Search Results (574)

Spectro-polarimetric imaging systems can simultaneously acquire spatial, spectral, and polarimetric information during remote sensing, yet the multimodal fusion data are often constrained in practical applications by insufficient exploitation of complementary information across different modalities. To address this issue, we propose a multimodal image fusion method based on a Dual-Branch Cross-Attention Synergistic Transformer (DBCS-T). In our method, three complementary feature components are independently extracted, i.e., a Characteristic Polarization Image (CPI), a Characteristic Spectral Image (CSI) and a Characteristic Intensity Image (CII). For CPI, it is derived from Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP) inputs via DBCS-T, which integrates a Cross-Channel Transposed Attention (CCTA) module for cross-modal interaction and a Multi-Scale Polarization Feature Adaptive Modulation (MPAM) module for local feature enhancement. CSI is obtained by leveraging maximum-divergence spectral band differences guided by prior spectral radiance curves. CII is computed from the Stokes parameter $S_0$. These three components are then fused via Principal Component Analysis (PCA) into a Multimodal Fusion Image (MFI). To demonstrate the effectiveness of our method, an experiment was conducted on a scene that contains real vegetation, artificial foliage, and same-color metallic objects. The experimental results show that the proposed method achieves effective semantic segmentation of all three target categories. Furthermore, quantitative evaluation demonstrates that the MFI attains the lowest Kullback–Leibler (KL) and Jensen–Shannon (JS) Divergence values among all evaluated modalities, with image entropy exceeding that of individual source inputs. These results validate the complementarity of the extracted multimodal features, significantly enhance the interpretation performance for complex scenes, and demonstrate the broad application potential of the proposed fusion framework in remote sensing and multidimensional imaging. Full article

Haze conditions degrade image quality via atmospheric scattering and absorption, posing challenges for optical imaging applications. In recent years, deep learning has emerged as an effective method for dehazing images. However, data-driven deep learning dehazing methods typically require large amounts of labeled training data and offer limited interpretability. In this paper, we propose a physics-guided dehazing method based on polarization imaging. By integrating polarization imaging with the physical model, the proposed method enables neural network training using only a set of hazy images captured at different polarization angles, thereby reducing the reliance on labeled training data. Experimental results show that the proposed method significantly outperforms commonly used deep learning dehazing methods in terms of contrast and mean gradient, while exhibiting strong generalization and physical interpretability. By integrating physical model and polarization imaging into deep learning, this method overcomes the limitations of traditional deep learning dehazing methods and paves the way for optical imaging in haze conditions. Full article

The degree of polarization is usually obtained from the coherency matrix or, equivalently, from the mean Stokes vector of a partially polarized optical field. Here, we adopt a complementary geometric viewpoint by representing normalized local Stokes vectors as random directions on the Poincaré sphere. When these directions are described by an effective unimodal von Mises–Fisher distribution, the concentration parameter gives a direct one-to-one description of the degree of polarization through the mean resultant length. This formulation does not define a new independent polarization observable. Instead, it gives the degree of polarization a rotation-invariant information-theoretic meaning, expressed in terms of directional concentration and angular disorder. Within this framework, we derive closed-form expressions for the differential entropy of the von Mises–Fisher distribution and for the Kullback–Leibler divergence between two directional polarization states. The symmetrized divergence further incorporates both differences in concentration and relative orientation on the Poincaré sphere. We also discuss the assumptions, range of validity, and limitations of the single-vMF model, particularly in relation to Gaussian field statistics and more general directional models needed for anisotropic or multimodal polarization fluctuations. Overall, this formalism establishes a model-based theoretical framework for entropy and divergence descriptors of unimodal directional polarization and suggests natural extensions toward mixtures of vMF, Bingham, or Kent distributions. Full article

