Arbitrary-oriented ship detection in SAR imagery remains especially challenging due to multi-scale imbalance and the characteristics of SAR imaging, a problem that is more pronounced than in optical ship detection. Unlike optical images, SAR data often lack rich textural and color cues, instead exhibiting non-uniform scattering, speckle noise, and non-standard elliptical ship shapes, all of which make robust feature extraction and bounding box regression significantly more difficult across different scales. To address these unique SAR-specific challenges, this paper proposes the Multi-Scale Dynamic Feature Fusion Network (MSDFF-Net) aims to alleviate multi-scale imbalance in three main ways. First, a Multi-Scale Large-Kernel Convolution Block (MSLK-Block) integrates large-kernel convolutions with partitioned heterogeneous operations to enhance multi-scale feature representation, tackling wide-ranging ship sizes under noisy conditions. Second, a Dynamic Feature Fusion Block (DFF-Block) handles scale-based feature utilization imbalance by adaptively balancing spatial and channel information, thereby reducing interference from clutter and strengthening discrimination for diverse-scale ships. Third, we propose the Gaussian Probability Distribution (GPD) loss function, which models ships’ elliptical scattering properties and mitigates regression loss imbalance for targets of varying scales and orientations. Experimental evaluations on the R-SSDD, R-HRSID, and CEMEE datasets demonstrate that MSDFF-Net reaches top-tier performance standards, outperforming 21 existing deep learning-based SAR ship detectors. Specifically, MSDFF-Net achieves 93.95% precision, 94.72% recall, 91.55% mAP, 94.33% F1-Score, and 135.79 FPS on the R-SSDD dataset, with a parameter size of only 8.94 M. Additionally, MSDFF-Net exhibits strong transferability across large-scale SAR images, making it suitable for real-world deployment. The code and datasets can be accessed publicly at https://github.com/SZZ-SXM/MSDFF-Net.

Presents corrections to the paper, (Corrections to “Arbitrary-Direction SAR Ship Detection Method for Multiscale Imbalance”).

Ship recognition in synthetic aperture radar (SAR) is an essential challenge in SAR image interpretation. The measured SAR ship targets often contain complex background such as port facilities and neighboring ships, which are easy to interfere with the model and affect the recognition performance. To address this issue, a SAR ship recognition method with complex background based on dual-branch transformer fusion network is proposed in this paper. First of all, a dual-branch feature extraction and fusion architecture is designed in this paper, including significant feature extraction (SFE), global feature extraction (GFE), and dual-branch feature fusion (D-BFF). Specifically, the SFE effectively extracts the most discriminative local fine-grained features of ship target using multi-layer convolution of significant regions. The GFE capture global semantic information by residual module optimization. In addition, combined with the self-attention in the transformer block based on cross-attention and position encoding, the effective fusion of SFE and GFE is realized in D-BFF. Finally, extensive experiments are carried out based on Gaofen-3 seven-category dataset (anyone can get the dataset after sending the applying e-mail). The results reveal that the proposed method can achieve a recognition accuracy of 75.55%, which is significantly superior to other algorithms.

Timely monitoring of ships is imperative for ensuring the safety and security of maritime operations. Ship detection in synthetic aperture radar (SAR) is typically applicable to focused images. The time consumption of target detection primarily relies on the imaging process duration, encompassing intricate and time-intensive processing steps such as range migration correction and azimuth compression. Consequently, achieving real-time SAR ship detection poses a significant challenge. To address these issues, ship detection in the range-compressed domain of SAR has emerged as a viable approach. However, there is still a lack of reliable ship detection datasets that can satisfy the detection on the range-compressed domain. In this paper, we construct a dataset specifically designed for ship detection in range-compressed SAR data, called RCShip-1.0 (range-compressed ship dataset). The original data source is publicly available complex-valued data from the Sentinel-1 acquisition and the OpenSARShip-1.0 dataset, encompassing numerous ship targets. Subsequently, the inverse chirp scaling (ICS) algorithm is employed on the complex-valued data to acquire range-compressed SAR data. RCShip-1.0 encompasses training set, validation set, and test set acquired through two distinct approaches. It consists of 1580 large-scale SAR range-compressed images which are further divided into 18322 sub-images to facilitate subsequent display and analysis of detection results within large-scale SAR images. The experimental results demonstrate that each deep network achieves good performance on the dataset, with an F1-score exceeding 65%. The utilization of the RCShip-1.0 dataset in obtaining these experimental outcomes showcases its feasibility, standardization, and public availability.

