Generative knowledge transfer for ship detection in SAR images

Ship detection in synthetic aperture radar (SAR) images has attracted extensive attention due to its promising applications. While numerous methods for ship detection have been proposed, detecting ships in complex scenarios remains challenging. The main factors contributing to the lower detection accuracy are SAR image characteristics, such as blurred outlines, and similar scattering intensities between actual ship targets and background environment, induced by the special imaging mechanism. To alleviate these issues, we propose a Mixed-scale Self-distillation Network (MSNet) for accurate ship detection in SAR images. Firstly, the zoom strategy is used to obtain more ship target information, and differentiated information between ship targets and background environments at different scales is aggregated through the designed search module. Then, the consistency self-distillation module is proposed to match feature attention maps at different scales, which forces the model to capture the potential semantic attributes of ship targets through a self-distillation fashion. After that, the refinement module is developed to further enhance the discriminative semantics among different hierarchical features under mixed scales. Furthermore, to alleviate the uncertainty arising from indistinguishable background interference in SAR images, we introduce an uncertainty perception loss to facilitate the model to make accurate judgments in candidate regions. Extensive experiments are performed on the SAR ship detection dataset from the Gaofen-3, RadarSat-2, Sentinel-1, and TerraSAR satellites. The experimental results consistently demonstrate the superiority of our method over the existing state-of-the-art methods. Besides, detailed model analysis experiments further validate the effectiveness of our proposed method in SAR image ship detection tasks. The code will be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/shuangliumax/MSNet/</uri>

In recent years, the rapid development of Deep Learning (DL) has provided a new method for ship detection in Synthetic Aperture Radar (SAR) images. However, there are still four challenges in this task. (1) The ship targets in SAR images are very sparse. A large number of unnecessary anchor boxes may be generated on the feature map when using traditional anchor-based detection models, which could greatly increase the amount of computation and make it difficult to achieve real-time rapid detection. (2) The size of the ship targets in SAR images is relatively small. Most of the detection methods have poor performance on small ships in large scenes. (3) The terrestrial background in SAR images is very complicated. Ship targets are susceptible to interference from complex backgrounds, and there are serious false detections and missed detections. (4) The ship targets in SAR images are characterized by a large aspect ratio, arbitrary direction and dense arrangement. Traditional horizontal box detection can cause non-target areas to interfere with the extraction of ship features, and it is difficult to accurately express the length, width and axial information of ship targets. To solve these problems, we propose an effective lightweight anchor-free detector called R-Centernet+ in the paper. Its features are as follows: the Convolutional Block Attention Module (CBAM) is introduced to the backbone network to improve the focusing ability on small ships; the Foreground Enhance Module (FEM) is used to introduce foreground information to reduce the interference of the complex background; the detection head that can output the ship angle map is designed to realize the rotation detection of ship targets. To verify the validity of the proposed model in this paper, experiments are performed on two public SAR image datasets, i.e., SAR Ship Detection Dataset (SSDD) and AIR-SARShip. The results show that the proposed R-Centernet+ detector can detect both inshore and offshore ships with higher accuracy than traditional models with an average precision of 95.11% on SSDD and 84.89% on AIR-SARShip, and the detection speed is quite fast with 33 frames per second.

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.

Ship detection from synthetic aperture radar (SAR) images is a hot topic, but the difficulty in collecting labeled SAR images may hinder the development of deep-learning-based detection methods. Inspired by the idea of domain adaptation, in this article, we propose a hierarchical similarity alignment neural network (HSANet) for ship detection in SAR images, which is a domain adaptive (DA) approach with optical remote sensing images as training samples. The kernel target of HSANet is to mine and align both the global structure and the local instance information between SAR and optical images, where two modules, structural alignment module (SAM) and prototype alignment module (PAM), are designed to, respectively, conduct two hierarchies of alignment process. In general, SAM attempts to extract the global structure similarity which exists in image-level feature representation, while PAM tends to extract the local shape similarity which is instance-level representation. To be specific, SAM is developed by Fourier-based feature alignment, which tries to describe the similar structural relationship between optical and SAR images. Meanwhile, PAM is proposed based on the conjoint confidence analysis where the instance-level ship representations of the source and target domains is aligned. SAM and PAM work together to construct a hierarchical domain adaptation network for SAR ship detection. Experiments on several public datasets may indicate the effectiveness of the proposed method.

