Ship detection and classification pose critical challenges in maritime surveillance and monitoring. On the one hand, they find applications in civilian domains, like fishery management, illegal immigrant monitoring, and marine rescue. On the other hand, ships are important targets in maritime combat. Ship detection and classification help to obtain timely maritime military intelligence that holds immense importance in seizing control of the maritime battlefield and ensuring the success of military operations [1], [2]. Remote sensing technology, facilitated by sensors mounted on platforms like satellites and airplanes, enables long-distance noncontact imaging of ground targets. It offers high timeliness, wide coverage, and low-cost accessibility and is not constrained by geographic conditions. Consequently, it has emerged as the primary method for monitoring vessels [4], [5].

Thanks to the powerful feature extraction and expression ability of convolutional neural networks (CNNs), exceptional success has been achieved in the field of ship automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the CNNs cannot work effectively with sparse labelled samples and imbalanced categories.This study proposes a new Attention-Dense-CycleGAN (ADCG) method that is suitable for the ship transfer learning task from optical to SAR (OPT2SAR). The key improvement of the ADCG lies in the construction of a Dense Connection Module (DCM) and a lightweight Convolutional Block Attention Module (CBAM). The DCM is able to overcome the problems of generator feature redundancy, large network model parameters, and severe training time in the original CycleGAN network. The lightweight CBAM can solve the problem of not being able to locate the main features of ships with a minimal increase in network parameters. Compared with the performance of other popular generative adversarial networks, the superior performance of the ADCG in the OPT2SAR transfer learning is demonstrated with the Fréchet Inception Distance (FID) minimum of 76.04 and the Kernel Inception Distance (KID) minimum of 0.0403. Finally, the ability of pseudo-SAR domain images were tested to improve the recognition accuracy of popular ship classification networks, this achieved an average improvement of 6% in recognition accuracy. Therefore the results of this study verifies the rationality, validity, and application value of pseudo-SAR domain in solving the problems of sparse marker samples and class imbalance in ship ATR network model.

In recent years, concern has increased about the depletion of marine resources caused by the overexploitation of fisheries and the degradation of ecosystems. The Automatic Identification System (AIS) is a powerful tool increasingly used for monitoring marine fishing activity. In this paper, identification of the type of fishing vessel (trawlers, gillnetters and seiners) was carried out using 150 million AIS tracking points in April, June and September 2018 in the northern South China Sea (SCS). The vessels’ spatial and temporal distribution, duration of fishing time and other activity patterns were analyzed in different seasons. An identification model for fishing vessel types was developed using a Light Gradient Boosting Machine (LightGBM) approach with three categories with a total of 60 features: speed and heading, location changes, and speed and displacement in multiple states. The accuracy of this model reached 95.68%, which was higher than other advanced algorithms such as XGBoost. It was found that the activity hotspots of Chinese fishing vessels, especially trawlers, showed a tendency to move northward through the year in the northern SCS. Furthermore, Chinese fishing vessels showed low fishing intensity during the fishing moratorium months and traditional Chinese holidays. This research work indicates the value of AIS data in providing decision-making assistance for the development of fishery resources and marine safety management in the northern SCS.

The detection of small fishing ships is very important for maritime fishery supervision. However, it is difficult to detect small ships using synthetic aperture radar (SAR), due to the weak target scattering and very small number of pixels. Polarimetric synthetic aperture radar (PolSAR) has been widely used in maritime ship detection due to its abundant target scattering information. In the present paper, a new ship detector, named Λ_M, is developed based on the analysis of polarization scattering differences between ship and sea, then combined with the two-parameter constant false alarm rate method (TP-CFAR) algorithm to conduct ship detection. The goals of the detector construction are to fully consider the ship’s depolarization effect, and further amplify it through sliding window processing. First, the signal-to-clutter ratio (SCR) enhancement performance of Λ_M for ships with different lengths ranging from 8 to 230 m under 90 different combinations of windows are analyzed in detail using three set of RADARSAT-2 quad-polarization data, then the appropriate window size is determined. In addition, the SCR enlargement between Λ_M and some typical polarization features is compared. Among these, for ships of length greater than 35 m, the average contrast of Λ_M is 33.7 dB, which is 20 dB greater than that of the HV channel. For small vessels of length less than 16 m, the average contrast of Λ_M is 16 dB higher than that of HV channel on average. Finally, the RADARSAT-2 data including nonmetallic small vessels are used to perform ship detection tests, and the detection ability for conventional and small ships of some classic algorithms are compared and analyzed. For large vessels of length greater than 35 m, the method proposed in this paper is able to obtain a superior detection result, maintain the ship contour well, and suppress false alarms caused by the cross side lobe in the SAR image. For small vessels of length less than 16 m, the method proposed in this paper can reduce the number of missed targets, while also obtaining superior detection results, especially for small nonmetallic vessels.

This paper introduces an innovative approach in Synthetic Aperture Radar (SAR) polarimetry and proposes a novel descriptor called polarimetric autocorrelation matrix. Different from polarimetric covariance and coherency matrices, the polarimetric autocorrelation matrix can capture hidden Doppler information in the frequency domain and encode it in the phase using higher-order statistical methods. This matrix facilitates the joint extraction and analysis of polarization and Doppler information from fully Polarimetric SAR (PolSAR) data through matrix analysis. The paper explains that the polarimetric autocorrelation matrix can be decomposed into a Doppler-related matrix and a covariance-related matrix. The magnitudes and phases of these matrices provide insight into Doppler shifts between different polarizations and the variance of the backscattering coefficient. Our research demonstrates how the Doppler shift is influenced not only by the target’s radial velocity but also by radar polarization. Four quad-polarimetric RADARSAT-2 images are used for testing in this study, and a set of characterization features and parameters are derived from the polarimetric autocorrelation matrix. The novel descriptor, together with the new parameters, can detect man-made target and sea ice, mitigate ambiguity caused by moving targets, and indicate the motion status of ship targets and sea currents. Specifically for marine scenes, our data found Doppler differences in HH polarization and VV polarization induced by sea surface motion can be up to 100 Hz.

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 two-dimensional 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 ECMWF ERA 5 wave data are 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 m 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.