Fast and Automatic Ship Detection for SAR Imagery Based on Multiscale Contrast Measure

Inshore ship detection in synthetic aperture radar (SAR) imagery poses significant challenges due to the high density and diversity of ships. However, low inter-object backscatter contrast and blurred boundaries of docked ships often result in performance degradation for traditional object detection methods, especially under complex backgrounds and low signal-to-noise ratio (SNR) conditions. To address these issues, this paper proposes a novel detection framework, the Dynamic Weighted Joint Time–Frequency Feature Fusion DEtection TRansformer (DETR) Model (DWTF-DETR), specifically designed for SAR-based ship detection in inshore areas. The proposed model integrates a Dual-Domain Feature Fusion Module (DDFM) to extract and fuse features from both SAR images and their frequency-domain representations, enhancing sensitivity to both high- and low-frequency target features. Subsequently, a Dual-Path Attention Fusion Module (DPAFM) is introduced to dynamically weight and fuse shallow detail features with deep semantic representations. By leveraging an attention mechanism, the module adaptively adjusts the importance of different feature paths, thereby enhancing the model’s ability to perceive targets with ambiguous structural characteristics. Experiments conducted on a self-constructed inshore SAR ship detection dataset and the public HRSID dataset demonstrate that DWTF-DETR achieves superior performance compared to the baseline RT-DETR. Specifically, the proposed method improves mAP@50 by 1.60% and 0.72%, and F1-score by 0.58% and 1.40%, respectively. Moreover, comparative experiments show that the proposed approach outperforms several state-of-the-art SAR ship detection methods. The results confirm that DWTF-DETR is capable of achieving accurate and robust detection in diverse and complex maritime environments.

Automatic ship detection from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR ship detection methods based on the convolution neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based methods and spatial-domain-based methods exhibit certain limitations. Some algorithms do not consider the detection speed and model scale when improving the detection accuracy, which limits the real-time application and deployment of SAR. To solve this problem, a lightweight, high-speed and high-accurate SAR ship detection method based on yolov3 has been proposed. First, the backbone part of the model is improved, the pure integer quantization network is applied as the core to reduce a small amount of accuracy while reducing the model scale by more than half; Second, modify the feature pyramid network to improve the detection performance of small-scale ships by enhancing feature receptive fields; third, introduce the IoU loss branch to further improve the detection and positioning accuracy; finally, the feature distillation is applied to handle the problem of accuracy decrease caused by model integer quantization. The experimental results on the two public SAR ship datasets show that this algorithm has certain practical significance in the real-time SAR application, and its lightweight parameters are helpful for future FPGA or DSP hardware transplantation.

In this letter, we propose a novel ship detection method in synthetic aperture radar (SAR) imagery via variational Bayesian inference. First, we establish the ship detection probabilistic model which decomposes the SAR image as the sum of a sparse component associated with ships and a sea clutter component. Then, we introduce hierarchical priors of the latent variables in the model and use variational Bayesian inference to estimate the posterior distributions of the latent variables. The proposed method is an automatic iterative process without any sliding window. Experimental results accomplished over synthetic data and a RADARSAT-2 SAR image demonstrate that the proposed method can achieve state-of-the-art ship detection performance.

Probability density function (pdf) estimation of sea clutter in synthetic aperture radar (SAR) imagery has a fundamental role in constructing a constant false alarm rate (CFAR) based ship detector. This paper proposes a semi-parametric sea clutter modeling method for SAR amplitude imagery. The pdf of sea clutter is estimated point by point for each amplitude value, by selecting an optimal component from a given dictionary. For a specific point, the optimal component is selected by measuring the statistical consistency between pdfs of different components and the pdf of sample data within a local window in pdf domain. The statistical consistency is measured by Kullback-Leibler distance (KL-distance). The size of local window is determined based on smoothness criterion. Experimental results on several real SAR imageries demonstrate that the proposed method accurately models the sea clutter, and is flexible to combine with CFAR to construct a ship detector.