The data distribution of synthetic aperture radar (SAR) vehicle targets in the actual missions is often imbalanced. However, the recent algorithms for SAR target recognition are designed either under abundant samples, or the situation of few labeled samples among all the categories. These cases all avoid facing the difficulties of imbalanced data distribution,i.e. the difference between the number of labeled samples among categories is huge. The samples in the majority classes will get more chances to be learnt by the deep neural network, which impedes the regular algorithms from achieving a high recognition rate. In this letter, a design guideline for imbalance loss and an example of data distribution (DD) loss based on the guideline is proposed, which provides an extremely effective way of handling the problem of imbalanced SAR target recognition. The DD loss takes the sample distribution and the data quantity of SAR vehicle targets into consideration. It can cause images with fewer samples in their categories to decrease more gradients proportionally. Moreover, the proposed DD loss adds no more burden to the networks and compared to other imbalanced algorithms with complex processes, the DD loss can be conducted easily. Plenty of experiments, which involve two various kinds of imbalanced datasets, are implemented and the proposed DD loss shows excellent performance among these imbalanced datasets. When there are only 40 labeled samples in minority categories, the DD loss can achieve over 95% in nine different cases, which exceeds other methods and losses of at least 7%.

In response to the problems existing in traditional SAR image target detection methods, such as complicated processes, long detection times, and poor detection effects in complex backgrounds, this paper proposes an integration of detection and recognition method based on deep learning for large-scene SAR images with vehicle targets. The paper introduces the issues of target identification through three stages of target detection, identification, and classification in traditional methods. To address these problems, this paper introduces a one-stage detection network based on YOLOv5 to construct a SAR image vehicle target detection and recognition algorithm. To verify the performance of the algorithm, this paper generated a dataset containing 10 different vehicle targets in large-scene SAR images and applied it to experiments. The results demonstrate that the algorithm has good performance and fast detection speed. The research results of this paper can provide important references for large-scale SAR image target detection.

In the context of ship monitoring in the ocean, targets are usually sparsely distributed. Thus, synthetic aperture radar (SAR) imaging of the whole scene is usually quite redundant and costly. However, raw SAR echo data were considered to be useless before focusing. Few studies have attempted to detect ships from raw SAR echo data. It seems to be an impossible task since the resolution of raw SAR echo data is too low. This article proposes a ship detection method for raw SAR echo data in view of a nonimaging target sensing paradigm. The core idea is that we can sense the existence of ships from raw SAR echo data without imaging. The underlying rationale is that the radar always speaks the same sentence, i.e., usually an exactly identical linear frequency modulated (LFM) signal, while target and clutter answer differently. The difference spread into each part of the whole echo sequence rather than only the focused energy after match filtering. Thus, the ships can be found by pattern analysis on one-dimension sequence data rather than two-dimension images. The experimental results based on simulation and typical real data validate our assumption. This study shows that SAR imaging is an unnecessary intermediate process and opens up new significant possibilities for ship detection in the vast ocean.

Most of synthetic aperture radar (SAR) based ship detection methods utilize two-dimension focused images. Ship detection in range-compressed data is promising since it needs no time-consuming azimuth focusing. This paper proposes a ship detection method in range-compressed SAR data, which employs the statistical characteristics and range trajectory of a ship target in the range-compressed time-domain. First, it employs complex signal kurtosis (CSK) to prescreen potential ship areas since CSK was demonstrated to be an reasonable indicator for SAR ship detection. Then, a convolutional neural networks (CNN) based discrimination is applied to the potential ship areas. The training samples comes from the simulation results of range trajectory based on the radar imaging parameters. Preliminary results show that the proposed method performs well in range-compressed SAR data.

Synthetic aperture radar (SAR) signatures of ship targets are often degraded by various types of distortions due to the distinctive imaging mechanism. The negative impacts could affect the characterization and identification of ships. Recently, complex signal kurtosis (CSK) was found to be a vital indicator of ship detection in SAR images. Since CSK can be used as an indicator of ship detection, we consider it can also be used to indicate ship target signature. One of the basic rationales is that the larger the CSK is, the easier it is to be detected and thus the more obvious its characteristics are as a ship. Defocusing and sidelobes are two common problems that affect the quality of ships. Their impacts on ship target signature are studied and compared from the perspective of CSK. Specifically, a ship target signature improvement methodology based on the maximum CSK criterion is proposed for both refocusing and sidelobe suppression. The role the CSK plays in these improvements and the underlying rationales are elaborated. In addition, a preliminary evaluation of the global imaging quality is also provided. Experimental results based on real data demonstrate that CSK can be used to indicate and improve ship target signature.

