The Sentinel-1 constellation can provide numerous high-resolution C-band synthetic aperture radar (SAR) data with long-term continuity and freely, thus showing a cost-effective solution for the coastal monitoring at high or moderate spatial resolutions. The major goal is to improve estimates of shallow water depth for SAR applications. We present an algorithm that is based on the linear dispersion relation between water depth and swell parameters like swell wavelength, direction, and period to estimate shallow water depth using multitemporal SAR data with a short repeating cycle. This is accomplished via circular convolution and Kalman filter that provides both the estimates and a measure of their uncertainty at each location. The introduced algorithm is tested on four Sentinel-1 interferometric wide swath (IW) mode SAR images over the coastal region of Fujian Province, China. The retrieved water depth both from multitemporal SAR images and different single SAR images show general agreement with water depth from an official electronic navigational chart. All comparisons indicate that the proposed method is feasible and multitemporal SAR data have great potential in bathymetric surveying.

Fully polarimetric synthetic aperture radar (SAR) can provide detailed information on scattering mechanisms that could enable the target or structure to be identified. This paper presents a method to detect underwater topography in coastal areas using high resolution fully polarimetric SAR data, while less prior information is required. The method is based on the shoaling and refraction of long surface gravity waves as they propagate shoreward. First, the surface scattering component is obtained by polarization decomposition. Then, wave fields are retrieved from the two-dimensional (2D) spectra by the Fast Fourier Transformation (FFT). Finally, shallow water depths are estimated from the dispersion relation. Applicability and effectiveness of the proposed methodology are tested by using C-band fine quad-polarization mode RADARSAT-2 SAR data over the near-shore area of the Hainan province, China. By comparing with the values from an official electronic navigational chart (ENC), the estimated water depths are in good agreement with them. The average relative error of the detected results from the scattering mechanisms based method and single polarization SAR data are 9.73% and 11.53% respectively. The validation results indicate that the scattering mechanisms based methodology is more effective than only using the single polarization SAR data for underwater topography detection, and will inspire further research on underwater topography detection with fully polarimetric SAR data.

As artificial intelligence continues to advance, deep learning has greatly contributed to the advancement of ship recognition using Synthetic Aperture Radar (SAR) images. Deep learning-based SAR ship recognition performance is largely dependent on the sample set used. SAR ship recognition datasets published in recent years, however, are most derived from a single SAR satellite sensor. It needs to be evaluated and analyzed carefully whether the model trained by a single satellite dataset can still achieve the same accuracy with different SAR satellites. The paper focuses on the following research to address these issues. Firstly, using multiple SAR satellite sensors, we create a new SAR ship dataset (named generalization performance evaluation dataset, GPED) containing multi-resolution and multi-polarization data to examine the generalization performance of the deep learning-based SAR ship recognition method. GPED and a marine target detection dataset (MTDD) are then used to evaluate and analyze the generalization performance of current mainstream deep learning methods. According to the the experiment results, the mean average accuracy (mAP) of the ship recognition model trained on GPED is generally higher than that of MTDD, which proves that GPED has a better generalization performance. Furthermore, SAR ship detection datasets have more samples than ship recognition datasets, which inspired us to use transfer learning to transfer knowledge from ship detection to ship recognition. In this paper, a method for ship recognition based on transfer learning that utilizes the knowledge gained from the ship detection task is proposed. The method includes two modules: pretraining module and fine-tuning module. It can apply samples of unlabeled ship types to ship recognition, thus reducing the number of labeled samples that are required for ship recognition. The experimental results on GPED and MTDD show that our method can achieve good recognition performances.

The azimuth cutoff wavelength of synthetic aperture radar (SAR) is an important parameter for retrieval of sea surface wind and wave. Earlier studies have fully demonstrated the substantial dependence of azimuth cutoff wavelength on polarization, but the present studies only focus on horizontal and vertical (H-V) linear polarization bases [horizontal-horizontal (HH), horizontal-vertical (HV)/vertical-horizontal (VH), and vertical-vertical (VV)] without considering the effects of other polarization bases (e.g., linear rotated, circular, and elliptical polarization). Benefiting from the quad-polarization advantage of GaoFen-3 SAR wave mode data and the support of polarization basis transformation theory, this study used 4648 SAR data to study the correlation between cutoff wavelength and wind and wave parameters (e.g., significant wave height and wind speed) under different polarization bases and analyzed the variation of correlation coefficient caused by polarization basis change. Finally, the results were applied to evaluate the performance of wind and wave parameters retrieval. The results of the study show that the azimuth cutoff is strongly dependent on the polarization state of the electromagnetic wave. The azimuth cutoff wavelength under the elliptical polarization bases has a higher correlation with wind and wave than that under H-V linear, circular, and linear rotated polarization bases. Using the azimuth cutoff wavelength of the elliptical polarization bases can significantly improve the retrieval accuracy of wind and wave parameters. This study shall enhance the capabilities of polarized SAR systems to precisely derive more ocean surface properties. The result implies that polarization basis is an important factor that must be considered in future ocean SAR studies.

There are two useful methods of current measurement based on synthetic aperture radar (SAR): one is along-track interferometry (ATI), and the other is Doppler centroid analysis (DCA). For the ATI method, the interferometric phase must be accurate enough for ocean current measurements. Therefore, the space-varying of phase imbalances along the range, caused by antenna phase center position error, attitude error, antenna electronic miss pointing, antenna pattern mismatch, and other reasons, cannot be ignored. Firstly, this paper mainly analyzes the above possible factors by using real GF-3 ATI data and error model simulation results. Secondly, the ocean current has been preliminarily measured by the ATI method and the DCA method, using CDOP model, based on the GF-3 ATI data of the ocean scene near Qingdao, China, which is up to around −1.45 m/s. The results of the two methods are in good agreement with the correlation coefficient of 0.98, the mean difference of −0.010 m/s, and the root mean squared error (RMSE) of 0.062 m/s. Moreover, by comparing with the current measured by high-frequency surface wave radar (HFSWR), the correctness of the analysis is further proved.