Search Results (203)

The coherency matrix serves as a valuable tool for explaining the intricate details of various terrain targets. However, a significant challenge arises when analyzing ground targets with similar scattering characteristics in polarimetric synthetic aperture radar (PolSAR) target decomposition. Specifically, the overestimation of volume scattering (OVS) introduces ambiguity in characterizing the scattering mechanism and uncertainty in deciphering the scattering mechanism of large oriented built-up areas. To address these challenges, based on the generalized five-component decomposition (G5U), we propose a hierarchical extension of the G5U method, termed ExG5U, which incorporates orientation and phase angles into the matrix rotation process. The resulting transformed coherency matrices are then subjected to a five-component decomposition framework, enhanced with four refined volume scattering models. Additionally, we have reformulated the branch conditions to facilitate more precise interpretations of scattering mechanisms. To validate the efficacy of the proposed method, we have conducted comprehensive evaluations using diverse PolSAR datasets from Gaofen-3, Radarsat-2, and ESAR, covering varying data acquisition timelines, sites, and frequency bands. The findings indicate that the ExG5U method proficiently captures the scattering characteristics of ambiguous regions and shows promising potential in mitigating OVS, ultimately facilitating a more accurate portrayal of scattering mechanisms of various terrain types. Full article

Dual polarimetric SAR is capable of reflecting the biophysical and geometrical information of terrain with open access data availability. When it is combined with time-series observations, it can effectively capture the dynamic evolution of scattering characteristics of crops in different growth cycles. However, the actual planting of crops often shows spatial dispersion, and the same crop may be dispersed in different plots, which fails to adequately consider the correlation information between dispersed plots of the same crop in spatial distribution. This study proposed a crop classification method based on multi-temporal dual polarimetric data, which considered the utilization of information between near and far spatial plots, by employing superpixel segmentation and a HyperGraph neural network, respectively. Firstly, the method utilized the dual polarimetric covariance matrix of multi-temporal data to perform superpixel segmentation on neighboring pixels, so that the segmented superpixel blocks were highly compatible with the actual plot shapes from a long-term period perspective. Then, a HyperGraph adjacency matrix was constructed, and a HyperGraph neural network (HGNN) was utilized to better learn the features of plots of the same crop that are distributed far from each other. The method fully utilizes the three dimensions of time, polarization and space information, which complement each other so as to effectively realize high-precision crop classification. The Sentinel-1 experimental results show that, under the optimal parameter settings, the classified accuracy of combined temporal superpixel scattering features using the HGNN was obviously improved, considering the near and far distance spatial correlations of crop types. Full article

The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, this study presents the first comprehensive comparison of their ship detection performance against conventional modes using Target-to-Clutter Ratio (TCR) and deep learning-based accuracy (AP50). Experiments on the FPSD dataset reveal that an optimized GCP mode (e.g., ellipse/orientation: [−10, −5]) consistently outperforms traditional CP and DP modes, yielding TCR gains of 0.2–2.7 dB. This translates to AP50 improvements of 0.5–4.7% (Faster R-CNN) and 0.1–5.5% (RetinaNet) over five common baseline modes. Crucially, this enhancement arises from optimizing the interaction between the polarization mode and target/clutter scattering characteristics rather than algorithmic improvements, supporting the proposed “optimization from the information source” strategy. These findings offer significant implications for future PolSAR system design and operational mode selection. Full article

This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water land can vary depending on the region and flood conditions. Therefore, the flood detection performance of the dual-pol parameters was evaluated across three datasets with different geographic, climatic, and land cover conditions. The results demonstrated that accurate and stable performance in the detection of inundated areas under different surface conditions can be achieved by combining water body information from dual-pol channels in a disjunctive way. It also suggests that synergy in flood detection can be expected when using polarization observation data by considering each polarimetric channel as an independent information source and combining them rather than deriving the most relevant polarization parameter. Furthermore, combining common information from two dual-pol channels in a conjunctive way could provide the most reliable SAR flood detection results with minimum false alarms from the user’s perspective. Based on these experimental results, a two-class flood classification scheme was proposed for improving the applicability of SAR remote sensing in identifying flooded areas. Full article