Synthetic aperture radar (SAR) target recognition faces the challenge that there are very little labeled data. Although few-shot learning methods are developed to extract more information from a small amount of labeled data to avoid overfitting problems, recent few-shot or limited-data SAR target recognition algorithms overlook the unique SAR imaging mechanism. Domain knowledge-powered two-stream deep network (DKTS-N) is proposed in this study, which incorporates SAR domain knowledge related to the azimuth angle, the amplitude, and the phase data of vehicles, making it a pioneering work in few-shot SAR vehicle recognition. The two-stream deep network, extracting the features of the entire image and image patches, is proposed for more effective use of the SAR domain knowledge. To measure the structural information distance between the global and local features of vehicles, the deep Earth mover’s distance is improved to cope with the features from a two-stream deep network. Considering the sensitivity of the azimuth angle in SAR vehicle recognition, the nearest neighbor classifier replaces the structured fully connected layer for$K$-shot classification. All experiments are conducted under the configuration that the SARSIM and the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset work as a source and target task, respectively. Our proposed DKTS-N achieved 49.26% and 96.15% under ten-way one-shot and ten-way 25-shot, whose labeled samples are randomly selected from the training set. In standard operating condition (SOC) as well as three extended operating conditions (EOCs), DKTS-N demonstrated overwhelming advantages in accuracy and time consumption compared with other few-shot learning methods in$K$-shot recognition tasks.

Synthetic Aperture Radar (SAR) is an important means for ship surveillance at sea due to its unique advantages of all-day and all-weather. This paper proposes a ship target detection method in SAR image based on Google Earth Engine remote sensing cloud computing platform. It can realize the real-time and fast detection of ship targets in a large area, the acquisition of ship target information and the batch download of SAR images in the detection area. Compared with the traditional ship target detection methods in SAR images, the proposed method is not limited to the acquisition, storage, utilization and batch processing of SAR satellite remote sensing data, and does not have high requirements on computer performance. Therefore, it has high practical application value for marine ship surveillance.

The C-band Sentinel-1 synthetic aperture radar (SAR) images are affected by radio frequency interference (RFI), and this article proposes an RFI detection and localization method for these images. First, a detailed analysis of RFI based on the generation mechanism is provided, which shows that RFI in the ground range detected (GRD) products differs from common backscattering not only in frequency spectrums but also in radiometric characteristics. Then, an RFI index (RFII) is proposed for RFI detection, which takes full advantage of unique RFI characteristics in dual-polarization GRD images. Finally, both detection results in ascending and descending passes are used to locate the ground RFI sources, assuming that the presence of RFI is persistent. Theoretical analyses show that the location area is a diamond of approximately 88.76 km2. Experimental results demonstrate that the proposed method performs quite well on Sentinel-1 GRD data. It provides a convenient and tractable tool for assessing Sentinel-1 data quality as well as for monitoring RFI. Thus, Sentinel-1 measurements can be used to monitor C-band RFI, which is an important task of electromagnetic spectrum management.

Water segmentation is a fundamental step for the information processing of SAR image, which plays an important role in ship detection, disaster monitoring and other applications. Because of the complexity of scenario in the SAR images, water segmentation of SAR image is a challenging task. Fewer convolutional neural networks (CNNs) have been developed for SAR image water segmentation in recent years, the accuracy and speed of CNN for water segmentation can be further improved. In this paper, we established a SAR water segmentation dataset based on the GF3 satellite data. then an improved water segmentation network based on Bilateral Segmentation Network (BiSeNet) is proposed. Further we propose a loss function based on edge area and a novel training data generation method to improve the segmentation ability of the network. Experimental results based on water segmentation dataset show that the proposed segmentation method has better segmentation accuracy and speed.

Recently, complex signal kurtosis (CSK) is found to be a vital indicator of ship detection in synthetic aperture radar (SAR) images. Since CSK can be used as an indicator of ship detection, we consider it can also be used to indicate ship target signature. One of the basic rationales is that the higher the CSK is, the easier it is to be detected and thus the more obvious its characteristics are as a ship. Presence of sidelobes and defocusing are two common problems that affect the quality of ship targets. They are discussed in this paper from the perspective of CSK. Experimental results show that CSK can be used to indicate and improve ship quality. We believe that ship detection and recognition can benefit from the CSK indicator.

