Search Results (68)

Synthetic aperture radar (SAR) target recognition tasks face the dilemma of limited training samples. The fusion of target scattering features improves the ability of the network to perceive discriminative information and reduces the dependence on training samples. However, existing methods are inadequate in utilizing and fusing target scattering information, which limits the development of target recognition. To address the above issues, the multi-level structured scattering feature fusion network is proposed. Firstly, relying on the visual geometric structure of the target, the correlation between local scattering points is established to construct a more realistic target scattering structure. On this basis, the scattering association pyramid network is proposed to mine the multi-level structured scattering information of the target to achieve the full representation of the target scattering information. Subsequently, the discriminative information in the features is measured by the information entropy theory, and the results of the measurements are employed as weighting factors to achieve feature fusion. Additionally, the cosine space classifier is proposed to enhance the discriminative capability of features and the correlation with azimuth information. The effectiveness and superiority of the proposed method are verified on two publicly available SAR image target recognition datasets. Full article

Inverse Synthetic Aperture Radar (ISAR) imaging plays a crucial role in reconnaissance and target monitoring. However, the presence of uncertain factors often leads to indistinct component visualization and significant noise contamination in imaging results, where weak scattering components are frequently submerged by noise. To address these challenges, this paper proposes a Phase-Aware Multi-Scale Transformer network (PA-MSFormer) that simultaneously enhances weak component regions and suppresses noise. Unlike existing methods that struggled with this fundamental trade-off, our approach achieved 70.93 dB PSNR on electromagnetic simulation data, surpassing the previous best method by 0.6 dB, while maintaining only 1.59 million parameters. Specifically, we introduce a phase-aware attention mechanism that separates noise from weak scattering features through complex-domain modulation, a dual-branch fusion network that establishes frequency-domain separability criteria, and a progressive gate fuser that achieves pixel-level alignment between high- and low-frequency features. Extensive experiments on electromagnetic simulation and real-measured datasets demonstrate that PA-MSFormer effectively suppresses noise while significantly enhancing target visualization, establishing a solid foundation for subsequent interpretation tasks. Full article

This paper proposes a novel Global Navigation Satellite System (GNSS)-based remote sensing method for simulating Radar Doppler Map (RDM) features through joint electromagnetic scattering modeling and signal processing, enabling characteristic parameter extraction for both point and ship targets in multi-satellite scenarios. Simulations demonstrate that the B3I signal achieves a significantly enhanced range resolution (tens of meters) compared to the B1I signal (hundreds of meters), attributable to its wider bandwidth. Furthermore, we introduce an Unscented Particle Filter (UPF) algorithm for dynamic target tracking and state estimation. Experimental results show that four-satellite configurations outperform three-satellite setups, achieving <10 m position error for uniform motion and <18 m for maneuvering targets, with velocity errors within ±2 m/s using four satellites. The joint detection framework for multi-satellite, multi-target scenarios demonstrates an improved detection accuracy and robust localization performance. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

As a novel system, multi-dimensional space joint-observation SAR (MSJosSAR) can simultaneously acquire target information across multiple dimensions such as frequency, angle, and polarization. This capability facilitates a more comprehensive understanding of the target and enhances subsequent recognition applications. However, current research on the configuration optimization of multi-dimensional SAR systems is limited, particularly in balancing recognition requirements with observation costs. This limitation has become a major bottleneck restricting the development of MSJosSAR. Moreover, studies on the joint utilization of multi-dimensional information at the scattering center level remain insufficient, which constrains the effectiveness of target component recognition. To address these challenges, this paper proposes a configuration optimization method for MSJosSAR based on the separability of scattering mechanisms. The approach transforms the configuration optimization problem into a vector separability problem commonly addressed in machine learning. Experimental results demonstrate that the multi-dimensional configuration obtained by this method significantly improves the classification accuracy of scattering mechanisms. Additionally, we propose a feature extraction and classification method for scattering centers across frequency and angle-polarization dimensions, and validate its effectiveness through electromagnetic simulation experiments. This study offers valuable insights and references for MSJosSAR configuration optimization and joint feature information processing. Full article