Limited labels and detailed changed land-cover interpretation requirements pose challenges for time-series PolSAR change monitoring research. Accurate labels and supervised models are difficult to reuse between massive unlabeled time-series PolSAR data due to the complex distribution shifts caused by different imaging parameters, scene changes, and random noises. Moreover, many related methods can only detect binary changes in PolSAR images and struggle to track the detailed land cover changes. In this study, an unsupervised cross-domain method based on limited-label transfer learning and a vision transformer (LLTL-ViT) is proposed for PolSAR land-cover change monitoring, which effectively alleviates the problem of difficult label reuse caused by domain shift in time-series SAR data, significantly improves the efficiency of label reuse, and provides a new paradigm for the transfer learning of time-series polarimetric SAR. Firstly, based on the polarimetric scattering characteristics and manifold-embedded distribution alignment transfer learning, LLTL-ViT transfers the limited labeled samples of source-domain PolSAR data to unlabeled target-domain PolSAR time-series for initial classification. Secondly, the accurate samples of target domains are further selected based on the initial transfer classification results, and the deep learning network ViT is applied to classify the time-series PolSAR images accurately. Thirdly, with the reliable secondary classification results of time-series PolSAR images, the detailed changes in land cover can be accurately tracked. Four groups of cross-domain change monitoring experiments were conducted on the Radarsat-2, Sentinel-1, and UAVSAR datasets, with about 10% labeled samples from the source-domain PolSAR. LLTL-ViT can reuse the samples between unlabeled target-domain time-series and leads to a change detection accuracy and specific land-cover change tracking accuracy of 85.22–96.36% and 72.18–88.06%, respectively. Full article

Onboard millimeter-wave radar is one way to improve helicopter flight safety at low altitudes in difficult meteorological conditions. Low-altitude flight is associated with the risk of collision with low-visibility obstacles such as power lines. Since power lines have the property of polarization anisotropy, it is necessary to use radar polarimetry methods to increase the radar visibility of such obstacles for detection. This paper investigates the possibility of identifying low-visibility polarization-anisotropic objects, such as power lines, in polarimetric images obtained by onboard helicopter radars to warn the pilot about the danger of a collision with a low-visibility object. Consequently, the use of a polarimetric radar with a polarization modulation of the emitted signal, the two-channel polarization synchronous reception of reflected signals and Eigenvalue signal processing in real time was proposed, aiming to eliminate the dependence of reflected signals on the spatial orientation of power transmission line wires. Additionally, an optimal algorithm for adaptive polarization selection, obtained by the maximum likelihood method, was proposed to detect such objects against the background of the underlying surface. The effectiveness of the proposed algorithm was tested using computer modeling methods. Moreover, a W-band polarimetric radar system was designed for experimental studies of this concept. The developed radar system provides digital real-time signal processing and the reconstruction of composite polarimetric images. It displays information about the polarization characteristics of the objects in the scanned area and the results of their polarization selection. Therefore, the system informs the pilot about dangerous objects along the helicopter’s route. Radar experimental tests in actual environmental conditions have confirmed the proposed concept’s correctness and the proposed method and algorithm’s effectiveness. Full article

Inverse Synthetic Aperture Radar (ISAR) serves as a valuable instrument for surveillance of space targets. There has been a great deal of research on space target identification using ISAR. However, the polarization characteristics of space target components are rarely studied. Polarimetric Inverse Synthetic Aperture Radar (PolISAR) comprises two information dimensions, namely, polarization and image, enabling a more comprehensive understanding of target structures. This paper proposes a space target structure polarization interpretation method based on component decomposition and PolISAR feature extraction. The proposed method divides the target into components at the stage of modeling. Subsequently, electromagnetic calculations are performed for each component. The names of these components are used to label the dataset. Multiple polarization decomposition techniques are applied and many polarization features are obtained. The mapping correlations between the interpreted results and authentic target structures are improved through preferential selection of polarization features. Ultimately, the method is validated through analysis of simulation and anechoic chamber measurement data. The results show that the proposed method exhibits a more intuitive correlation with the authentic target structures compared to traditional polarized interpretation methods based on Cameron decomposition. Full article

To enhance the capability of a ground penetrating radar (GPR) in subsurface target identification and improve its polarization sensitivity in detecting underground linear objects, a circularly polarized broadband composite spiral antenna was designed. This antenna integrates equiangular spiral and Archimedean spiral structures, achieving a wideband coverage of 1–5 GHz with stable circular polarization characteristics. The antenna employs an exponentially tapered microstrip balun for impedance matching and a metallic-backed cavity filled with absorbing materials to enhance its directivity. Experimental results demonstrate excellent radiation performance and stable circular polarization characteristics, with the axial ratio consistently below 3 dB throughout the operating frequency band, highlighting its suitability for polarimetric GPR systems. Furthermore, a 3D GPR measurement using the designed antenna validates its improved capacity for detecting subsurface linear objects, compared to the conventional linearly polarized bowtie antenna. Full article