Marine oil spills severely threaten marine ecosystems, the coastal economy, and marine engineering structures. Because it enables all-weather and all-time acquisition of rich target information, fully polarimetric synthetic aperture radar (FP SAR) is widely used for monitoring marine oil spills. However, the differences in the scattering characteristics among oil types can cause variability in the information contained in the features extracted using FP SAR. Herein, RADARSAT-2 images obtained from a rare oil-on-water experiment conducted in the Norwegian North Sea were used to compare the distribution differences in polarimetric features based on the oil slick type and incident angle. Results showed that the incident angle exerted some influence on polarimetric features and the detection performance for oil spills with a low oil–water contrast, particularly at large incident angles. The polarimetric features related to scattering mechanisms exhibited good robustness and effectiveness across various incident angles. The polarimetric feature that combines the scattering entropy H and modified anisotropy A₁₂ exhibited strong overall performance and high suitability for extracting information on oil spills at different incident angles. This study demonstrates that incorporating appropriate polarimetric features according to the incident angle enables the identification of different oil slick types and facilitates oil spill detection and monitoring. Full article

Freeman−Durden Decomposition (FDD) and Non-Negative Eigenvalue Decomposition (NNED) are among the most widely used incoherent polarimetric decomposition algorithms for analyzing fully Polarimetric Synthetic Aperture Radar (PolSAR) data, particularly FDD. However, with advancements in model-based incoherent polarimetric decomposition techniques, their original algorithms have certain aspects that can be modified to enhance their decomposition performance. These aspects include: FDD occasionally yielding negative power values and typically overestimating the power of the volume scattering component; the scattering mechanism of the remainder matrix in NNED being further interpretable; and the potential for improving its decomposition performance through specific modifications. Therefore, two improved incoherent polarimetric decomposition algorithms, Modified Freeman−Durden Decomposition (MFDD) and Deorientation Non-Negative Eigenvalue Decomposition (DNNED), are proposed in this study. For MFDD, deorientation is applied at the outset, and two additional steps are introduced to eliminate negative power values in the decomposition results. The DNNED algorithm also employs deorientation and enhances the interpretation of the scattering mechanism of the remainder matrix. DNNED identifies that the remainder matrix corresponds to a dihedral with a 45-degree orientation angle, thus classifying its power as double-bounce scattering. Decomposition performance tests have been conducted using two actual PolSAR images derived from E-SAR of Germany and GF-3 of China. Experimental results demonstrate that the performance of MFDD is superior to that of FDD, and the performance of DNNED is the best among the four aforementioned decomposition algorithms. Full article

Recent studies have proposed improving Laser Speckle Contrast Imaging (LSCI) by using polarimetric filtering to isolate multiply scattered photons from moving red blood cells (RBCs), an approach referred to as Laser Speckle Orthogonal Contrast Imaging (LSOCI). This reliance on multiple scattering enables the development of a calibration method based on a moving reference sample, chosen to generate dynamic circular Gaussian speckle fields that replicate the statistical properties of RBC scattering in both intensity and the distribution of polarization states. Assuming that multiply scattered photons from both RBCs and the reference sample exhibit a homogeneous distribution of polarization states over the Poincaré sphere, the proposed calibration links in vivo speckle contrast reduction bijectively to an equivalent speed of the reference sample. We demonstrate that this equivalent-velocity metric yields consistent in vivo measurements across distinct instruments despite the use of different laser spectral widths, thereby providing a standardized and transferable means to quantify microcirculatory activity. Full article