Complex generalized Gaussian distribution (CGGD) is quite significant in synthetic aperture radar (SAR) modeling since original focused SAR data are complex-valued. However, the estimation method of the vital parameter of the CGGD, i.e., the shape parameter, is seldom studied. This letter proposes a fast shape parameter estimation method of the CGGD in SAR images. The proposed method is developed based on a concept in the complex signal processing field, i.e., complex signal kurtosis (CSK). Specifically, this letter provides an introduction to the CSK at first. Then, the relationship between the shape parameter and the CSK is elaborated. Finally, the estimation chain based on the relationship is proposed. Experimental results demonstrate that the proposed method outperforms the state-the-of-art, i.e., the maximum-likelihood (ML) method proposed by Novey et al. It works in a near-real-time fashion with good estimation precision, being much faster than Novey's method and achieving better performance in distinguishing different kinds of non-Gaussianity of typical SAR targets.

Synthetic aperture radar (SAR) ship detection is an important part of marine monitoring. With the development in computer vision, deep learning has been used for ship detection in SAR images such as the faster region-based convolutional neural network (R-CNN), single-shot multibox detector, and densely connected network. In SAR ship detection field, deep learning has much better detection performance than traditional methods on nearshore areas. This is because traditional methods need sea-land segmentation before detection, and inaccurate sea-land mask decreases its detection performance. Though current deep learning SAR ship detection methods still have many false detections in land areas, and some ships are missed in sea areas. In this letter, a new network architecture based on the faster R-CNN is proposed to further improve the detection performance by using squeeze and excitation mechanism. In order to improve performance, first, the feature maps are extracted and concatenated to obtain multiscale feature maps with ImageNet pretrained VGG network. After region of interest pooling, an encoding scale vector which has values between 0 and 1 is generated from subfeature maps. The scale vector is ranked, and only top K values will be preserved. Other values will be set to 0. Then, the subfeature maps are recalibrated by this scale vector. The redundant subfeature maps will be suppressed by this operation, and the detection performance of detector can be improved. The experimental results based on Sentinel-1 images show that the detection performance of the proposed method achieves 0.836 which is 9.7% better than the state-of-the-art method when using F1 as matric and executes 14% faster.

Recent studies have shown that complex information in single-channel synthetic aperture radar (SAR) imagery has practically always been underrated. This improves the perception of their potential for ocean monitoring. Based on the in-depth interpretation of complex signal kurtosis (CSK), this paper proposes a new ship detection method based on CSK in single-channel SAR imagery. The proposed method consists of two main parts, i.e., region proposal and target identification. The basic idea is to first detect potential ship locations based on the region proposal. Then, the final ship target is acquired based on the target identification. Compared to conventional methods based on detected products, e.g., the constant false alarm rate (CFAR), the proposed method has three advantages. First, CSK can take advantage of both non-Gaussianity and noncircularity, which is the fundamental concept distinguishing complex signal analysis from the real case. Second, the proposed method can be intrinsically free of false alarms caused by radio frequency interference (RFI). Finally, the proposed method can avoid missing detection in dense target situations. This methodology has been demonstrated over significant data sets acquired from Sentinel-1, TerraSAR-X, and Gaofen-3. These results validate that CSK is a vital indicator of ship detection. Complex information is expected to play a more important role in single-channel SAR imagery.

Complex information in single-channel synthetic aperture radar (SAR) imagery is seldom used. This is a common practice based on the conventional resolution theory. However, with the advent of high-resolution SAR sensors, information in the complex data has been found to be of significance for ocean applications. In particular, we note that there is a special type of instrumental artifact in Sentinel-1 images. It is rarely researched and may be attributed to radio frequency interference (RFI). It has similar intensity with ships and can degrade ocean interpretation performance severely. This paper proposes an innovative method to discriminate ships from RFIs based on noncircularity and non-Gaussianity. Among them, noncircularity is calculated based on the measure called normalized noncircularity, and non-Gaussianity is estimated based on the complex generalized Gaussian distribution. The discrimination rationale is analyzed in detail. The experimental procedure is based on Sentinel-1 interferometric wide swath products. Only cross-polarization data are tested since RFIs are quite weak in co-polarization data. It is found that noncircularity and non-Gaussianity can characterize and identify the difference between ships and RFIs. Ships present larger noncircularity and sup-Gaussianity while RFIs are found to exhibit quite low noncircularity and mainly show sub-Gaussianity. The proposed method achieves quite good performance. These results show that noncircularity and non-Gaussianity are extremely helpful complements for single-channel SAR imagery interpretation.

