Sea-surface velocity (SSV) and sea-surface height (SSH) are among the most crucial parameters in an oceanic dynamic environment. Using spaceborne interferometric synthetic aperture radar (InSAR) to obtain high-resolution, large-scale survey area, high observation frequency, and high-precision ocean dynamic parameters is advantageous. However, the hybrid baseline InSAR phase data include components from both SSV and SSH, making it challenging to distinguish them and affecting inversion accuracy without additional information. The PIE-1 constellation, the world’s first four-satellite distributed InSAR system, was successfully launched into orbit on March 30, 2023. This constellation can form multiple interferometric pairs by combining two satellites, enabling the potential to extract SSV and SSH from hybrid phase signals simultaneously. In this study, two pioneering and fundamental works were conducted: 1) an integrated current-height inversion model was developed based on the multichannel likelihood (ML) function, with error analysis performed according to PIE-1 parameters and 2) the detailed data processing scheme for the first simultaneous inversion of SSV and SSH based on spaceborne InSAR data was presented. The inversion results were compared to Doppler centroid analysis (DCA)-derived Doppler velocity and reference data from the ESA’s Copernicus Marine Service (CMEMS). Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results. This approach represents an effective technique for simultaneous inversion of SSV and SSH in future multibaseline spaceborne/airborne InSAR systems.

The sliding spotlight synthetic aperture radar (SAR) system, as a high-resolution imaging mode, has played a crucial role in vessel detection and tracking over the ocean. Radial velocity is an important indicator for SAR vessel information extraction, which is of great significance for vessel positioning, actual velocity estimation, and target tracking. However, the unique operating mode and complex signal model of sliding spotlight SAR result in a complex coupling between Doppler frequency shift and azimuth offset caused by vessel motion, making traditional Stripmap mode radial velocity estimation methods unsuitable. This letter based on the imagery signal model of moving targets in sliding spotlight SAR mode, establishes the mapping relationship between radial velocity and theoretical Doppler shift. Finally, a vessel radial velocity estimation method for sliding spotlight SAR mode was proposed, combining the single-look complex (SLC) data from real SAR imagery. The proposed method is validated by simulation results and real SAR SLC data of GaoFen-3 (GF-3) sliding spotlight mode. Additionally, the processed results demonstrate that the estimation error has a root-mean-square (rms) value of 0.34 m/s when compared with the information provided by automatic identification system (AIS) data.

Synthetic aperture radar (SAR) is used to persistently monitor marine areas in all weather conditions for excellent ship and wake identification. Current deep learning-based ship wake detection methods rely on supervised learning. However, no publicly available large-scale SAR dataset is available to support this learning method. Owing to the diversity of ship wake characteristics in SAR images and the complexities of sea states, contemporary computer vision algorithms are generally ineffective for SAR image recognition tasks. To overcome these issues, this article presents the new, well-annotatedOpenSARWakedataset dedicated to oriented ship wake detection. This collection provides 3,973 images containing two polarization modes and 4,096 instances. Their image features are used to train a novel two-stage SWNet feature refinement detector that adopts a sophisticated HR-FPN* backbone for SAR ship wake detection. The detector recognizes nearly all wake characterizations. The trained SWNet achieves state-of-the-art detection performance with 49.0% mAP, outperforming most benchmarking algorithms. TheOpenSARWakedataset is available at https://github.com/libzzluo/OpenSARWake.

MIMO ground-based synthetic aperture radar (GB-SAR) have experienced rapid development in recent years because they can rapidly acquire echo data from observed scenes neglecting mechanical motion and thus can monitor the entire geological deformation process ranging from slow changes to rapid instabilities. However, the unique alternating transceiver configuration of MIMO GB-SAR systems presents rapidly varying periodic characteristics of the channel amplitude and phase errors, leading to severe paired false targets in the imaging results. Hence, their estimation and compensation accuracy requirements are extremely high due to the energy concentration properties of periodic amplitude and phase errors. In response to these concerns, this study develops a parameter model for the amplitude and phase errors of the transceiver channels and proposes an amplitude and phase errors parameter estimation method based on the minimizing of image entropy. The proposed algorithm involves imaging, pre-correction, and fine correction. Specifically, a non-interpolated sub-aperture imaging algorithm is proposed, then the amplitude and phase error parameter model is established, and pre-correction is performed using the model to reduce the number of iterations required for parameter estimation optimization. Finally, based on the parameter model, the amplitude and phase errors are estimated according to the entropy minimization criterion. Experimental results demonstrate that the proposed method can accurately estimates the channel phase cycle errors, significantly suppresses azimuth ambiguities, and achieves appealing focusing performance for the images.