Polarimetric color cameras are a forefront technology that simultaneously captures polarimetric and color information by analyzing polarization states across different color channels, commonly red, green, and blue. In general, each of these color channels can carry different polarization information. Therefore, measuring the polarization Stokes vector at several discrete wavelengths simultaneously and with the highest possible resolution is of interest in multiple research areas. However, when a commercial color polarization sensor is used under simultaneous narrowband RGB illumination mode, its channels cannot be assumed to represent independent wavelength channels. Spectral overlap of the color filters introduces color crosstalk between wavelength-dependent analyzer intensities, which may bias the reconstructed Stokes parameters if it is not corrected before polarimetric inversion. Several methods have been proposed in the literature to address the color crosstalk problem but they typically assume that the polarization state is identical for all wavelengths. This assumption does not generally hold for real samples, which exhibit wavelength-dependent depolarization, retardance, and dichroism. To the best of our knowledge, this is the first work presenting a method that addresses the color crosstalk problem without assuming that the polarization state is identical across all wavelengths. In addition, Fourier domain demosaicking techniques are applied to interpolate the data and reconstruct the images. The present study demonstrates how the proposed method leads to an accurate recovery of chromatic and polarimetric information on both synthetic and real-world datasets. To test our approach, narrowband light beams at three wavelengths (470, 554, 630 nm), with different spatial polarization and degree of linear polarization distributions, have been simulated and validated with simulated and experimental data. The results demonstrate the feasibility of the method for accurate three polarization channels measurements. Full article

Polarimetric synthetic aperture radar (Pol-SAR) is one of the most important approaches for sea-surface target detection, but under rainfall conditions it tends to be distorted by the electromagnetic (EM) propagation effects and clutter interference of rainfall. To address this problem, this paper proposes a joint compensation method to mitigate the impacts of rainfall on the detection of sea-surface targets. In the method, a composite imaging model that thoroughly takes into account the propagation and scattering effects of rainfall, sea surface, and ship targets is first established. Then, a range-wise algorithm is proposed to effectively estimate the propagation effects, which are used to compensate for the radar echoes distorted by rainfall. Consequently, a hierarchical search strategy is employed to optimize the receiving polarization state to better discriminate the targets from rainfall and sea clutter. Simulation results show that, across the tested sea-surface wind and rainfall conditions, the proposed method improves the signal-to-clutter-plus-noise ratio (SCNR) by 4 to 13 dB compared with the polarimetric whitening filter, demonstrating its effectiveness under coupled rain–sea conditions. Full article

The Lutan-1 (LT-1) mission is the first civilian L-band differential interferometric synthetic aperture radar (SAR) system in China, with interferometry as its primary application. The system comprises two multi-polarimetric satellites, LT-1A and LT-1B. For the purpose of quantitative application from SAR images of Lutan-1 satellites, the relationship between the SAR image intensity and the backscattering coefficient of ground objects should be established by radiometric calibration. Field radiometric calibration provides absolute calibration constants, but it suffers from beam coverage. Internal on-board calibration, by contrast, tracks relative changes in radiometric performance but cannot yield absolute calibration constants. Therefore, we develop a method that combines on-board internal calibration with field radiometric calibration to monitor the radiometric performance of LT-1 satellites and to analyze the variation patterns revealed by both internal and field calibrations. We monitor the amplitude and phase trend of internal calibration, calculate absolute calibration constants from field calibration, and refine and evaluate the absolute calibration constants. We analyzed the internal calibration data and SAR calibration data of the LT-1 satellite from 2023 to 2025. The results show that the TRMs of the LT-1 satellite exhibit a slight decline over time, and the magnitude of the decrease in LT-1B is greater than that of LT-1A. The slight decrease in internal calibration has not yet led to visible changes in the absolute calibration constant for LT-1A, while the absolute calibration constants decrease slightly for LT-1B. After removing the calibration constant outliers and correcting the gain difference among the beams for the LT-1A satellite, absolute radiometric accuracy is improved from 0.40 dB (1σ) to 0.25 dB (1σ). The absolute radiometric accuracy of the LT-1B satellite is 0.38 dB (1σ). It gives a reference for radiometric performance monitoring of the SAR satellite over a long period. Full article

