SAR Ship Detection Dataset (SSDD) is the first open dataset that is widely used to research state-of-the-art technology of ship detection from Synthetic Aperture Radar (SAR) imagery based on deep learning (DL). According to our investigation, up to 46.59% of the total 161 public reports confidently select SSDD to study DL-based SAR ship detection. Undoubtedly, this situation reveals the popularity and great influence of SSDD in the SAR remote sensing community. Nevertheless, the coarse annotations and ambiguous standards of use of its initial version both hinder fair methodological comparisons and effective academic exchanges. Additionally, its single-function horizontal-vertical rectangle bounding box (BBox) labels can no longer satisfy the current research needs of the rotatable bounding box (RBox) task and the pixel-level polygon segmentation task. Therefore, to address the above two dilemmas, in this review, advocated by the publisher of SSDD, we will make an official release of SSDD based on its initial version. SSDD’s official release version will cover three types: (1) a bounding box SSDD (BBox-SSDD), (2) a rotatable bounding box SSDD (RBox-SSDD), and (3) a polygon segmentation SSDD (PSeg-SSDD). We relabel ships in SSDD more carefully and finely, and then explicitly formulate some strict using standards, e.g., (1) the training-test division determination, (2) the inshore-offshore protocol, (3) the ship-size reasonable definition, (4) the determination of the densely distributed small ship samples, and (5) the determination of the densely parallel berthing at ports ship samples. These using standards are all formulated objectively based on the using differences of existing 75 (161 × 46.59%) public reports. They will be beneficial for fair method comparison and effective academic exchanges in the future. Most notably, we conduct a comprehensive data analysis on BBox-SSDD, RBox-SSDD, and PSeg-SSDD. Our analysis results can provide some valuable suggestions for possible future scholars to further elaborately design DL-based SAR ship detectors with higher accuracy and stronger robustness when using SSDD.

Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology.

Ship detection from synthetic aperture radar (SAR) imagery is a fundamental and significant marine mission. It plays an important role in marine traffic control, marine fishery management, and marine rescue. Nevertheless, there are still some challenges hindering accuracy improvements of SAR ship detection, e.g., complex background interferences, multi-scale ship feature differences, and indistinctive small ship features. Therefore, to address these problems, a novel quad feature pyramid network (Quad-FPN) is proposed for SAR ship detection in this paper. Quad-FPN consists of four unique FPNs, i.e., a DEformable COnvolutional FPN (DE-CO-FPN), a Content-Aware Feature Reassembly FPN (CA-FR-FPN), a Path Aggregation Space Attention FPN (PA-SA-FPN), and a Balance Scale Global Attention FPN (BS-GA-FPN). To confirm the effectiveness of each FPN, extensive ablation studies are conducted. We conduct experiments on five open SAR ship detection datasets, i.e., SAR ship detection dataset (SSDD), Gaofen-SSDD, Sentinel-SSDD, SAR-Ship-Dataset, and high-resolution SAR images dataset (HRSID). Qualitative and quantitative experimental results jointly reveal Quad-FPN’s optimal SAR ship detection performance compared with the other 12 competitive state-of-the-art convolutional neural network (CNN)-based SAR ship detectors. To confirm the excellent migration application capability of Quad-FPN, the actual ship detection in another two large-scene Sentinel-1 SAR images is conducted. Their satisfactory detection results indicate the practical application value of Quad-FPN in marine surveillance.

Synthetic aperture radar (SAR) is an advanced active microwave sensor widely used in marine surveillance. As part of typical marine surveillance missions, ship classification in synthetic aperture radar (SAR) images is a significant task for the remote sensing community. However, fully utilizing polarization information to enhance SAR ship classification remains an unresolved issue. Thus, we proposed a dual-polarization information-guided network (DPIG-Net) to solve it. DPIG-Net utilizes available dual-polarization information from the Sentinel-1 SAR satellite to adaptively guide feature extraction and feature fusion. We first designed a novel polarization channel cross-attention framework (PCCAF) to model the correlations of different polarization information for feature extraction. Then, we established a novel dilated residual dense learning framework (DRDLF) to refine the polarization characteristics for feature fusion. The results on the open OpenSARShip dataset indicated DPIG-Net’s state-of-the-art classification accuracy compared with eleven other competitive models, which showed the potential of DPIG-Net to promote effective and sufficient utilization of SAR polarization data in the future.

Synthetic aperture radar (SAR) is an advanced microwave sensor, which has been widely used in ocean surveillance, and its operation is not affected by light and weather. SAR ship instance segmentation can provide not only the box-level ship location but also the pixel-level ship contour, which plays an important role in ocean surveillance. However, most existing methods are provided with limited box positioning ability, hence hindering further accuracy improvement of instance segmentation. To solve the problem, we propose a global context boundary-aware network (GCBANet) for better SAR ship instance segmentation. Specifically, we propose two novel blocks to guarantee GCBANet’s excellent performance, i.e., a global context information modeling block (GCIM-Block) which is used to capture spatial global long-range dependences of ship contextual surroundings, enabling larger receptive fields, and a boundary-aware box prediction block (BABP-Block) which is used to estimate ship boundaries, achieving better cross-scale box prediction. We conduct ablation studies to confirm each block’s effectiveness. Ultimately, on two public SSDD and HRSID datasets, GCBANet outperforms the other nine competitive models. On SSDD, it achieves 2.8% higher box average precision (AP) and 3.5% higher mask AP than the existing best model; on HRSID, they are 2.7% and 1.9%, respectively.

Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID.