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”).

Synthetic aperture radar (SAR) automatic target recognition (ATR) has attracted extensive attention and achieved satisfactory results. However, most SAR ATR methods follow the closed-set assumption, which assumes that all target classes in the test set have been contained by the training set. In actual scenarios, it may encounter the target classes that are not included in the training set, and it presents a challenge for SAR ATR. To tackle this issue, this letter proposes an open-set recognition method based on joint training of class-specific sub-dictionary learning. First, joint training is used to optimize the sub-dictionary learning process, and it could significantly enhance the discriminative ability of these sub-dictionaries. Second, the reconstruction errors of the targets on each sub-dictionary are calculated. These errors can be split into matched and no-matched errors. Third, extreme value theory (EVT) is employed to model the matched and no-matched errors of each class, which could determine the class boundaries. Finally, for the target to be recognized, its reconstruction errors on each sub-dictionary are calculated individually. The class of this target can be determined by comparing the errors with these class boundaries. Our method achieved an accuracy of 87.22–94.02 and an F1 score of 88.03–90.25 in multiple experiments on moving and stationary target automatic recognition (MSTAR) dataset. Compared with several state-of-the-art methods, it has better accuracy and robustness.

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.

SAR target classification is an important application in SAR image interpretation. In practical applications, the battlefield is open and dynamic, and the SAR target classification model often encounters the targets of unknown classes. However, most of the existing SAR target classification methods follow the close-set assumption. It makes them only classify several fixed classes of targets and can’t deal with the targets from unknown classes. To this end, this paper proposes a novel SAR target classification method. This method can not only classify the targets from known classes and search targets from unknown classes but also incrementally update the classification model with these unknown class targets. Specifically, an autoencoder improved by MS-SSIM (multi-scale structural similarity) loss is utilized to extract targets’ features, and it can better utilize the structural information in SAR images. Next, the classifier based on EVT (Extreme Value Theorem) is established, which can classify the known class targets and search the unknown class targets. Then, we perform improved model reduction on the established classifier. This operation could speed up the model and prepare for incremental learning. Finally, after manually labeling those unknown class targets, the classifier is updated with these data in incremental form. Experimental results on the MSTAR (Moving and Stationary Target Automatic Recognition) dataset indicate that, compared with the state-of-the-art methods, our proposed method has better performance in open set recognition and incremental learning.

Oriented object detection (OOD) in remote sensing image interpretation is challenging due to the difficulty of locating objects with arbitrary orientations. Existing methods have made considerable progress based on oriented heads or anchors. However, most of them follow the classical detection paradigm, such as assigning samples based on Intersection-over-Unions (IoU) and predicting through two independent tasks. These fixed strategies impair the consistency between classification and localization predictions, resulting in the prediction with optimal localization accuracy being suppressed by the nonoptimal ones during nonmaximum suppression (NMS). To address this problem, a task-collaborated detector (TCD) is proposed. Compared with current single-stage methods, its improvements include two aspects: task-collaborated assignment (TCA) and task-collaborated head (TCH). Specifically, to better pull closer the best anchors for two tasks, TCA introduces classification and localization confidence into sample assignment and tends to select the anchors with accurate and consistent predictions as positive during training. TCH provides a better balance for learning interactive and discriminative features. It can flexibly adjust the spatial feature distribution of classification and localization tasks by learning the joint features from the aggregation layer. Extensive experiments are conducted on HRSC2016, DOTA, and DIOR-R, and the proposed TCD achieves the state-of-the-art performance [90.60, 80.89, and 65.04 mean average precision (mAP), respectively]. Consistency analysis also demonstrates that TCD can significantly improve prediction consistency.

Most of the existing synthetic aperture radar (SAR) automatic target recognition (ATR) methods aim at the closed set situation, in which the classes of targets in the test set have appeared in the training set. However, in practice, the classifier is likely to encounter the targets from unseen categories and classify them incorrectly, which brings a huge challenge to current SAR ATR techniques. To overcome this problem, this letter proposes an open set recognition (OSR) method based on multitask learning, and the method is developed from generative adversarial network (GAN). Essentially, this method decomposes OSR into two tasks: classification and abnormal detection. The classification task is the same as that in the closed set situation, while the abnormal detection task is used to determine whether the targets belongs to the unseen categories. Correspondingly, the network structure of GAN is modified and the other full-connection network branch is added to the end of the discriminator, so it has the ability to accomplish the above two tasks. Finally, according to the results of two tasks, the OSR for SAR targets can be realized. The experimental results on moving and stationary target acquisition (MSTAR) dataset demonstrate that the proposed method has the better recall, precision,$F1$, and accuracy than other OSR methods.

