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 trade-offs 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 paper 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 is 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 S ² A-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.

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, largescale 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 function, with error analysis performed according to PIE-1 parameters; (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 multi-baseline spaceborne/airborne InSAR systems.

The inversion of ocean currents is a significant challenge and area of interest in ocean remote sensing. Spaceborne along-track interferometric synthetic aperture radar (ATI-SAR) has several advantages and benefits, including precise observations, extensive swath coverage, and high resolution. However, a limited number of spaceborne interferometric synthetic aperture radar (InSAR) systems are operating in orbit. Among these, the along-track baseline length is generally suboptimal, resulting in low inversion accuracy and difficulty in achieving operational stability. One of the approaches involves employing lower-frequency bands such as the L band to increase the baseline length to achieve the optimal baseline for a satellite formation. The LuTan-1 mission, the world’s first L-band distributed spaceborne InSAR system, was successfully launched on 27 February 2022. L-band distributed formation operation provides insight into the development of future spaceborne ATI systems with application to new exploration regimes and under optimal baseline conditions. There are two novel aspects of this investigation: (1) We described the ocean current inversion process and results based on LuTan-1 SAR data for the first time. (2) A cross-track baseline component phase removal method based on parameterized modeling was proposed for distributed InSAR systems. Both qualitative and quantitative comparisons validated the effectiveness and accuracy of the inversion results.