China’s first L-band fully polarimetric Synthetic Aperture Radar (SAR) constellation, LuTan-1 (LT-1), was designed for terrain mapping and geohazard monitoring. This study evaluates LT-1’s capability in identifying landslides in the southern hilly regions of China, focusing on Longshan County, Hunan Province. Using both ascending and descending orbit data from LT-1, we conducted landslide identification experiments. First, deformation was obtained using Differential Interferometric SAR (D-InSAR) technology, and the deformation rates were derived through the Stacking technique. A landslide identification method that integrates C-index, slope, and ascending/descending orbit deformation information was then applied. The identified landslides were validated against existing geohazard points and medium-to-high-risk slope and gully unit data. The experimental results indicate that LT-1-ascending orbit data identified 88 landslide areas, with 39.8% corresponding to geohazard points and 65.9% within known slope units. Descending orbit data identified 90 landslide areas, with 37.8% matching geohazard points and 61.1% within known slope units. The identification results demonstrated good consistency with existing data. Comparative analysis with Sentinel-1 data revealed that LT-1’s combined ascending and descending orbit data outperformed Sentinel-1’s single ascending orbit data. LT-1’s L-band characteristics, comprehensive ascending and descending orbit coverage, and high-precision deformation detection make it highly promising for landslide identification in the southern hilly regions. This study underscores LT-1’s robust technical support for early landslide identification, highlighting its potential to enhance geohazard monitoring and mitigate risks in challenging terrains. Full article

Due to the limited accuracy of experimental data, Mueller polarimetry can produce real 4×4 matrices that fail to meet required covariance or passivity conditions. A general and simple procedure to convert any real 4×4 matrix into a valid Mueller matrix by adding a portion of polarimetric white noise is presented. This approach provides deeper insight into the structure of Mueller matrices and has a subtle relation to the effective component of the Mueller matrix, which is defined through the subtraction of the fully random component of the characteristic decomposition. Up to a scale coefficient determined by the third index of polarimetric purity of the original Mueller matrix, the effective component retains complete information on the polarimetric anisotropies. Full article

This paper presents a new approach to polarimetric calibration, extending classical PARC-based methods by exploring new matrix combinations that broaden the applicability of the existing techniques. By investigating alternative matrix configurations, the proposed method not only enhances the flexibility of conventional calibration approaches but also identifies matrix combinations that offer superior performance advantages. The influence of the SNR and scattering matrix error of PARC on the proposed method is evaluated by numerical simulations. The results demonstrate that the proposed method is highly accurate for PARCs with an SNR greater than 34 dB and with single-channel scattering matrix deviations less than −40 dB and four-channel scattering matrix deviations less than 0.5 dB. The effectiveness and precision of the method were validated through calibration experiments conducted on the L-band polarimetric synthetic-aperture radar aboard the LT-1 satellite. The experimental results demonstrate that the amplitude and phase estimation errors of channel unbalance are less than 0.6 dB and 4.5°, respectively, and that the crosstalk estimation error is less than −33 dB. Furthermore, the effectiveness of the method is validated through trihedral corner reflector correlation experiments and the synthesis of pseudo-color images via Pauli decomposition. The theoretical polarization characteristics of the reference target exhibited a high degree of agreement with the calibrated polarization characteristics. Full article

Polarimetric decomposition methods are widely used in polarimetric Synthetic Aperture Radar (SAR) data processing for extracting scattering characteristics of targets. However, polarization SAR methods for ship detection still face challenges. The traditional constant false alarm rate (CFAR) detectors face sea clutter modeling and parameter estimation problems in ship detection, which is difficult to adapt to the complex background. In addition, neural network-based detection methods mostly rely on single polarimetric-channel scattering information and fail to fully explore the polarization properties and physical scattering laws of ships. To address these issues, this study constructed two novel characteristics: a helix-scattering enhanced (HSE) local component and a multi-scattering intensity difference (MSID) edge component, which are specifically designed to describe ship scattering characteristics. Based on the characteristic differences of different scattering components in ships, this paper designs a context aggregation network enhanced by local and edge component characteristics to fully utilize the scattering information of polarized SAR data. With the powerful feature extraction capability of a convolutional neural network, the proposed method can significantly enhance the distinction between ships and the sea. Further analysis shows that HSE is able to capture structural information about the target, MSID can increase ship–sea separation capability, and an HV channel retains more detailed information. Compared with other decomposition models, the proposed characteristic combination model performs well in complex backgrounds and can distinguish ship from sea more effectively. The experimental results show that the proposed method achieves a detection precision of 93.6% and a recall rate of 91.5% on a fully polarized SAR dataset, which are better than other popular network algorithms, verifying the reasonableness and superiority of the method. 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

Magneto-optical magnetic field/current sensors are based on the Faraday effect, which involves changing the polarized state of light. Polarimetric methods are therefore used for measuring polarization characteristics. Channeled polarimetry allows polarization information to be obtained from the analysis of the spectral domain. Although this allows the characterization of Faraday materials, the method has not yet been used for detection in magneto-optical sensors. This paper reports experimental results for magnetic field/current detection using the channeled polarimetry method. It is shown that in contrast to other methods, this method allows the detection of the phase shift caused by Faraday rotation alone, making the detection independent of temperature. Although an increase in measurement accuracy is required for practical applications by refining the data processing, the experimental results obtained show that this method offers a new approach to improving the performance of magneto-optical current sensors. Full article

Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms. Full article