Although existing oriented object detection methods have made considerable progress based on oriented heads or anchors, the training process itself is not perfect. In this letter, we point out the inconsistency problem between the fixed network setting and varying aspect ratios, which greatly limits the performance. For example, the fixed parameters in label assignment and regression loss cannot fit the changes of aspect ratios and, thus, are harmful to the training process. Considering the prior information about objects’ aspect ratios, the aspect-ratio-guided (ARG) methods are proposed. Specifically, the ARG label assignment is used to adjust the label assignment criteria (intersection over union (IoU) threshold) automatically, and the ARG IoU loss can change the weights of angle regression dynamically. This ARG design makes better use of training samples and pushes the detector more robust to the change of aspect ratios. With no additional cost, our method improves upon the ResNet-50-feature pyramid network (FPN) baseline with 3.99% AP50 and 6.09% AP75 on HRSC2016.

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.

Ship detection and recognition in remote sensing images have important significance in military and civilian applications. Traditional methods have insufficient generalization ability in complicated scenes. The Faster R-CNN-based methods cannot predict the orientation of the ship. The R2CNN-based methods can predict the orientation of the ship but not considerate the scale of object in classification. In order to solve the problems mentioned above, this article proposes a scale enhancement rotating Cascade R-CNN network (SER-Cascade). Using the multistage network of rotating Cascade R-CNN, the output of the previous stage is fed to current stage, which can effectively regress the orientation of the ship. To improve the recognition performance of multi-class ships, a novel RoI pooling method is proposed in this article, in which the scale information is enhanced and context information is reserved. To evaluate the proposed networks, a dataset named HR-SHIP-15 that currently contains 15 categories of ship targets has been produced for ship recognition. Experiments are conducted on HR-SHIP-15 dataset, and the results verify that the proposed method has state-of-the-art performance.

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.

Subject to the statistical distribution assumption and the fixed window shape, the classical edge detectors for polarimetric synthetic aperture radar (PolSAR) images generally generate inaccurate results in heterogeneous scenes. In this letter, a PolSAR image edge detector integrating the scattering characteristics and optimal contrast (OC) is proposed. Hierarchical model-based decomposition is first implemented for the scattering mechanism characterization. On this basis, a scattering mechanism-driven adaptive window is then designed, which contains pixels with uniform polarimetric scattering. Finally, to avoid making the assumption, the OC measurement is adopted for the edge strength calculation. Experimental results conducted on different PolSAR data confirm the effectiveness of the proposed method and its superiority over the classical edge detectors, especially in heterogeneous areas.

This paper concerns two polarization orientation parameters of built-up areas derived from the polarimetric synthetic aperture radar (PolSAR) data considering two modeling orthogonal dihedral structures. The first orientation parameter is derived from the well-known circular polarization algorithm with the enrichment of arc distance median filtering using an adaptive neighborhood. The derivation of the second orientation parameter is realized by combining the slope-induced changes in polarimetric orientation angle with the shape-from-shading technique. The combination provides the possibility to measure the incidence angle and the azimuth component of the terrain slopes from the cross-pol SAR intensity image. With reference to the cross scattering model, a doubled cross scattering model (DCSM) is introduced by incorporating the orientation parameters, thus serving to guide the model-based decomposition. Using the DCSM refines the estimation of the cross-pol component by enabling us to further reveal the scattering characteristics of built-up areas. Following a novel criterion, the decomposition is implemented at two layers: one for urban areas and one for nonurban areas. The performance of parameter derivation is demonstrated and evaluated with airborne synthetic aperture radar, uninhabited aerial vehicle synthetic aperture radar, and GF-3 fully PolSAR data over different test sites. The decomposed results are consistent with the reference information provided by the National Land Cover Database 2011 about the land cover classification of test sites and encourage the use of the proposed decomposition scheme for different applications.

This paper proposes a novel registration method for optical and SAR images which is based on straight line features and mutual information. Firstly, different edge detectors are employed to detect the line segments in both optical and SAR images respectively. Then, through the Hough transform and a straight line fitting and filtration method, the main straight lines of each image are extracted and their intersections are obtained and taken as the candidate matching points. With the RANSAC (RANdom SAmpling Consensus) method, corresponding point pairs (CPPs) are found with these candidate points and a coarse registration between the heterogeneous images is implemented. At last, by using the mutual information of the separated patches generated from the coarse registered images, a fine registration result is finally achieved. The experiment with a pair of X-band air-borne SAR and optical images validates the efficiency and precision of the proposed method.

It is the basis of realization ship recognition to accurately extract geometric parameters of the ship target in synthetic aperture radar (SAR) images. Due to the unique SAR imaging mechanism, speckle noise, azimuth ambiguity and side lobe effect seriously impact on geometric parameter estimation of the ship target. Therefore, a method is presented to extract geometric parameters of the ship based on ellipse fitting and the gradient vector flow (GVF) snake with shape priors.

Shape is considered to be one of the most promising tools to represent and recognise an object. In this study, an effective and rigorous shape matching algorithm is developed based on a new descriptor and relaxation labelling technique. For each contour point, the descriptor captures the distribution of all points within the shape region along the vector perpendicular to that from the centroid to the point. In addition to stable affine invariance, the descriptor is robust to noise since it makes use of all points in the shape region. The descriptor distance is used to initialise the contour point matching probability, and relaxation labelling technique is utilised to update the matching probability using a new compatibility coefficient function, which is defined based on the shape projection preserving characteristic. The experiments on synthetic and real remote sensing data are provided to test the performance of the authors’ proposed algorithm. Compared to other four state‐of‐the‐art contour‐based shape matching algorithms, their algorithm is more robust and capable of shape matching under affine transformations and noise.