The utilization of Synthetic Aperture Radar (SAR) for real-time ship detection proves highly advantageous in the supervision and monitoring of maritime activities. Ship detection in the range-compressed domain of SAR rather than in fully focused SAR imagery can significantly reduce the time and computational resources required for complete SAR imaging, enabling lightweight real-time ship detection methods to be implemented on an airborne or spaceborne SAR platform. However, there is a lack of lightweight ship detection methods specifically designed for the SAR range-compressed domain. In this paper, we propose Fast Range-Compressed Detection (FastRCDet), a novel lightweight network for ship detection in the SAR range-compressed domain. Firstly, to address the distinctive geometric characteristics of the SAR range-compressed domain, we propose a Lightweight Adaptive Network (LANet) as the backbone of the network. We introduce Arbitrary Kernel Convolution (AKConv) as a fundamental component, which enables the flexible adjustment of the receptive field shape and better adaptation to the large scale and aspect ratio characteristics of ships in the range-compressed domain. Secondly, to enhance the efficiency and simplicity of the network model further, we propose an innovative Multi-Scale Fusion Head (MSFH) module directly integrated after the backbone, eliminating the need for a neck module. This module effectively integrates features at various scales to more accurately capture detailed information about the target. Thirdly, to further enhance the network’s adaptability to ships in the range-compressed domain, we propose a novel Direction IoU (DIoU) loss function that leverages angle cost to control the convergence direction of predicted bounding boxes, thereby improving detection accuracy. Experimental results on a publicly available dataset demonstrate that FastRCDet achieves significant reductions in parameters and computational complexity compared to mainstream networks without compromising detection performance in SAR range-compressed images. FastRCDet achieves a low parameter of 2.49 M and a high detection speed of 38.02 frames per second (FPS), surpassing existing lightweight detection methods in terms of both model size and processing rate. Simultaneously, it attains an average accuracy (AP) of 77.12% in terms of its detection performance. This method provides a baseline in lightweight network design for SAR ship detection in the range-compressed domain and offers practical implications for resource-constrained embedded platforms.

Ship detection on synthetic aperture radar (SAR) imagery has many valuable applications for both civil and military fields and has received extraordinary attention in recent years. The traditional detection methods are insensitive to multiscale ships and usually time‐consuming, results in low detection accuracy and limitation for real‐time processing. To balance the accuracy and speed, an end‐to‐end ship detection method for complex inshore and offshore scenes based on deep convolutional neural networks (CNNs) is proposed in this paper. First, the SAR images are divided into different grids, and the anchor boxes are predefined based on the responsible grids for dense ship prediction. Then, Darknet‐53 with residual units is adopted as a backbone to extract features, and a top‐down pyramid structure is added for multiscale feature fusion with concatenation. By this means, abundant hierarchical features containing both spatial and semantic information are extracted. Meanwhile, the strategies such as soft non‐maximum suppression (Soft‐NMS), mix‐up and mosaic data augmentation, multiscale training, and hybrid optimization are used for performance enhancement. Besides, the model is trained from scratch to avoid learning objective bias of pretraining. The proposed one‐stage method adopts end‐to‐end inference by a single network, so the detection speed can be guaranteed due to the concise paradigm. Extensive experiments are performed on the public SAR ship detection dataset (SSDD), and the results show that the method can detect both inshore and offshore ships with higher accuracy than other mainstream methods, yielding the accuracy with an average of 95.52%, and the detection speed is quite fast with about 72 frames per second (FPS). The actual Sentinel‐1 and Gaofen‐3 data are utilized for verification, and the detection results also show the effectiveness and robustness of the method.

Finding ships in water using high-resolution radar images is goal of synthetic aperture radar (SAR) ship detection, which works in any lighting or weather scenario. By taking advantage of ships' distinct scattering characteristics against water's surface, it is an essential tool for border security, maritime surveillance, and monitoring illicit fishing or trafficking. To accurately recognize ships in synthetic aperture radar (SAR) images in real-time, this study presents AMTNet, a new attention-enhanced multiscale transformer network. Despite abundance of structural information in SAR images, object detection remains a tough task due to speckle noise and diverse backdrops. suggested AMTNet integrates a ResNet-based multiscale convolutional backbone, a spatial attention module (SAM), a channel attention module (CAM), and transformer-based contextual modeling to solve these problems. In addition, a feature exchange system is implemented to address issue of domain gaps that might be generated by differences in environment and sensors in bi-temporal SAR images. To improve accuracy of change detection and localization, this Siamese design allows cooperative exploitation of spatial, temporal, and contextual signals. Compared to top models like MSDFF-Net, GLDet, and LEAD-YOLO, AMTNet outperforms them all on SSDD dataset, with metrics like detection speed (138.5 FPS), accuracy (94.60 percent), recall (95.15 percent), and F1-score (74.87%). Attention processes and feature exchange have been proven to play a key role in ablation research. Another proof of model's practicality is its resilience in face of occlusion and noise. With its excellent performance in a variety of SAR imaging settings and its high interpretability, AMTNet is a computationally economical and effective option for marine surveillance.