In recent years, multiple synthetic aperture radar (SAR) satellites, such as Radarsat-2 and Gaofen-3 (GF-3) spaceborne SAR systems have adopted multichannel high-resolution and wide-swath (MC-HRWS) imaging technology, which can overcome the resolution and mapping width limitation of single-channel SAR. However, considering the influence of Doppler velocity of moving targets and signal energy outside the processed Doppler bandwidth (PDB) on azimuth signal reconstruction processing, Doppler distortion of moving targets and ambiguities energy will occur in the final single-look complex (SLC) data. These issues will result in significant Doppler centroid error and high azimuth ambiguity-to-signal ratio (AASR), severely restricting the ability to obtain accurate ocean surface Doppler velocities. Therefore, an accurate ocean Doppler velocity estimation method for the MC-HRWS SAR system was proposed based on a reconstruction algorithm in the imaging process and the azimuth ambiguity signal model. According to the Bayesian principle, the problem of estimating Doppler velocity on the ocean surface has been transformed into an optimization problem for maximizing the Doppler parameters of posterior probability. Simulation comparative experiments revealed that our method outperformed the conventional methods in the cases of low-uniform sampling, particularly in the region with high AASR. In addition, the Doppler velocity estimation results of the GF-3 ultrafine strip-map (UFS) mode also indicate that the proposed method has a good suppression effect on the Doppler velocity estimation error and AASR for the MC-HRWS SAR system.

Sea clutter and inherent speckle noise in synthetic aperture radar (SAR) images can pose challenges to accurate sea surface target detection, especially for phenomena such as ship wakes reliant on efficient feature extraction. Traditional denoising methods require manual tradeoffs between denoising effects and detail retention. Supervised denoising methods based on deep learning demand a substantial number of real noisy-clean image pairs for training, coupled with specific parameter settings and labeled data amounts. In response to these challenges, this article introduces Wake2Wake, a self-supervised denoising method aimed at enhancing the performance of existing deep learning-based ship wake detectors. The method incorporates a novel ship wake awareness (SWA) block designated to address the distinctive features of turbulent and Kelvin wakes. Furthermore, to overcome the source imbalance problem in the dataset, simulated wake data are integrated into the training process. This not only mitigates dataset imbalances but also significantly improves both denoising and detection performance. The experimental results indicate that Wake2Wake improves the accuracy of Rotated RepPoints by 3.6 mAP and S2A-Net by 2.6 mAP on the OpenSARWake dataset, respectively. The proposed approach achieves varied extents of improvement, showcasing its potential in mitigating sea clutter and enhancing feature extraction, especially in detecting SAR ship wakes.

The accurate interpretation of ocean waves in synthetic aperture radar (SAR) images is challenging because the scattering of moving waves causes various complex mechanisms. Moreover, SAR imaging is based on a unique nonlinear mapping relationship. This study proposed a full-link simulator to simulate the main parts of ocean surface backscattering, ocean surface motion, platform motion, echo generation and acquisition, SAR imaging, etc. The proposed simulator demonstrated the dynamic characteristics of the ocean surface by simulating the movement and decoherence effect of scattering cells within each radar pulse. Subsequently, a method for retrieving the wave direction spectrum from the SAR image spectrum based on the simulator was developed. The simulator, which can accurately reflect the modulation of backscattering, velocity bunching effect, and decoherence effect, was used as the forward transformation from the ocean wave spectrum to SAR image spectrum. In addition, the Levenberg-Marquardt iterative method was applied to determine the optimal solution through adjustment of several key parameters of the wave spectrum and ocean surface coherence time. Further, the Sentinel-1 SAR data were used to verify the accuracy of the full-link simulator and the effectiveness of the retrieval scheme. The results showed the following. 1) The SAR image with the simulation of both the moving scene and decoherence effect was highly consistent with the SAR image observed by Sentinel-1. 2) The retrieval scheme exhibited strong correction ability, thereby effectively improving the consistency between the retrieved and observed SAR spectra. In addition, the wave parameters (significant wave height, mean wave period, and dominant wave propagation direction) calculated considering the retrieved wave spectrum were consistent with the buoy measurements.