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.

This study utilizes nearly synchronous Sentinel-1 Synthetic Aperture Radar (SAR) data and dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement mission (GPM) to analyze the effect and sensitivity of a significant precipitation event in the Amazon region on the normalized radar cross-section (NRCS) attenuation for HH/HV. Additionally, by comparing the changes in NRCS obtained from two consecutive observations, the rainfall intensity was also estimated. The results reveal that the maximum attenuation of HH/HV NRCS caused by heavy precipitation exceeded 8 dB, and the sensitivity of HH polarization and HV polarization showed significant consistency in response to rainfall attenuation. Furthermore, the rainfall rate derived from SAR signal attenuation is highly consistent with the GPM DPR rainfall rate.

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) core observatory possesses unique capabilities for delineating the three-dimensional structures of Heavy Ice Precipitation (HIP). This study employs a preliminary statistical analysis of 10 years of GPM DPR measurements from 2014 to 2023 to examine the spatial variability of HIP over various topographical regions in eastern China. The findings indicate that the extent and vertical depth of HIP are greater over plains compared to mountainous areas. Moreover, the seasonal distribution patterns of HIP in the southern hilly terrain and adjacent oceanic zones exhibit marked distinctions from other areas, with the most substantial coverage and thickness occurring during spring. The "high occurrence and coverage" of HIP is most likely related to the applicability of the HIP detection algorithm of GPM DPR in Eastern China. The underlying dynamic and thermodynamic factors potentially influencing these observations--particularly those relating to the monsoonal cycle and the topography of Eastern China--warrant further investigation in subsequent research.

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.

Researchers attempt to use synthetic aperture radar (SAR) for retrieving of rainfall over sea surface, but the mechanism of rainfall on the sea surface is too complex to quantitatively describe, such as the relationship between the NRCS of SAR and rainfall rates. In this article, we matched Radarsat-2 SAR and SFMR observation dataset for 35 tropical cyclones. Based on the existing empirical models of rainfall attenuation, the volume scattering model and the CMOD5 model, we indirectly obtain the sea surface backscattering by subtracting them from the NRCS of SAR. Correlate it with meteorological elements such as wind and rain, we found that the sea surface contribution to the signal is strongly influenced by the radar incidence angle. A fitting model of the rain-induced sea surface backscatter coefficient affected by the angle of incidence and wind direction was established. We find the attenuation is found to decrease with increasing angle of incidence, and the rate of change is faster at small angles.

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.

Taking advantage of AIS data to mine the dynamic characteristics of fishery resource exploitation helps to carry out scientific management of fishery and realize the sustainable development of marine resources. The paper selected 210 million records of AIS data of approximately 115,000 fishing vessels in the six Chinese fishing grounds. After processing the AIS dataset for fishing activities and fishing vessel types identification, we conducted a thorough mining and analysis of the characteristics of fishing vessel activities in winter and spring of 2019. The results showed that the number of fishing vessels was gradually increasing as the latitude decreased in winter, and that were quite different between winter and spring in the northern fishing grounds. Gillnetters were the most numerous fishing vessel type operating in the inshore fishing grounds with increased in spring, while seiners had an absolute advantage in the Xisha-Zhongsha fishing ground.

As the improving of the synthetic aperture radar (SAR) resolution and the increase in the amount of data acquisition, the ship type recognition has become an important research topic. In order to meet the precise identification for ship types, 101 SAR data and the Automatic Identification System (AIS) were used to build a SAR ship database. The database contains 5288 ship samples with different polarizations, incidence angle and resolutions, including more than 20 kinds of ship type such as cargo, container, oil tankers, and fishing boats. Furthermore, the influence of different polarization, incidence angle and heading on ship geometry parameters was analyzed. Moreover, a random forest (RF) classifier was used to carry out the ship type recognition experiment, and the classification accuracy reached more than 60%.