Ship surveillance by remote sensing technology has become a valuable tool for protecting marine environments. In recent years, the successful launch of advanced synthetic aperture radar (SAR) sensors that have high resolution and multipolarimetric modes has enabled researchers to use SAR imagery for not only ship detection but also ship category recognition. A hierarchical ship detection and recognition scheme is proposed. The complementary information obtained from multipolarimetric modes is used to improve both the detection pre- cision and the recognition accuracy. In the ship detection stage, a three-class fuzzy c-means clustering algorithm is used to calculate the segmenting threshold for prescreening ship candi- dates. To reduce the false alarm rate (FAR), we use a two-step discrimination strategy. In the first step, we fuse the detection results from multipolarimetric channels to reduce the speckle noise, ambiguities, sidelobes, and other sources of interference. In the second step, we use a binary classifier, which is trained with prior data collected on ships and nonships, to reduce the FAR even further. In the ship category recognition stage, we concatenate texture- based descriptors extracted from multiple polarmetric channels to construct a robust ship representation for category recognition. Furthermore, we construct and release a ship category database with real SAR data. We hope that it can be used to promote investigations of SAR ship recognition in the remote sensing and related academic communities. The proposed method is validated by a comprehensive experimental comparison to the state-of-the-art methods. The validation procedure showed that the proposed method outperforms all of the competing meth- ods by about 5% and 15% in terms of ship detection and recognition, respectively. © 2014 Society

\nShip detection in synthetic aperture radar (SAR) remote sensing images, being a fundamental but challenging problem in the field of satellite image analysis, plays an important role for a wide range of applications and is receiving significant attention in recent years. Aiming at the requirements of ship detection in high-resolution SAR images, the accuracy, the intelligent level, a better real-time operation and processing efficiency, The characteristics of ocean background and ship target in high-resolution SAR images were analyzed, we put forward a ship detection algorithm in high-resolution SAR images. The algorithm consists of two detection stages: The first step designs a pre-training classifier based on improved spectral residual visual model to obtain the visual salient regions containing ship targets quickly, then achieve the purpose of probably detection of ships. In the second stage, considering the Bayesian theory of binary hypothesis detection, a local maximum posterior probability (MAP) classifier is designed for the classification of pixels. After the parameter estimation and judgment criterion, the classification of pixels are carried out in the target areas to achieve the classification of two types of pixels in the salient regions. In the paper, several types of satellite image data, such as TerraSAR-X (TS-X), Radarsat-2, are used to evaluate the performance of detection methods. Comparing with classical CFAR detection algorithms, experimental results show that the algorithm can achieve a better effect of suppressing false alarms, which caused by the speckle noise and ocean clutter background inhomogeneity. At the same time, the detection speed is increased by 25% to 45%.\n

At present, the ship detection of optical remote sensing images based on deep learning has made great progress. However, due to the different use scenarios and specific tasks, how to select an appropriate algorithm according to the characteristics of the target and the target priority, so that achieve the detection goal while consider the detection accuracy and speed, still requires relevant research. In this paper, ship detection methods for optical remote sensing images are studied based on deep learning. First, to meet the needs of ship detection research, according to the characteristics of target size and type, datasets of medium and large ships and small target ships are made, and model training and testing are conducted based on Faster R-CNN, YOLOv4, and SSD algorithms respectively. The actual detection performance of the three algorithms under different ship sizes is obtained. The results show that for medium and large targets, Faster R-CNN has the highest precision, the next is YOLOv4, and SSD is the lowest. The detection speed is that SSD is the fastest, the next is YOLOv4, Faster R-CNN is the slowest. For small target ship detection, YOLOv4 has the best detection accuracy and SSD has the fastest detection speed. Faster R-CNN is not as accurate and fast as the other two algorithms. In addition, for different type ships, the detection results of different algorithms also have some differences. In practical applications, different methods should be used to achieve detection by comprehensively considering such factors as target size, target priority, detection accuracy and speed requirements.

