Point cloud is often regarded as a discrete sampling of Riemannian manifold and plays a pivotal role in the 3D image interpretation. Particularly, rotation perturbation, an unexpected small change in rotation caused by various factors (like equipment offset, system instability, measurement errors and so on), can easily lead to the inferior results in point cloud learning tasks. However, classical point cloud learning methods are sensitive to rotation perturbation, and the existing networks with rotation robustness also have much room for improvements in terms of performance and noise tolerance. Given these, this paper remodels the point cloud from the perspective of manifold as well as designs a manifold distillation method to achieve the robustness of rotation perturbation without any coordinate transformation. In brief, during the training phase, we introduce a teacher network to learn the rotation robustness information and transfer this information to the student network through online distillation. In the inference phase, the student network directly utilizes the raw 3D coordinate information to achieve the robustness of rotation perturbation. Experiments carried out on four different datasets verify the effectiveness of our method. On average, on the ModelNet40 and ScanObjectNN classification datasets with random rotation perturbations, our method improves classification accuracy by 4.41% and 3.65%, respectively, compared to popular rotation-robust networks. Similarly, on the ShapeNet and S3DIS segmentation datasets, our method achieves improvements in mIoU of 6.96% and 5.12%, respectively. Furthermore, the experimental results also demonstrate that our algorithm exhibits higher computational efficiency and stronger resistance to noise and outliers.

Due to the challenges of obtaining data from valuable targets, few-shot learning plays a critical role in synthetic aperture radar (SAR) target recognition. However, the high noise levels and complex backgrounds inherent in SAR data make this technology difficult to implement. To improve the recognition accuracy, in this paper, we propose a novel vision-language framework, VLF-SAR, with two specialized models: VLF-SAR-P for polarimetric SAR (PolSAR) data and VLF-SAR-T for traditional SAR data. Both models start with a frequency embedded module (FEM) to generate key structural features. For VLF-SAR-P, a polarimetric feature selector (PFS) is further introduced to identify the most relevant polarimetric features. Also, a novel adaptive multimodal triple attention mechanism (AMTAM) is designed to facilitate dynamic interactions between different kinds of features. For VLF-SAR-T, after FEM, a multimodal fusion attention mechanism (MFAM) is correspondingly proposed to fuse and adapt information extracted from frozen contrastive language-image pre-training (CLIP) encoders across different modalities. Extensive experiments on the OpenSARShip2.0, FUSAR-Ship, and SAR-AirCraft-1.0 datasets demonstrate the superiority of VLF-SAR over some state-of-the-art methods, offering a promising approach for few-shot SAR target recognition.

To mitigate the overfitting in the few-shot high-resolution range profile (HRRP) recognition, we introduce the Mahalanobis based statistical ProtoNet (MSP) with regularization, inspired by the prototypical network (ProtoNet). MSP leverages regularized feature covariance matrix to enhance the ProtoNet’s Euclidean distance metric based on the isotropic Gaussian distribution. Additionally, we propose a simplified MSP, the normalized statistical ProtoNet (NSP) for the faster inference of the statistical ProtoNet. Experiments demonstrate that statistical distance metrics enhance the few-shot recognition performance in scenarios with varying signal-to-noise ratios (SNR) and domain bias.

This paper introduces a novel plug-and-play Multi-scale Feature Spatial Attention Fusion (MFSAF) module, aiming at enhancing the capabilities of convolutional neural networks (CNNs) in Synthetic Aperture Radar (SAR) ship classification tasks. The MFSAF module integrates spatial attention mechanisms and feature alignment strategies, providing a seamless integration into general CNNs to better capture ship features of different scales. The experimental results on the OpenSARShip2.0 and FUSARShip datasets demonstrate a significant improvement of the "baseline+MFSAF" model compared to baseline model, highlighting the effectiveness of the MFSAF module in capturing SAR ship features and its adaptability across different networks.

To address the problem of imbalanced datasets in SAR ship classification, this paper presents a novel cosine affinity (CA) loss that enhances the Gaussian affinity (GA) loss. The CA loss focuses on the angular relationship between feature vectors, prioritizing their direction over their magnitude, which is advantageous for high-dimensional space analysis. In addition, class weights are incorporated to compute weighted distances. Importantly, the proposed CA loss does not increase the computational complexity of algorithm, nor does it lead to overfitting problems associated with data-level techniques. Through various experiments, its effectiveness has been demonstrated by achieving the highest F1 score and recall compared to other existing loss functions, highlighting its superior ability to classify minority classes in FUSARShip.

Flood caused by torrential rain is one of the most influential meteorological disasters in the world. Currently, most of the flood change detection networks are based on homogeneous images. However, due to the influence of bad weather and satellite revisit cycle, the acquisition of homogeneous images is greatly limited. In this paper, a novel heterogeneous image change detection network based on prior feature estimation is proposed. In order to better guide the network to find the solution space related to change, we propose feature enhancement module to strengthen the water body features and introduce auxiliary information. At the same time, the fusion module is designed to solve the problem that the feature space of heterogeneous images is difficult to align while mapping water features into changing features. Experimental results on the CAU-Flood dataset demonstrate the effectiveness of our network.

