This work proposes the use of a robust geometrical segmentation algorithm to detect inherent shapes from dense point clouds. The points are first divided into voxels based on their connectivity and normal consistency. Then, the voxels are classified into different types of shapes through a multi-scale prediction algorithm and multiple shapes including spheres, cylinders, and cones are extracted. Next, a hybrid voting RANSAC algorithm is adopted to separate the point clouds into corresponding segments. The point–shape distance, normal difference, and voxel size are all considered as weight terms when evaluating the proposed shape. Robust voxels are weighted as a whole to ensure efficiency, while single points are considered to achieve the best performance in the disputed region. Finally, graph-cut-based optimization is adopted to deal with the competition among different segments. Experimental results and comparisons indicate that the proposed method can generate reliable segmentation results and provide the best performance compared to the benchmark methods.

In recent years, synthetic aperture radar (SAR) ship recognition based on deep learning has received extensive attention and research. The accuracy and reliability of the recognition model can be effectively improved by fusing physical scattering characteristics and image features, which has become one of the important trends in SAR recognition research. However, there are some challenges in feature extraction and fusion of the scattering characteristics and image features. To address these issues, a new dual-driven deep learning model with local-structure physical scattering characteristics and fine-grained image features (DDLM) is proposed. First, an integrated Local and Global physical Scattering characteristics Aware Module (LGSAM) is constructed. The spatial information and structural attributes of the sets of attribute scattering centers (ASCs) are co-feature encoded to obtain local and global structural features of ASCs in the scattering domain. Second, an Adaptive Multi-scale feature Refinement extraction Module (AMRM) is designed. The AMRM can extract multi-scale fine-grained features and focus more on the target, thereby effectively reducing interference from background noise in the image domain. Third, based on the significant differences in the information and structure contained in the scattering characteristics and image features, a Differential Feature Fusion of semantic alignment Module (DFFM) is developed. The DFFM can effectively fuse the differential features extracted, fully utilize the complementarity between the two features, and strengthen the learning ability as well as the interpretability of the model. Finally, experimental validation is carried out using the ComplexSAR_Ship and the MSTAR dataset. The DDLM achieves Accuracy, Precision, Recall, and F1 of 95.32, 89.30, 89.45, and 89.28, respectively, in ComplexSAR_Ship.

Accurate SAR-AIS association is critical for maritime monitoring but faces three key challenges: 1) The sampling frequency of AIS varies irregularly, making it difficult to predict the trajectory. 2) The available features of sailing and anchored targets are not uniform and the distribution of position errors is different among different motion states, making conventional non-discriminative methods less universally applicable in different scenes. 3) The false alarms and missed detections in target-dense region which seriously affect the optimal association, result many wrong and missed associations. To solve the above problems, firstly, a Dual stage AIS Encoding (DAE) module is constructed to extract the features of AIS and unify feature dimensions. Secondly, a dual Geometric Transformer module is proposed to simultaneously consider the features of sailing and anchored targets as well as target topology and deriving the association results via sinkhorn method. Thirdly, overlap loss is introduced to consider the feature differences between SAR and anchored AIS, and restrict the wrong and missed association. The experimental results show that based on the simulation data, it improves 11.5-20.7%, 5.5-14.1%, and 9.1-20.4% in precision, F1 score, and overall accuracy respectively, compared with the GNN, DS evidence theory, Fuzzy decision-making method and IT2FS; based on the real data, it improves 4.3-7.9%, 1.8-5.6%, and 3.5- 10.2%, respectively. The proposed method yields more accurate and reliable association results.

Weak-echo ship target detection in GNSS-based bistatic synthetic aperture radar is severely limited by the coupled effects of burst-type strong windows and polarization mismatch, cross-frequency mis-registration, and long-sequence chain drift in dual-frequency dual-polarization observations. To address these issues, this paper proposes DFDP-QuadDiff, a dual-frequency dual-polarization quad-differential framework for weak-echo ship target detection using B1/B3 × horizontal–horizontal (HH)/vertical–vertical (VV) four-channel complex range-time data. The proposed framework integrates polarization-consistency-driven strong-window suppression, intra-band adaptive polarimetric synthesis, joint delay–Doppler–phase cross-frequency registration, segment-wise Jones drift calibration, and quality-aware final fusion in a unified hierarchical processing chain. In this way, multi-source inconsistencies are progressively constrained and suppressed from the polarization level to the segment level before final accumulation and detection are performed. Experimental results on self-developed four-channel GNSS-S demonstrate that, relative to the best raw single-channel result, the proposed framework increases the median SCR from 6.51 dB to 9.04 dB (+2.53 dB), improves the P10 SCR from −1.76 dB to 3.05 dB (+4.81 dB), and raises the track continuity from 0.85 to 0.97. In addition, the standard deviation of segment-wise delay drift is reduced from 0.97 bin to 0.29 bin, and positive multi-scale accumulation gains are maintained up to the second-long integration range. These results indicate that the proposed framework not only substantially enhances the stability, continuity, and long-time integrability of weak-target responses under low-SNR maritime conditions, but also maintains robust gains under weak-visibility, interference-dominant, and mismatch-sensitive local conditions in the stratified evaluation, thereby establishing a physically interpretable and implementation-ready solution for collaborative weak-target detection in dual-band dual-polarization GNSS-S.