterized and built into an SST-enhanced KHCC03 spectrum. By employing the Monin-Obukhov similarity theory (MOST), the air-sea interaction is considered in the analyses. Under the neutral condition, the SST effects on wind retrieval cannot be neglected at L-band and the seawater permittivity and air density are the dominant factors. For C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds. At Ku-band, the SST-related factors besides the air density and seawater viscosity can also significantly affect the ocean backscattering.

We use full-wave simulations of rough surfaces to calculate the emissivities of a polar ocean between 0.5 GHz and 2 GHz for applications in ocean salinity. High accuracy is required because the emissivity variations between flat and rough surfaces are at the order of magnitude of 10-3. In addition, the changes of emissivity due to salinity change are only about 4.2 × 10-4/psu to 2.6 × 10-3/psu for 0.5-2 GHz over the polar region. An enhanced method is proposed by combining neighborhood impedance boundary condition (NIBC), Nystrom, and sparse matrix canonical grid (SMCG) to solve the dual surface integral equations (SIEs) using the method of moments (MoM). To simulate large-scale ocean waves using a complete ocean spectrum, the surface root-mean-square (RMS) heights are up to 22.5 cm corresponding to 1.5 times wavelength of 2 GHz. Thus, the surface lengths used are up to 333 wavelengths to account for the large RMS height. Simulation results are illustrated for 0.5-2 GHz. We show that the energy conservations are obeyed to 10-4. The effects of roughness and salinity on broadband polar ocean emissivity are studied with the proposed approach. Numerical simulations show that the sensitivity to salinity is still preserved in spite of roughness.

The roughness scale effects on ocean radar scattering are studied using the second-order small slope approximation (SSA-II) and the method of moments (MoM). The KHCC03 spectrum is employed to represent 2-D and 1-D sea surfaces in the above two scattering methods, respectively. Criteria of full spectrum truncation are proposed for the numerical simulations of ocean scattering. Numerical results are illustrated in fully bistatic configuration at L- and C-bands. It is found that scattering at higher frequency is relatively more sensitive to the small-scale roughness but less sensitive to the large-scale roughness. At L- and C-bands, short waves with wavenumber larger than 316 rad/m have little effect on ocean scattering. The large-scale waves put more impacts on scattering in the forward directions, especially for large incidence angles. Other than the specular direction, the effects of large-scale roughness on ocean scattering are in general smaller at VV-pol than HH-pol. The bistatic scattering at cross polarizations is less sensitive to the roughness scale as compared to the copolarizations. For numerical simulations of ocean scattering with incidence angle less than 60°, using small surface profiles with size about 1/6 of those accounting for full spectrum yields results with errors less than 2 dB. Results also indicate that the incoherent parts dominate the scattered power from ocean surfaces with large-scale roughness.

In passive microwave remote sensing, the change of ocean emissivity is very small due to the roughness and salinity change, particularly for the polar region. This puts forward a strict requirement of the simulation accuracy. The ultra-wideband software-defined microwave radiometer (UWBRAD) is developed to measure the ocean salinity over polar region using wide-frequency microwave from 0.5 GHz to 2 GHz. In this paper, in order to meet the high accuracy requirement for sea surface salinity detection with UWBRAD, the Nystrom method is used in solving surface integral equations with moment method (MoM). A neighborhood impedance boundary condition (NIBC) technique is also applied to solve the matrix equation efficiently. Moreover, to save the memory and further accelerate the calculation speed, the sparse matrix canonical grid (SMCG) method is firstly combined with Nystrom/NIBC. Simulation results are illustrated for 0.5-2 GHz and show that emissivities using Nystrom/NIBC combined with SMCG are accurate to the magnitude of 10-4, which meet the accuracy requirement of retrieving polar ocean salinity within 0.2 psu.

A semi-empirical model is proposed by merging an improved directional spectrum into the advanced integral equation method (AIEM) in this paper. By establishing a new angular spreading function (ASF), this improved directional spectrum provides a better description of wave directionality, especially over wavenumber range from short-gravity waves to capillary waves. Based on this scattering model, the features of L-band ocean surface backscatter are studied. A unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range that was recently observed is interpreted and simulated first. The model is validated against Aquarius/SAC-D observations at the L band and the geophysical model functions (GMFs) at higher frequency band. The simulation results have good agreements with observations and the GMFs for different incidence angles, azimuth angles and wind speeds.

Recently the bistatic radar systems have seen increasing attention and development for the advantages in remote sensing of ocean surfaces. With the considerable merits of spatial diversity, bistatic systems supplement the retrieval of ocean physical parameters with conventional monostatic systems. For instance, an emerging bistatic radar technique, global navigation satellite signal reflectometry (GNSS-R), has been developed and utilized in retrieval of high wind speed. Moreover, bistatic phenomena such as the Brewster effect can reveal target properties that are not revealed clearly in monostatic scattering [1]. Therefore, for better application of ocean microwave remote sensing, understanding of bistatic scattering from the ocean surface is crucial and meaningful.