Segmentation of Synthetic Aperture Radar (SAR) imagery constitutes the cornerstone of SAR image analysis, with sea-land segmentation in SAR images playing a crucial role in determining the precision of subsequent sea surface target detection. This study introduces an integrated approach for sea-land segmentation and coastline detection in SAR imagery, aiming to overcome the limitations posed by the traditional separation of these two tasks. In essence, the proposed method merges a segmentation module and an edge detection module, employing a hollow convolution and a global context mechanism. Additionally, the approach utilizes a cross-entropy loss function incorporating multiple losses with adaptive weighting, thereby enhancing the richness of the extracted feature information. To validate the algorithm’s efficacy, a specialized dataset for sea-land segmentation and coastline detection is constructed, utilizing the GRD data format from the Sentinel-1 satellite. Experimental outcomes show that the presented algorithm achieves scores of 0.988 and 0.981 on the Intersection over Union (IOU) metrics for sea-land segmentation, and 0.569 and 0.401 on the Optimal Dataset Scale (ODS) F1 and ODS IOU metrics for coastline detection.

Optimal hyper-parameter selection for low-rank and sparse matrix decomposition (LRSMD) in synthetic aperture radar jamming suppression is usually challenging. This letter proposes an effective approach to LRSMD jamming suppression by exploiting the polarized similarity. A polarimetric ratio function is established to straightforwardly determine the rank hyper-parameter. The polarimetric ratio function is defined as the energy ratio of the decomposed low-rank jamming component to the sparse target signal, which is consistent across multiple polarized channels due to the equal jamming gain distinguished from the target polarized scattering. The algorithm optimally exploits the polarized similarity of jamming components to determine the rank hyper-parameter. It provides enhanced robustness and accuracy in SAR jamming removal, confirmed by synthetic experiments.

As one important application of synthetic aperture radar (SAR), ship classification attracts researchers’ attention in recent years. To improve the accuracy of ship classification in dual-polarized SAR images, we here put forward a novel polarimetric feature-guided deep network PFDN. Detailedly, a new polarimetric feature SPF (Smoothed-Polarimetric Information-Fusion) is first built through fusing both amplitudes of dual-polarized channels. Since only the amplitude information is used, SPF cannot be affected by the phase noise. Then, another two key components, i.e., the Multi-scale Feature Fusion Attention Module (MFFAM) and the Dynamic Gating Feature Fusion Mechanism (DGFFM), are further proposed to extract deeper classification features. Finally, via combing these three different modules together, PFDN is constructed. Experiments tested on the dataset OpenSARShip2.0 show that, PFDN can achieve higher accuracies (87.13% in three-class task and 65.97% in six-class task) than the other state-of-the-art (SOTA) methods.

For the task of marine target detection in synthetic aperture radar (SAR) images, sea-land segmentation and coastline detection are often essential. Despite exciting results, many of them are still separately performed. Only a few studies have been done on the simultaneous realization of sea-land segmentation and coastline detection. To this end, this letter proposes a new network SAENet, wherein one edge enhancement module (EEM), one maximum fusion difference convolution (MaxFDC), and one multiscale spatial attention module (Multiscale SAM) are developed. In order to verify its effectiveness, we further construct one sea-land segmentation and coastline detection dataset with the Sentinel-1 ground range detected (GRD) data. The corresponding experimental results show that SAENet can reach 0.989 and 0.980 on the evaluation indexes$F1$and IOU for sea-land segmentation, and 0.577 and 0.391 on the evaluation indexes ODS$F1$and ODS IOU for coastline detection, which better accomplishes the task of sea-land segmentation and coastline detection simultaneously in comparison with other state-of-the-art (SOTA) methods.

In recent years, many imaging algorithms for highly squint synthetic aperture radar (SAR) have been proposed. An algorithm based on keystone transform (KT) and azimuth Extended Nonlinear Chirp Scaling (ENLCS) is widely used in highly squint SAR imaging processing. It's effective in solving spatial-variant linear range cell migration (LRCM) and azimuth-variant Doppler parameters. However, due to the highly squint configuration, the beam center crossing time of many illuminated targets are not included in the track. We need extend the azimuth data length to ensure the targets a corresponding position in the data, which leads to an increase in computational complexity. And the imaging result also has geometric distortion. This paper proposes an improved ENLCS method with lower complexity combined with fast KT. First, we use the low complexity KT without interpo-lation in the RCM correction (RCMC) process. Then, we find a solution to reduce the extended data length by adding a time shift factor in the ENLCS process, saving data storage space and operation cost. Finally, geometric correction is performed by the grid mapping. The effectiveness of the proposed method is verified by numerical simulation and real data processing.

The combination and interconnection of the computational, cyber, and also communication devices using physical processes produced the Cyber- Physical Systems (CPSs), which turned out to be promising and extensively used in practice. These systems operating in real-world space environments have to overcome numerous challenges, chief among which are the reduction of the communication and computation burden, and the constraint upon the controller actuators. In this paper, aimed to overcome these difficulties, we have proposed the semi-global distributed parametric self-triggered constraint control protocols for the consensus task, under which the problem of achieving consensus when the agents collect information from the neighbors only at times that are designed iteratively and independently by each one of them on the basis of its current local measurements. Compared with the existing results, our approach presents several remarkable features. Both of the state feedback and output feedback policies for the constraint self-triggered frameworks are developed respectively. The consensus can be reached provided the communication graph is connected. An illustrative example is given to illustrate the effectiveness and the feasibility of the proposed control protocols.