Deep learning has been widely applied to ship detection in Synthetic Aperture Radar (SAR) images. Unlike optical images, the current object detection methods have the problem of weak feature representation due to the low object resolution in SAR images. In addition, disturbed by chaotic noise, the features of classification and location are prone to significant differences, resulting in classification and location task misalignment. Therefore, this paper proposes a novel SAR ship target detection algorithm based on Cross-Attention Mechanism (CAM), which can establish the information interaction between the classification and localization task and strengthen the correlation between features through attention. In addition, to suppress the noise in multi-scale feature fusion, we designed an Attention-based Feature Fusion Module (AFFM), which uses the attention information between channels to perform the re-weighting operation. This operation can enhance useful feature information and suppress noise information. Experimental results show that on a benchmark SAR Ship Detection Dataset (SSDD), the Fully Convolutional One-Stage Object Detector (FCOS) with ResNet-50 backbone network was optimized to improve AP by 6.5% and computational cost by 0.51%. RetinaNet with ResNet-50 backbone network was optimized to improve AP by 1.8% and computational cost by 0.51%.

With the development of deep learning (DL) and synthetic aperture radar (SAR) imaging techniques, SAR automatic target recognition has come to a breakthrough. Numerous algorithms have been proposed and competitive results have been achieved in detecting different targets. However, due to the influence of various sizes and complex background of ships, detecting multiscale ships in SAR images is still challenging. To solve the problems, a novel network, called attention receptive pyramid network (ARPN), is proposed in this article. ARPN is a two-stage detector and designed to improve the performance of detecting multiscale ships in SAR images by enhancing the relationships among nonlocal features and refining information at different feature maps. Specifically, receptive fields block (RFB) and convolutional block attention module (CBAM) are employed and combined reasonably in attention receptive block to build a top-down fine-grained feature pyramid. RFB, composed of several branches of convolutional layers with specifically asymmetric kernel sizes and various dilation rates, is used for grabbing features of ships with large aspect ratios and enhancing local features with their global dependences. CBAM, which consists of channel and spatial attention mechanisms, is utilized to boost significant information and suppress interference caused by surroundings. To evaluate the effectiveness of ARPN, experiments are conducted on SAR Ship Detection Dataset and two large-scene SAR images. The detection results illustrate that competitive performance has been achieved by our method in comparison with several CNN-based algorithms, e.g., Faster-RCNN, RetinaNet, feature pyramid network, YOLOv3, Dense Attention Pyramid Network, Depth-wise Separable Convolutional Neural Network, High-Resolution Ship Detection Network, and Squeeze and Excitation Rank Faster-RCNN.

Fisher vectors (FVs) can capture multiple order information from superpixels (SPs) in synthetic aperture radar (SAR) images. Existing FV-based ship detectors mainly exploit the local contrast of FVs (LCFVs) but do not consider their global density features. This may lead to degraded performance in terms of discrimination between ship targets and the complex sea clutter. In this article, two new global cues from FVs are designed based on the fact that target FVs exhibit much lower densities than those of clutter FVs and also have large distances to the latter. Our two new global cues can suppress the sea clutter and significantly enhance ship targets throughout the SAR image. We also design an improved local cue from FVs for ship detection, in which the intensity contrast of SPs is incorporated into the existing LCFV indicator to reduce false alarms. By fusing the above two new global cues (and an improved local cue from FVs), we propose a new method for ship detection in SAR images. Experimental results based on Gaofen-3 SAR images show that the newly proposed detector provides better detection performance than other state-of-the-art detectors, especially in the presence of strong and highly heterogeneous sea clutter.

Density-based ship detection in SAR images: Extension to a self-similarity perspective

Ship detection in synthetic aperture radar (SAR) images is critical to ocean surveillance and rescue. Although many deep learning SAR ship detection methods have been proposed, the performance of these methods depends on the size and quality of the training samples. To resolve these issues, this letter presents an unsupervised ship detection method in SAR images using superpixel segmentation and cross stage partial network (CSPNet). First, the SAR image is over-segmented into superpixels based on our previously proposed superpixel generation algorithm. Then, the complex signal kurtosis (CSK) and a local superpixel contrast are integrated as a statistical indicator for the automatic identification of ship superpixels and background superpixels, thus leading to generation of training samples. Finally, the segmented superpixels are input to the CSPNet, which can learn a representative feature set with high discrimination ability between ships and backgrounds. Our method can achieve pixel-level detection map rather than the bounding box result. Experiments based on the Gaofen-3 and TerraSAR SAR data demonstrate that our method can achieve above 90% actual detection rate.

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.

Transformer-based methods have demonstrated their potential capabilities in ship detection of synthetic aperture radar (SAR) images. However, their exclusive reliance on global context information hinders the precise localization of small ships, leading to suboptimal detection performance. In this work, a global-to-local detection transformer (GL-DETR) framework is proposed to enhance the detection accuracy of small ships in SAR images. In GL-DETR, the decoder is constituted of a global layer and a carefully designed local layer. Within the global layer, object queries fully interact with global context information. Furthermore, a local interaction attention (LIA) module is designed in the local layer, with the purpose of refining and enriching the object query features through local multiscale region of interest (ROI) information. Additionally, a multiscale information enhancement (MIE) module is introduced to enhance the high-frequency information of small ships through Gaussian filtering. Numerical results demonstrate that GL-DETR outperforms baseline by 3.71% and 4.89% on classical SAR datasets LS-SSDD and HRSID, respectively. These results demonstrate the effectiveness and superiority of the proposed strategy.