Synthetic Aperture Radar (SAR) imagery is available all the time and, in all weathers, due to its ability to penetrate through cloud cover, dust, haze except heavy rainfall. SAR imagery mainly characterizes structural properties of various earth objects and provides a data which is rich in spatial information. Processing of SAR data has been of great interest due to these advantages. However, due to coherent processing of SAR signal, imagery is corrupted by speckle noise which is multiplicative in nature. Reduction of speckle noise from SAR imagery is helpful for analysis and interpretation of SAR images. In this paper, performance of conventional speckle reduction filters, namely Lee, Enhanced Lee, Frost, Enhanced Frost and Gamma MAP are compared with wavelet-based filtering technique for RISAT-1 imagery. Results are compared visually and using quality metrics, Speckle Suppression Index (SSI), Equivalent Number of Looks (ENL) and Speckle Suppression and Mean Preservation Index (SMPI). From visual interpretation of de-speckled image, it is evident that wavelet-based filtering out performs the conventional filters in terms of edge and texture preservation.

Oil spills on the ocean from ships have a strong impact on the environment as well as on human societies. The MV X-Press Pearl container ship incident in 2021 became the worst marine environmental disaster in Sri Lanka due to its powerful impact. Oil leakage from the ship to the ocean continued for months. Synthetic Aperture Radar (SAR) imagery has the advantage of penetrating cloud cover, as well as day and night operating capabilities. Therefore, SAR imagery can be used to detect as well as observe the temporal changes of oil spills. The study in this article aimed to detect the oil spill caused by the MV X-Press Pearl container ship on Sri Lanka’s west coast from May to August 2021 using Sentinel-1 SAR data and measure the temporal changes of the oil slick. Three SAR imageries with Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarization from June, July, and August of 2021 have been processed by applying the orbit file, running the multi-looking algorithm, and making the ellipsoid correction. The possible oil spills have been identified from each set of imagery as dark linear shapes with lengths of 3.1 ±0.012 km with an average reflectance of 15.6 decibels for June and 3.6 ±0.012 km with an average reflectance of 16.5 decibels for the other two months, and the shapes correspond with the oil slick classifications from other literature. The results indicate that the VV polarization is the most suitable to detect oil spills, and that the oil spill from the X-Press Pearl continued from June to the end of August 2021.

Synthetic Aperture Radar (SAR) imagery have been one of the important tools to support earth observations and topographic measurements. It means SAR imagery are essentially rich in structures and some important target categories are difficult to recognize. Optical imagery contains rich and clear spectral information which has a good influence on semantic image segmentation. The success of deep neural networks for semantic segmentation heavily depends on large-scale and well-labeled data sets, which are hard to collect in practice. In this paper, we consider deep transfer learning for semantic segmentation, we propose a deep novel transfer learning method, which transfers a semantically segment model from SAR imagery to SAR and optical fusion imagery. The experimental results show that the method proposed achieves higher mean Intersection over Union (mIoU) with less training time compared with other methods.

Reliable automatic ship detection in Synthetic Aperture Radar (SAR) imagery plays an important role in the surveillance of maritime activity. Apart from the well-known Spectral Residual (SR) and CFAR detector, there has emerged a novel method for SAR ship detection, based on the deep learning features. Within this paper, we present a framework of Sea-Land Segmentation-based Convolutional Neural Network (SLS-CNN) for ship detection that attempts to combine the SLS-CNN detector, saliency computation and corner features. For this, sea-land segmentation based on the heat map of SR saliency and probability distribution of the corner is applied, which is followed by SLS-CNN detector, and a final merged minimum bounding rectangles. The framework has been tested and assessed on ALOS PALSAR and TerraSAR-X imagery. Experimental results on representative SAR images of different kinds of ships demonstrate the efficiency and robustness of our proposed SLS-CNN detector.

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model’s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models.