Statistical optimization methods serve as fundamental tools for studying sea-ice-related phenomena in the polar regions. To comprehensively analyze the spatial distributions of ice ridge keels, including the draft and spacing distributions, a statistical optimization model was developed with the aim of determining the optimal keel cutoff draft, which differentiates ice ridge keels from sea ice bottom roughness. By treating the keel cutoff draft as the identified variable and minimizing the relative errors between the theoretical and measured keel spatial distributions, the developed model aimed to seek the optimal keel cutoff draft and provide a precise method for this differentiation and to explore the impact of the ridging intensity, defined as the ratio of the mean ridge sail height to spacing, on the spatial distributions of the ice ridge keels. The utilized data were obtained from observations of sea ice bottom undulations in the Northwestern Weddell Sea during the winter of 2006; these observations were conducted using helicopter-borne electromagnetic induction (EM-bird). Through rigorous analysis, the optimal keel cutoff draft was determined to be 3.8 m, and this value was subsequently employed to effectively differentiate ridge keels from other roughness features on the sea ice bottom. Then, building upon our previous research that clustered measured profiles into three distinct regimes (Region 1, Region 2, and Region 3, respectively), a detailed statistical analysis was carried out to evaluate the influence of the ridging intensity on the spatial distributions of the ice ridge keels for all three regimes. Notably, the results closely matched the predictions of the statistical optimization model: Wadhams’80 function (a negative exponential function) exhibited an excellent fit with the measured distributions of the keel draft, and a lognormal function proved to effectively describe the keel spacing distributions in all three regimes. Furthermore, it was discovered that the relationship between the mean ridge keel draft and frequency (number of keels per kilometer) could be accurately modeled by a logarithmic function with a correlation coefficient of 0.698, despite considerable data scatter. This study yields several significant results with far-reaching implications. The determination of the optimal keel cutoff draft and the successful modeling of the relationship between the keel draft and frequency represent key achievements. These findings provide a solid theoretical foundation for analyzing the correlations between the morphologies of the sea ice surface and bottom. Such theoretical insights are crucial for improving remote sensing algorithms for ice thickness inversion from satellite elevation data, enhancing the accuracy of sea ice thickness estimations. Full article

To study the influence of speed brake deflection on remote sensing grayscale images and the radar cross section (RCS) of aircraft, we present a comprehensive method based on remote sensing modeling and dynamic electromagnetic scattering. The results indicate that grayscale images from ground remote sensing can capture the hierarchical information of various reference objects and water bodies. When the target aircraft enters the observation area, complex ground reference objects may blur the grayscale features of the speed brake. The RCS of the speed brake shows strong dynamic characteristics under the example of the forward azimuth, where the maximum variation can reach 43.433 dBm². When the speed brakes on both sides dynamically deflect, the aircraft’s RCS in the lateral azimuth will fluctuate significantly in the first half of the observation time, and those in the forward and backward azimuths will show clear dynamic characteristics in the second half of the observation time. Low grayscale ground reference and water body boundaries/areas are beneficial for distinguishing the deflection of the deceleration plate. The comprehensive method proposed here is effective for studying remote sensing grayscale images and the dynamic RCS of aircraft under speed brake deflection. Full article

Synthetic aperture radar (SAR) recognition systems often need to collect new data and update the network accordingly. However, the network faces the challenge of catastrophic forgetting, where previously learned knowledge might be lost during the incremental learning of new data. To improve the applicability and sustainability of SAR target classification methods, we propose a multi-stage regularization-based class-incremental learning (CIL) method for SAR targets, called SCF-CIL, which addresses catastrophic forgetting. This method offers three main contributions. First, for the feature extractor, we fuse the convolutional neural network features with the scattering center features using a cross-attention feature fusion structure, ensuring both the plasticity and stability of the extracted features. Next, an overfitting training strategy is applied to provide clustering space for unseen classes with an acceptable trade-off in the accuracy of the current classes. Finally, we analyze the influence of training with imbalanced data on the last fully connected layer and introduce a multi-stage regularization method by dividing the calculation of the fully connected layer into three parts and applying regularization to each. Our experiments on SAR datasets demonstrate the effectiveness of these improvements. Full article