A bilateral constant false alarm rate (CFAR) algorithm for ship detection in synthetic aperture radar (SAR) images is proposed in this letter. Compared to the standard CFAR algorithm, the proposed algorithm can reduce the influence of SAR ambiguities and sea clutter, by means of a combination of the intensity distribution and the spatial distribution of SAR images. The spatial distribution plays an equally important role as the intensity distribution. It is estimated before ship detection by a new kernel density estimation algorithm proposed in this letter. The experimental results of typical SAR images show that the algorithm is effective.

Recently, detectors based on deep learning have boosted the State-of-the-Art (SOTA) of application on ship detection in synthetic aperture radar (SAR) images. However, constructing discriminative feature from scattering of background and distinguishing contour of ship precisely still present challenging subject to the inherent scattering mechanism of SAR. In this article, a dual-branch detection framework with perception of scattering characteristic and geometric contour is introduced to deal with the problem. Firstly, a scattering characteristic perception branch is proposed to fit the scattering distribution of SAR ship through conditional diffusion model, which introduces learnable scattering feature. Secondly, a convex contour perception branch is designed as two-stage coarse-to-fine pipeline to delimit the irregular boundary of ship by learning scattering key points. Finally, a cross-token integration module following Bayesian framework is introduced to couple features of scattering and texture adaptively to learn construction of discriminative feature. Furthermore, comprehensive experiments on three authoritative SAR datasets for oriented ship detection demonstrate the effectiveness of proposed method.

Ship detection in synthetic aperture radar (SAR) images has witnessed rapid development in recent years, especially after the adoption of convolutional neural network (CNN)-based methods. Recently, a transformer using self-attention and a feed forward neural network with a encoder-decoder structure has received much attention from researchers, due to its intrinsic characteristics of global-relation modeling between pixels and an enlarged global receptive field. However, when adapting transformers to SAR ship detection, one challenging issue cannot be ignored. Background clutter, such as a coast, an island, or a sea wave, made previous object detectors easily miss ships with a blurred contour. Therefore, in this paper, we propose a local-sparse-information-aggregation transformer with explicit contour guidance for ship detection in SAR images. Based on the Swin Transformer architecture, in order to effectively aggregate sparse meaningful cues of small-scale ships, a deformable attention mechanism is incorporated to change the original self-attention mechanism. Moreover, a novel contour-guided shape-enhancement module is proposed to explicitly enforce the contour constraints on the one-dimensional transformer architecture. Experimental results show that our proposed method achieves superior performance on the challenging HRSID and SSDD datasets.

Due to its great application value in the military and civilian fields, ship detection in synthetic aperture radar (SAR) images has always attracted much attention. However, ship targets in High-Resolution (HR) SAR images show the significant characteristics of multi-scale, arbitrary directions and dense arrangement, posing enormous challenges to detect ships quickly and accurately. To address these issues above, a novel YOLO-based arbitrary-oriented SAR ship detector using bi-directional feature fusion and angular classification (BiFA-YOLO) is proposed in this article. First of all, a novel bi-directional feature fusion module (Bi-DFFM) tailored to SAR ship detection is applied to the YOLO framework. This module can efficiently aggregate multi-scale features through bi-directional (top-down and bottom-up) information interaction, which is helpful for detecting multi-scale ships. Secondly, to effectively detect arbitrary-oriented and densely arranged ships in HR SAR images, we add an angular classification structure to the head network. This structure is conducive to accurately obtaining ships’ angle information without the problem of boundary discontinuity and complicated parameter regression. Meanwhile, in BiFA-YOLO, a random rotation mosaic data augmentation method is employed to suppress the impact of angle imbalance. Compared with other conventional data augmentation methods, the proposed method can better improve detection performance of arbitrary-oriented ships. Finally, we conduct extensive experiments on the SAR ship detection dataset (SSDD) and large-scene HR SAR images from GF-3 satellite to verify our method. The proposed method can reach the detection performance with precision = 94.85%, recall = 93.97%, average precision = 93.90%, and F1-score = 0.9441 on SSDD. The detection speed of our method is approximately 13.3 ms per 512 × 512 image. In addition, comparison experiments with other deep learning-based methods and verification experiments on large-scene HR SAR images demonstrate that our method shows strong robustness and adaptability.