Passive millimeter-wave (PMMW) imaging technology has become a highly promising technology that can protect privacy in human body security inspections. However, most existing methods rely on single-pixel and single-polarization processing mechanisms, which often lead to discrete false-alarm pixels or missed detections in practical applications. Although multi-polarization information can provide richer distinguishing features, the current methods typically depend on limited Stokes parameters or artificially designed polarization features, lacking a systematic framework to fully exploit the intrinsic potential of multi-polarization information. In this paper, we propose a novel multi-scale edge-preserving decomposition model, termed Gaussian and weighted average curvature filtering (GWACF), to hierarchically decompose a multi-polarization PMMW image into three structural layers: base structural (BS) layer, coarse structural (CS) layer, and fine structural (FS) layer. Furthermore, we also propose a fusion strategy in which a gradient-domain pulse-coupled neural network (PCNN) is employed to fuse the texture-rich CS and FS layers, while the energy attribute fusion method is applied to the BS layer where primary structure and background information play a dominant role. This method effectively leverages complementary polarimetric information without introducing artifacts or compromising edge sharpness. Experimental results demonstrate that the proposed method effectively enhances the brightness temperature (BT) contrast of concealed objects. Compared with existing mainstream methods, it exhibits notable advantages in both detection accuracy and robustness. Full article

Coherent space-based InSAR observations over snow- and ice-covered areas have been a valuable resource for cryospheric research. Coherence is considered a critical parameter for evaluating the quality of InSAR observations. This study evaluates the coherence characteristics of snow- and ice-covered areas using mainly fully polarimetric (quad-pol) X-band TerraSAR-X (TSX) and L-band ALOS PALSAR observations. The TSX data were acquired systematically during the Dual Receive Antenna campaign in 2010, while the quad-pol ALOS PALSAR L-band observations were acquired in 2007. A total of 57 TSX quad-pol images acquired over 17 areas at latitudes higher than 60° N were analyzed. The results across all study areas show relatively high coherence levels, ranging from 0.38 to 0.57, with the highest values observed in VV, followed by HH, and the lowest in HV. Interestingly, the highest coherence was found in the VV polarization, whereas HH coherence is typically higher than VV coherence in most InSAR applications. A comparative coherence analysis using quad-pol ALOS PALSAR L-band observations over selected snow- and ice-covered areas showed very similar coherence levels for both HH and VV polarizations. These results suggest that VV polarization is the most suitable for X-band InSAR applications over snow- and ice-covered areas. Full article

To meet the demand for high-precision target classification in complex scenes, a hyperspectral–polarimetric–LiDAR multimodal image fusion method tailored for few-shot scenarios is proposed. Feature-mapping functions for polarimetric and LiDAR images are constructed, and a multi-scale hierarchical optimization strategy is employed to jointly enhance low- and high-frequency components across modalities. This approach effectively addresses key challenges under limited training data, such as substantial cross-modal dimensional disparities and the difficulty of robust feature extraction and fusion. The proposed algorithm conducts bimodal image fusion on the NWPUSP spectral-polarization dataset and KAIST spectral-depth dataset. Compared with other fusion methods, it achieves average increases of 7.3% and 4.87% in information entropy, 53.18% and 30.35% in standard deviation, 48% and 108.28% in average gradient, as well as 96.25% and 101.13% in spatial frequency, respectively. Moreover, relying on the self-developed integrated hyperspectral-polarization imaging system and commercial LiDAR, we synchronously and efficiently acquire multimodal images including hyperspectral, polarization and LiDAR images of complex ground object scenes. Comparative experiments are implemented against six other mainstream fusion algorithms. The objective evaluation results show that the average improvements reach 7.19% in information entropy, 46.85% in standard deviation, 76.62% in average gradient and 79.74% in spatial frequency, which notably enhances the feature retention capability of fused images. Under few-shot conditions, the target recognition classification accuracy and Kappa coefficient of the fused image are improved by 9.8% and 11.05%, respectively, compared with those of the unimodal hyperspectral image. This effectively highlights targets under shadow occlusion and compensates for LiDAR’s response deficiencies to surface textures, achieving complementary advantages of multimodal images for ground object targets in complex scenes. This research provides a new solution for future optical multimodal remote sensing and image fusion. Full article