Due to the special properties of synthetic aperture radar (SAR) images, they are widely used in maritime applications, such as detecting ships at sea. To perform ship detection in SAR images, existing algorithms commonly utilize convolutional neural network (CNN). However, the challenges in acquiring SAR images and the imaging noise hinder CNN in performing SAR ship-detection tasks. In this paper, we revisit the relationship between SAR expert features and network abstract features, and propose an expert-feature-based convolutional neural network (EFCNet). Specifically, we exploit the inherent physical properties of SAR images by manually extracting a range of expert features, including electromagnetic scattering, geometric structure, and grayscale statistics. These expert features are then adaptively integrated with abstract CNN features through a newly designed multi-source features association module, which improves the common CNN’s capability to recognize ship targets. Experiment results on the SSDD demonstrate that EFCNet outperforms general CNN approaches. Furthermore, EFCNet achieves comparable detection performance to baseline methods while utilizing only 70% of the data capacity, highlighting its efficiency. This work aims to reignite interest in leveraging expert features in remote sensing tasks and offers promising avenues for improved SAR image interpretation. Full article

Optical sensing technologies play a crucial role in various fields such as biology, medicine, and food safety by measuring changes in material properties, such as the refractive index, light absorption, and scattering. Dielectric metasurfaces, with their subwavelength-scale geometric features and the ability to achieve high-quality-factor (Q-value) resonances through specific meta-atom designs, offer a new avenue for achieving faster and more sensitive material detection. The resonant wavelength, as one of the key indicators in meta-atom design, is usually determined using traditional solving methods such as electromagnetic simulations, which, although capable of providing high-precision prediction results, suffer from slow computational speed and long processing times. To address this issue, this paper proposes a forward prediction network for the amplitude spectrum of dielectric metasurfaces. Test results demonstrated that the mean square error of this network was consistently less than ${10}^{-3}$, and the neural network required less than 1 s, indicating its high-precision prediction capability. Furthermore, we employed transfer learning to apply this network to predict the near-infrared transmission spectra of high-Q-value resonant dielectric metasurfaces, achieving significant effectiveness. This method greatly enhanced the efficiency of metasurface design, and the designed network could serve as a universal backbone model for the forward prediction of spectral responses for other types of dielectric metasurfaces. Full article

Peanut quality, with the defect rate as a critical determinant, has a profound impact on its market value. In this study, we introduce an innovative non-destructive evaluation method for peanut defects. Differing from traditional and often expensive or complex detection methods, our approach utilizes a low-cost antipodal Vivaldi antenna, complemented by a custom-designed defect rate detection system. Prior to experimentation, we simulated the antenna and system architecture to ensure their operational efficiency, a step that not only conserves resources but also validates the reliability of subsequent results. We conducted experimental tests on fresh peanut pods, obtaining electromagnetic scattering parameters (S₁₁ and S₂₁ magnitudes/phases within 1–2 GHz) through non-destructive measurements. These parameters were used as input features, while the defect rate served as the output variable. By implementing the XGBoost algorithm, we established predictive models for defect rate quantification (regression) and defect grade classification. In comparison to some traditional statistical models, such as linear regression, which may struggle with non-linear data patterns, XGBoost effectively modeled the complex relationship between the scattering parameters and the defect rate. Experimentally, the regression model achieved an R² value of 0.8113 for defect rate prediction, and the classification model reached an accuracy of 0.7526 in grading defect severity. The entire device, costing less than USD 50, provides a significant cost advantage over many commercial systems. This low-cost setup enables real-time evaluation of peanut pod defects and efficiently categorizes the defect rate without the time-consuming sample preparation and tiling operations required by traditional image-based inspection methods. As a result, it offers an affordable and practical solution for quality control in peanut production, showing great potential for wide application in the peanut industry. Full article

