Rapid and accurate prediction of the sea clutter amplitude distribution is essential to improve target detection capability in synthetic aperture radar (SAR) imagery. In this letter, we propose a sea clutter amplitude network (SCA-Net) based on multitask learning for sea clutter amplitude distribution prediction (SCADP) of SAR images. To reduce the number of model parameters, we design a shallow residual network structure with four residual blocks and replace the normal convolution with depthwise separable convolution in the residual blocks. The efficient channel attention (ECA) module is incorporated into each residual block to strengthen the model’s feature extraction capability. To validate the performance of the model, we construct a SCADP dataset using GaoFen-3 wave mode data. The experimental results on the SCADP dataset indicate that the proposed method achieves the highest prediction accuracy, which proves that the method can effectively achieve integrated prediction of amplitude distribution types and parameters of sea clutter.

Target detection is of vital importance to maritime security and maritime resource protection. However, the detection of small or high state targets is difficult based on traditional methods, for targets are easy to be submerged in sea clutter. In this paper, by using the Polarimetric differences of targets and sea clutter, a new polarized detector, multi-feature fusion Geometrical Perturbation–Polarimetric Notch Filter (GP-PNF) is proposed. To make the use of polarization features, a feature dimension reduction method is introduced to reduce the redundancy and the computational complexity, so as to extract new polarization features for the design of the new detector. Radarsat-2 full-Polarimetric SAR data are used to verify the effectiveness of the proposed method. The performances of full-, compact- and dual-Polarimetric SAR detectors are evaluated. The results demonstrated that the proposed method perform better than K-CFAR, G0-CFAR methods.

Wave breaking significantly influences the scattering mechanism at the sea surface. Therefore, investigating wave breaking is crucial for understanding the microwave scattering mechanism at the sea surface. This paper presents a study on wave breaking contribution under small incidence angles, building upon the analyzed characteristics of wave breaking contribution. We propose a backscattering model that combines wave breaking at small incidence angles with small slope approximation. Additionally, we execute a spectrum cutoff, leveraging the dominant relationship between incidence wave and their corresponding sea surface roughness detection capabilities. This method not only further constrains the spectrum cutoff range but also substantially decreases the computational complexity, all while maintaining the core computational content.

Polarimetric SAR has been widely used in ship detection. In this paper, the Doppler information between polarization channels is extracted based on the covariance matrix elements, and the joint description of polarization characteristics and Doppler characteristics for targets is realized. On this basis, two new ship detectors considering Doppler information are designed. The experimental results show that the proposed method can maintain a good ship detection effect, and to a certain extent inhibit the false alarms generated by the land, thus improving the ship detection performance.

Sea clutter is an important factor for the detection of sea surface targets in radar images. However, only limited time and local sea clutter samples can be obtained currently, which cannot cover the ever-changing marine environment. The generation of sea clutter data from unknown sea areas based on complex marine environments has important theoretical significance and application value. Therefore, a sea clutter data generation method based on Pix2Pix is proposed for SAR images. The nonlinear mapping relationship between wave spectrum and SAR image spectrum is learned by 2912 pairs of SAR images and ERA-5 wave spectrum data. Thus, the network can generate matched sea clutter images by inputting real marine environment information (wave spectrum) after training. Finally, the image similarity index and histogram model fitting are used to verify the effectiveness of the proposed method.

Ocean wave is of great significance in marine environment prediction, maritime navigation, and global climate change. Synthetic aperture radar (SAR) is widely used in ocean wave spectra retrieval due to its 2-D high resolution, all-weather, and all-time advantages. Nevertheless, the nonlinear mapping between SAR and ocean waves, caused by velocity bunching, hinders the advancement of wave spectra inversion techniques, resulting in low-quality and incomplete wave spectra. To overcome the problem, a novel deep learning model SAR2WV for ocean wave spectra retrieval based on Pix2pix is proposed by constructing the nonlinear mapping relationship of SAR cross spectra and ocean wave spectra. A total of 106 844 Sentinel-1 wave mode dataset along with the corresponding European Centre for Medium-Range Weather Forecasts (ECMWF) ERA 5 wave data is processed and used for training the SAR2WV model. Experiments demonstrate that the proposed SAR2WV model can significantly improve the accuracy of the retrieved wave spectra and wave parameters, with the spectra similarity improved by 60.3%, root-mean-square error (RMSE) of significant wave height (SWH) decreased from 0.966 to 0.386 m, RMSE of mean wave period (MWP) decreased from 1.208 s to 0.811 s, and correlation coefficient of peak wave direction increased from 0.65 to 0.72, which achieves better performance than ocean swell wave spectra (OSW) algorithm and other methods.

The freeboard height of the ship is a critical parameter that mirrors the ship’s load capacity and safety performance. However, research on the height of the ship is less explored. This letter derives imaging differences between the top and bottom of the ship’s freeboard based on SAR imaging geometry and then establishes a ship-sea coupling scattering path model. Relying on the proposed model and the polarimetric synthetic aperture radar’s (PolSAR) capability to differentiate various scattering mechanisms, a method for retrieving the freeboard height of ships is proposed. This method primarily utilizes the total backscattering power SPAN and double-bounce scattering component (DBL) features of single-pass PolSAR data acquired with a single antenna. Finally, the proposed method is verified by the in situ data and tested on different types of ships, such as cargo ships and bulk carriers, and the absolute relative error (ARE) of the retrieval results is less than 6.1%.

With the development of deep learning (DL), research on ship classification in synthetic aperture radar (SAR) images has made remarkable progress. However, such research has primarily focused on classifying large ships with distinct features, such as cargo ships, containers, and tankers. The classification of SAR fishing vessels is extremely challenging because of two main reasons: 1) the small size and minor interclass differences of fishing vessels make learning fine-grained features difficult, and 2) determining fishing vessel types is difficult, resulting in a lack of labeled data. Hence, after designing a process framework for vessel tagging, we construct a high-resolution fine-grained fishing vessel classification dataset (FishingVesselSAR), which contains 116 gillnetters, 72 seiners, and 181 trawlers. We then propose a novel DL model (FishNet) that aims to strengthen feature extraction and utilization. In FishNet, we introduce four innovative modules to ensure superior performance in SAR fishing vessel classification: a multipath feature extraction (MUL) module, a feature fusion (FF) module, a multilevel feature aggregation (MFA) module and a parallel channel and spatial attention (PCSA) module. Furthermore, we design an adaptive loss function to achieve better classification performance by mitigating the effects of class imbalance. In this paper, we report extensive ablation studies conducted to confirm the efficacy of the five improvements listed above. Sufficient comparisons with 33 advanced methods from the DL and SAR target classification communities demonstrate that FishNet achieves a SAR fishing vessel classification accuracy of 89.79%, which is 6.77% higher than that of the second-best method.

The azimuth cutoff wavelength of 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 H-V linear polarization bases (HH, HV/VH, and 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 4,648 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 evaluating 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 electromagnetic wave. The azimuth cutoff wavelength under the elliptical polarization bases has 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.