Deep learning has offered new ideas in SAR ship target recognition. Although many methods improve the recognition performance through the improvement of loss function and migration of deep networks, scattering features as the important intrinsic features of SAR targets, need to be considered in the SAR ship recognition tasks. To introduce the scattering features into the deep network and characterize the features of ship targets more comprehensively, a multi-scale global scattering feature association network (MGSFA-Net) for SAR ship target recognition is proposed in this paper. In the network, the SAR ship target is firstly separated from the background by fine target segmentation. Then, the scattering centers (SCs) of ship targets are extracted and converted to local graph structures based on the $k$ -Nearest Neighbors (KNN) algorithm. The local graph structures are associated by the scattering center feature association (SCFA) module and enhanced by the multi-scale feature enhancement (MSFE) module to produce the multi-scale global scattering features. Moreover, the deep features of the targets are extracted by the multi-kernel deep feature extraction (MKDFE) module to characterize the high-dimensional information. Finally, the scattering features and deep features are fused by weighted integration to enrich the diversity of features. The experimental results on the FUSAR-Ship and OpenSARShip dataset show that, the MGSFA-Net can significantly improve the recognition performance, even on a few-shot condition with the accuracy increasing over 2%-3%. The feature distribution and visualization show the effectiveness of the MGSFA-Net to characterize the multi-scale global scattering association features.

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.

With the advent of the era of high-resolution remote sensing, semantic segmentation methods for solving pixel-level classification have been widely studied. Deep learning has significantly advanced deep feature extraction methods, becoming widely employed in remote sensing image analysis. Deep feature fusion methods is able to effectively combine features from different sources. Optical and synthetic aperture radar (SAR) images stand out as primary data sources in remote sensing, offering complementary and consistent information. Fusion of deep semantic features of optical and SAR images can alleviate the limitations of single-source images in application and improve semantic segmentation accuracy. Therefore, this article reviews the research on deep fusion of optical and SAR images in semantic segmentation tasks from four aspects. Firstly, we provide a summary of challenges and research methods pertinent to semantic segmentation of remote sensing images. Then the challenges and urgent needs of deep feature fusion of optical and SAR images are analyzed, and current research is summarized from the perspective of structural design by studying various feature fusion strategies. Additionally, the compilation and in-depth analysis of open-source optical and SAR datasets suitable for semantic segmentation are undertaken, serving as fundamental resources for future research endeavors. Finally, the article identifies the major challenges summarized from the literature review in this field, outlining expectations and potential future directions for researchers.

In practical SAR target detection applications, it is often encountered that the training and testing data come from different SAR sensors, leading to a decline in SAR target detection performance. Although domain adaptation methods can achieve model generalization through feature transfer, the change of scattering characteristics for the same target and the difference of feature distribution, caused by cross-sensor, cannot be ignored in SAR images. It is inevitable to lead to the escalation of the offset in the bounding box regression and deviation of the feature alignment. To address these issues, a cross-sensor SAR image target detection method based on dynamic feature discrimination and center-aware calibration is proposed. Based on the domain adaptation framework, initially, a Dynamic Feature Discrimination Module (DFDM) is introduced to address the exacerbated offset in the regression. A bidirectional spatial feature aggregation mechanism is employed to aggregate features in both horizontal and vertical directions and a multi-scale structure is adopted to enhance the scattering and semantic features, which can dynamically constrain the target position while improving target discrimination capability. Then, the Center-Aware Calibration Module (CACM) is designed to address the alignment deviation in feature transfer. The target salience relationship is modeled based on the distance between different positions and the target center to suppress background clutter interference. The perception center of the target is focused by combining the centerness map and classification map, which can calibrate the domain-invariant features and alleviate misalignment. Finally, the proposed method is tested on two datasets, MiniSAR and FARAD, and compared with the latest domain adaption methods. Both mAP and F1 values have improved by more than 6%-20%, verifying the effectiveness of the proposed method.

Ship recognition in synthetic aperture radar (SAR) is an essential challenge in SAR image interpretation. The measured SAR ship targets often contain complex backgrounds 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 a complex background based on a dual-branch transformer fusion network is proposed in this letter. First of all, a dual-branch feature extraction and fusion architecture is designed in this letter, 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 the ship target using multilayer convolution of significant regions. The GFE captures 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 the Gaofen-3 seven-category dataset (anyone can get the dataset after sending the application e-mail). The results reveal that the proposed method can achieve a recognition accuracy of 75.55%, which is significantly superior to other algorithms.