Due to the huge differences in Synthetic Aperture Radar (SAR) image features of the same target under different observing aspects, the demand for constructing multi-aspect SAR datasets of various typical targets is becoming increasingly urgent with the expansion of SAR technology application fields. Meanwhile, multi-aspect interpolation techniques for constructing multi-aspect SAR datasets, based on electromagnetic scattering features and on Generative Adversarial Networks (GANs), have some shortcomings that are difficult to address. The former method provide descriptions of the target scattering so overly idealized that they are not real, while the latter method suffers from incomplete amplitude information and a loss of phase information in multi-aspect interpolation results due to the SAR images input into GANs being phaseless and amplitude-quantized. In response to the above issues, this paper proposes the Multi-aspect Scattering Information Complex GAN (MS-CGAN) guided by the scattering information in observing aspects of SAR images to simulate the multi-aspect interpolation of SAR images from specific aspects. MS-CGAN provides a new approach for dataset construction and augmentation. Moreover, as a complex network, MS-CGAN does not require phase removal or amplitude quantization of the input SAR images; thus, the significant issue of the severe loss of scattering information in multi-aspect interpolation methods based on GANs is greatly addressed. In the experiments, assuming the absence of real SAR images from certain aspects, both the correlation coefficient and the phase correlation between interpolated SAR images from MS-CGAN and real SAR images achieve good results. In the case of a sampling aspect interval of 10°, the mean correlation of the amplitude and phase of the interpolated SAR images and the corresponding real SAR images both reach over 80%. In the case of a sampling aspect interval of 20°, the mean correlation of the amplitude and phase of the interpolated SAR images and the corresponding real SAR images remain above 75%. In the case of a sampling aspect interval of 30°, the mean correlation of the amplitude and phase of the interpolated SAR images and the corresponding real SAR images can reach around 70%. Energy integration curves are completed at specific aspects, demonstrating the effectiveness of the MS-CGAN multi-aspect interpolation method. Full article

Artificial metasurfaces can rapidly modulate their electromagnetic scattering properties and the characteristics of echo signals, which can lead to different imaging features in synthetic aperture radar (SAR) imaging results. Based on this, for the first time, this paper proposes an approach for SAR feature reconfiguring based on periodic phase modulation with inter-pulse time bias. Considering the position and energy requirements of the expected reconfigured imaging target, this approach optimizes the metasurface modulation parameters via a dual algorithm collaborative optimization system, i.e., a modulation parameter generation algorithm (MPGA) and a parameter mapping matching algorithm (PMMA). Time-modulated metasurface targets can reconfigure imaging features of different targets at SAR reconnaissance moments under the guidance of optimized modulation parameters obtained using this approach. Compared with the previous single-point target research on the combination of SAR and metasurfaces, this method is expanded to include the combined analysis of multi-point targets and the reconfigurability of SAR features. Experiments have proved that the programmable reconfigurability of different target features (such as passenger plane targets and truck targets) can be achieved in SAR imaging results through dynamic adjustment of the modulation parameter set. The reconfigured imaging features maintain geometric consistency within the resolution error range, and the size and position of the target can be set as required. Full article

Polarimetric radar systems play a crucial role in enhancing microwave remote sensing and target identification by providing a refined understanding of electromagnetic scattering mechanisms. This study introduces the concept of polarization scattering regions as a novel tool for describing the polarization characteristics across three spectral regions: the polarization Rayleigh region, the polarization resonance region, and the polarization optical region. By using ellipsoidal models, we simulate and analyze scattering across varying electrical sizes, demonstrating how these sizes influence polarization characteristics. The research leverages Cameron decomposition to reveal the distinctive scattering behaviors within each region, illustrating that at higher-frequency bands, scattering approximates spherical symmetry, with minimal impact from the target shape. This classification provides a comprehensive view of polarization-based radar cross-section regions, expanding upon traditional single-polarization radar cross-section regions. The results show that polarization scattering regions are practical tools for interpreting polarimetric radar data across diverse frequency bands. The applications of this research in radar target recognition, weather radar calibration, and radar polarimetry are discussed, highlighting the importance of frequency selection for accurately capturing polarization scattering features. These findings have significant implications for advancing weather radar technology and target recognition techniques, particularly as radar systems move towards higher frequency bands. Full article

An intelligent approach is proposed and investigated in this paper for the detection of ultra-low-altitude sea-skimming moving targets for airborne pulse Doppler radar. Without suppressing interferences, the proposed method uses both target and multipath information for detection based on their distinguishable image features and deep learning (DL) techniques. First, the image features of the target, multipath, and sea clutter in the real-measured range-Doppler (RD) map are analyzed, based on which the target and multipath are defined together as the generalized target. Then, based on the composite electromagnetic scattering mechanism of the target and the ocean surface, a scattering-based echo generation model is established and validated to generate sufficient data for DL network training. Finally, the RD features of the generalized target are learned by training the DL-based target detector, such as you-only-look-once version 7 (YOLOv7) and Faster R-CNN. The detection results show the high performance of the proposed method on both simulated and real-measured data without suppressing interferences (e.g., clutter, jamming, and noise). In particular, even if the target is submerged in clutter, the target can still be detected by the proposed method based on the multipath feature. Full article