This paper proposes a ship detection method based on weighted support vector machines (SVM) and m-χ decomposition in compact polarimetric (CP) synthetic aperture radar (SAR) imagery. Firstly, the proposed method constructs the weighted feature vectors by extracting CP parameters. Each feature will be weighted by the ReliefF method. Then, ship targets in CP SAR imagery are detected by the weighted SVM classifier. Finally, false alarms are removed by scattering mechanism strength differences corresponding to three components of m-χ decomposition. NASA/JPL AIRSAR airborne quad-polarimetric (QP) data are used to simulate the CP data in the circular transmitlinear receive (CTLR) mode. Experimental results show that the method performs well in detecting ship targets, and can reject azimuth ambiguities.

Traditionally, phase content and information contained in the complex data in single-channel synthetic aperture radar (SAR) imagery is often discarded based on the conventional resolution theory. With the rapid development of SAR technology, however, ship target is no longer a point target but an extended target in high resolution SAR imagery. Thus, the conventional resolution theory is not strictly applicable to high resolution SAR imagery. Noncircularity can describe the distribution consistency between the real and imaginary parts. In this paper, we proposed a method using noncircularity parameters for ship detection in high resolution SAR imagery. The potential by using noncircularity parameters for ship detection is studied in detail. Experimental results based on TerraSAR-X data show that noncircularity parameters can identify ship targets well and can discriminate azimuth ambiguities. We believe that noncircularity parameters can benefit ship detection in various research aspects.

The deep convolutional neural network (CNN) has been widely used for target classification, because it can learn highly useful representations from data. However, it is difficult to apply a CNN for synthetic aperture radar (SAR) target classification directly, for it often requires a large volume of labeled training data, which is impractical for SAR applications. The highway network is a newly proposed architecture based on CNN that can be trained with smaller data sets. This letter proposes a novel architecture called the convolutional highway unit to train deeper networks with limited SAR data. The unit architecture is formed by modified convolutional highway layers, a maxpool layer, and a dropout layer. Then, the networks can be flexibly formed by stacking the unit architecture to extract deep feature representations for classification. Experimental results on the moving and stationary target acquisition and recognition data set indicate that the branched ensemble model based on the unit architecture can achieve 99% classification accuracy with all training data. When the training data are reduced to 30%, the classification accuracy of the ensemble model can still reach 94.97%.

Land masking is one of the most important stages for ship detection in synthetic aperture radar (SAR) images. However, a fast and efficient algorithm for land masking in SAR images is far from resolved. Current land masking algorithms are time-consuming or not accurate enough for ship detection in SAR images. In this paper, an algorithm for land masking is proposed. It is designed for ship detection in SAR images based on a series of image processing steps. Experimental results based on real SAR data demonstrate that the algorithm proposed in this paper is fast and accurate enough for ship detection in SAR images.

With the rapid development of spaceborne Synthetic Aperture Radar (SAR) and the increasing need of ship detection, research on adaptive ship detection in spaceborne SAR imagery is of very great importance. Focusing on practical problems of adaptive ship detection, this paper present a highly adaptive ship detection method for spaceborne SAR imagery. It applies two different detection strategies to high and low resolution SAR imagery respectively. By taking into account the imaging mode, incidence angle, polarization channel of SAR imagery, it implements the adaptive ship detection in spaceborne SAR imagery. Experimental results based on real data show that the proposed method is able to detect all ship targets adaptively in a real-time fashion.

Bilateral filtering is a technique to smooth images while preserving edges; it employs both geometric closeness and intensity similarity of neighbouring pixels. When intensity similarity of neighbouring pixels is very high, however, bilateral filtering weakens into Gaussian filtering. The performance does not improve significantly while the computation is still expensive. Many existing accelerated algorithms, however, ignored this basic fact. In this study, a hybrid bilateral filtering algorithm based on edge detection is proposed. By making use of edge detection, the proposed algorithm combines bilateral filtering and Gaussian filtering and its degree can be controlled by a threshold. Experimental results show that the proposed algorithm is able to reduce the computation efficiently and achieve better performance. What is more, the proposed algorithm shows potential to speed up existing accelerated bilateral filtering algorithms.

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.