Change detection is a process of identifying changes in the state of objects between the reference and test images. This letter presents a target prescreening method that employs the change detection technique for automatic target recognition in synthetic aperture radar (SAR) images. First, four translated versions of an original SAR image are generated, and the corresponding four likelihood ratio images are computed. Then, a robust threshold is derived from the ratio of the histogram at two adjacent gray-level values of the likelihood ratio images. Finally, the threshold is applied to perform the prescreening. The proposed method implements the procedure without any prior knowledge and overcomes the weak adaptability of traditional algorithms. Two different real X-band airborne SAR images acquired over Beijing are used to quantitatively and qualitatively demonstrate the effectiveness of the proposed method.

In this paper, a new azimuth ambiguities removal method is introduced for ship detection by Time-Frequency (TF) analysis. A TF coherence indicator is proposed to filter ghost echoes due to the different TF coherence characteristics between real ship target echoes and ambiguous ones. The effectiveness of this proposed TF coherence indicator for ship detection is demonstrated using single polarimetric spaceborne TerraSAR-X coherent data over the test sea/ocean site in Hongkong, China.

Most of methods for change detection of floods in SAR images are based on the difference image (DI), which is from comparing the flood image and the reference image straightforwardly. DI, however, is often distorted by the interferences of the complicated reference image. Meanwhile, the spatial contextual information of floods is difficult to be effectively used. In response to these problems, a novel change detection method by removing the interference of the reference image is proposed. Firstly, the difference image is segmented to derive the initial change mask, i.e. a part of flood areas. Secondly, the whole flood areas are gained by region growing directly in the flood image. Experimental results on the real ERS SAR dataset validate its effectiveness on both the quantitative and subjective aspects.

Moment has been widely used for contour matching. To use the moment to achieve contour matching under affine transformations, the affine‐invariant support point set (SPS) should be constructed first. Then, a novel method of acquiring SPS based on the contour projection (SPS‐CP) is proposed here. For an arbitrary selected contour point, the contour is projected onto the line vertical to the vector connecting the contour centroid and the selected point, and the contour points with the sampled projection values are picked up to form the SPS‐CP of the point. SPS‐CP which captures the global structure of the contour is stably affine‐invariant. Experiments on synthetic and real data demonstrate that moments generated from SPS‐CP outperform those generated from SPSs sampled by uniform spacing or affine length.

This paper presents a method in finding tie points automatically in synthetic aperture radar (SAR) image pairs based on the extracted closed regions. There are mainly three steps during this process. The first step is to extract the closed regions in the SAR image with an image segmentation approach deducted by the geodesic active contour (GAC) model. Then, a polygonal approximation process is adopted to locate the feature points on the boundaries of these regions. With the obtained feature points, geometric hashing theory is employed to match these feature points as the tie points. A pair of simulated SAR images and a pair of high-resolution airborne SAR images are used to test and evaluate the proposed method. The experimental results show that the proposed method is effective and appropriate for the acquisitions of tie points in SAR image pairs.

This letter presents a threshold selection method in change detection (CD) with synthetic aperture radar (SAR) images, which combines the characteristics of two different CD measures by using the Markov random field model. One is the well-known log-ratio CD measure, and the other is derived from the likelihood ratio and is based on the statistical properties of SAR intensity images. The proposed unsupervised CD algorithm overcomes the shortcomings and strengthens the advantages of these two measures. The experimental results with two pairs of SAR images show that the proposed algorithm is effective and better than the algorithms using the two aforementioned CD measures.

The precise estimation of the baseline is a crucial procedure in repeat-pass interferometric synthetic aperture radar (InSAR) applications. Using the ephemeris of the satellite, a polynomial regression algorithm can fit the satellite orbit at the third or higher order with a main shortcoming that the mutual constraints among the three dimensions defining the orbit are missed. In this paper, a new approach is presented to fit the satellite orbit based on the assumption that the satellite orbit is a 3-D ellipse, which retains the relations among the three dimensions. Considering the complexity of 3-D ellipse parameters estimation, the 3-D orbit is first transformed into three 2-D ellipses. Then, the parameters of these 2-D ellipses are estimated with a direct least-square ellipse fitting method (DLS-EFM). These two orbit fitting algorithms are tested with ten sets of advanced land observation satellite phased array L-band SAR data, which were acquired in north Toronto, Ontario, Canada, from September, 2008 to January, 2009. Moreover, two of them acquired with an adjacent period were chosen to form a repeat-pass InSAR, and the corresponding baseline is calculated with the proposed method as an example. The experimental results show that the error of the satellite position using DLS-EFM is at a submetric level, which is less than one-tenth of that of the polynomial regression algorithm. Consequently, the proposed method is appropriate for the baseline estimation in spaceborne InSAR applications.