High-resolution three-dimensional (3D) radar imaging of non-coherent point target clusters faces significant challenges, particularly severe angular glint induced by the simultaneous presence of dual targets or co-channel interference (CCI) within the antenna mainlobe. Conventional monopulse systems often struggle to resolve such overlapping sources, particularly under conditions of high power disparity between signal components. To overcome the Rayleigh resolution limit, this paper proposes a polarimetric 3D imaging framework for three-channel monopulse radar by leveraging joint polarimetric-angular diversity. By exploiting the intrinsic instability of spatial parameter estimates induced by snapshot-to-snapshot echo envelope fluctuations, a cost function based on fluctuation minimization is constructed. Furthermore, an optimized oblique projection (OP) strategy is developed to decouple overlapped echoes in the joint domain, thereby effectively extracting stable angular features of non-coherent sources under various stochastic scattering scenarios (e.g., Swerling models). Extensive simulations demonstrate that, compared with traditional MPV, Seung, and Blair methods, the proposed approach consistently achieves superior estimation precision and robustness, especially in challenging scenarios characterized by low signal-to-noise ratios (SNR), limited snapshots, and restricted polarimetric diversity. Moreover, experimental validation using real-world data from a 45-m civilian vessel and an active non-cooperative radio frequency (RF) source confirms the practical effectiveness of the algorithm in resolving extended targets in the presence of strong non-coherent background emissions. This work provides a reliable solution for high-fidelity 3D imaging of point target clusters in environments characterized by dense targets and complex electromagnetic interference. Full article

Polarimetric Synthetic Aperture Radar (PolSAR) has significant advantages for land-cover classification for its all-weather, day-and-night, and multi-polarization observation capability. Traditional methods often exhibit limited classification accuracy in regions with strong noise and complex textures. Although deep learning methods can improve classification performance, they usually suffer from high model complexity, while lightweight models often show insufficient spatial consistency. To address these issues, this study proposes a PolSAR land-cover classification framework that integrates a Lightweight Dynamic Sequential Axial Network (LiteDSANet) with a polarization feature-guided Dense Conditional Random Field (PFG-DenseCRF). LiteDSANet is employed to generate the initial class probability map, and PFG-DenseCRF optimizes the classification results by introducing polarimetric features. Experiments were conducted on AIRSAR L-band and RADARSAT-2 C-band datasets from the San Francisco Bay and Flevoland regions, covering agricultural, urban, and natural land-cover scenes. The results show that the proposed method improves classification accuracy by 2.14~15.36% compared with other methods, while achieving a favorable balance between accuracy and computational efficiency. These results demonstrate the effectiveness of the proposed method for PolSAR land-cover classification in different regional environments. Full article

Being capable of high-resolution imaging and non-contact measurement, Ground Penetrating Radar (GPR) is a promising technology for the detection of unexploded ordnance (UXO). However, UXO detection is severely hindered by clutter, particularly in environments with significant surface roughness where conventional suppression methods prove ineffective. To address this, we propose a polarimetric GPR clutter suppression method based on an improved non-convex Tensor Robust Principal Component Analysis (TRPCA) framework. Specifically, a polarization-aware tensor construction scheme is designed by stacking the HH and VV channel data. This approach exploits the strong inter-channel correlation of clutter to enhance its low-rank property, while highlighting the distinct sparse signatures of targets derived from their polarimetric responses. To further optimize tensor decomposition, we introduce a non-convex Tensor Adjustable Logarithmic Norm (TALN) to overcome the estimation bias inherent in the conventional Tensor Nuclear Norm (TNN). Serving as a tighter surrogate for tensor rank, the proposed TALN regularizer improves the approximation accuracy of the low-rank component, thereby ensuring a clearer separation between clutter and targets. The resulting non-convex optimization problem is efficiently solved using Alternating Direction Method of Multipliers (ADMM). Numerical simulations and laboratory experiments demonstrate that the proposed method suppresses strong clutter stemming from rough-surface reflections more effectively than existing methods, achieving a Signal-to-Clutter Ratio (SCR) improvement of over 20 dB. Full article