Search Results (273)

To improve depth reconstruction accuracy of streak tube imaging LiDAR (STIL) under weak echo and low-contrast conditions in complex scattering environments, this paper proposes a hierarchical reliability-guided multispectral polarization reconstruction framework (MSP-STIL). The proposed method addresses measurement uncertainty in multi-wavelength and multi-polarization observations by constructing a progressive reliability modeling strategy, which evolves from polarization stability to statistical uncertainty and finally to signal strength enhancement. First, a Dual-channel Polarization Contrast (Pc) is introduced to evaluate local scattering stability and suppress fringe peak degradation caused by polarization distortion. Second, SNR is employed to model the uncertainty of depth measurements across different wavelength channels. Finally, echo intensity is incorporated as a confidence refinement factor to further enhance high-quality signals. Based on this hierarchical modeling strategy, an adaptive inverse-variance weighting scheme is developed to achieve robust multi-wavelength depth fusion. The results show that the proposed method outperforms equal-weight and single-feature methods in all test regions. Compared with the non-weighted method, the MAE, RE, MSE, RMSE, and STD are reduced by approximately 10.95%, 12.02%, 25.44%, 13.68%, and 15.58% on average, respectively. Under low signal-to-noise conditions (simulated by controlled noise levels) and long-distance detection scenarios, the proposed method still maintains low reconstruction errors and effectively suppresses depth fluctuations, demonstrating good noise resistance and distance robustness. In addition, the maximum contrast of the RGB image constructed through weighted fusion increases from 5.0065 to 5.8300, verifying the effectiveness of the proposed method in low-contrast complex scenes. Overall, the proposed MSP-STIL multi-feature joint weighting method shows clear advantages in reconstruction accuracy, robustness, and scene adaptability. Full article

The subtropical hilly regions of southern China are characterized by dense vegetation and highly concealed landslides, making it difficult for traditional, single-source remote sensing methods to meet disaster prevention needs. The core scientific contribution of this study is the development of a hierarchical, progressive hazard identification framework that bridges the gap between InSAR deformation detection and landslide risk identification. This study focuses on Mayang County, Hunan Province, China, and combines time-series InSAR data from C-band Sentinel-1 and L-band ALOS-2 with a random forest (RF) algorithm to construct an early-stage identification model for landslide risks. All SAR data were processed under controlled baseline conditions (perpendicular baseline <150 m; polarization: VV for Sentinel-1, HH for ALOS-2). By screening highly reliable deformation points through dual-source cross-validation and integrating nine evaluation factors including slope, we established a two-layer coupled identification model combining InSAR deformation and susceptibility indices at the slope unit scale. The results showed that the dual-source InSAR approach achieved an identification accuracy of 71% (precision 68%, recall 65%, F1-score 0.66, Cohen’s κ 0.62), significantly outperforming single-source methods (62% for Sentinel-1 alone and 58% for ALOS-2 alone); the AUC was 0.815 under spatial block cross-validation, with an out-of-bag error of 16.8%. The dual-source InSAR approach identified a total of 59 potential hazard sites, 83.1% of which were located in medium- to high-risk zones. Following field surveys and LiDAR verification, 35 of these were confirmed as active landslide sites, demonstrating identification accuracy significantly superior to that of a single data source. The multi-source coupling framework proposed in this study effectively overcomes the decoherence issues associated with single-source SAR data in subtropical vegetated areas, providing reliable technical support for the early identification of landslides in humid hilly regions of southern China. Full article

Multi-wavelength Raman–Mie polarization lidar provides vertically resolved measurements of aerosol scattering, particle shape, and wavelength-dependent response. We analyzed quality-controlled clear-sky observations from June 2025 to March 2026 to identify aerosol optical regimes at inland Beijing Nanjiao and coastal Beihai. The feature space comprised ${\log }_{10}\left({\beta }_{532}\right)$, ${\delta }_{p,532}$, and ${\mathrm{AE}}_{\beta ,355/532}$, representing scattering intensity, particle nonsphericity, and size-sensitive spectral response. K-means was applied independently at each site. At Beijing Nanjiao, a high-depolarization, strong-scattering, and low-${\mathrm{AE}}_{\beta ,355/532}$ regime was concentrated in the lowest observed layer, whereas lower-loading regimes occurred more frequently aloft. Its dust AOD and dust fraction were descriptively 37.5% and 19.2% above the site means, although the dust-related inter-regime differences were not significant after FDR correction. Beihai was dominated by low-depolarization regimes separated mainly by scattering intensity and wavelength response. Its strongest-scattering regime showed total, sea salt, OC, and sulfate AOD enhancements of 33.0%, 15.5%, 24.5%, and 41.9%, respectively; the inter-regime differences were significant for total and sulfate AODs but not for sea salt AOD. MERRA-2 aerosol diagnostics and trajectory analyses provided auxiliary regional context for interpreting the lidar-defined optical states and were not used as clustering inputs or direct chemical validation. These results demonstrate that combined polarization and multi-wavelength lidar sensing can distinguish vertically varying aerosol optical states that are obscured in surface or column-integrated observations. Full article

Multi-modal 3D object detection is an important task in autonomous driving systems, where cameras and LiDAR provide complementary semantic and geometric information. Most existing BEV fusion methods are designed based on the Cartesian representation space, which does not fully match the sensing geometry of camera and LiDAR. This generally leads to redundant computation in distant regions. To address this issue, GARF, a geometry-aware polar BEV framework, is presented for multi-modal 3D object detection. GARF organizes camera and LiDAR features in a unified polar BEV space, which can represent spatial resolution more compactly. For the camera branch, the uncertainty-guided transformation of the polar view is designed to improve the reliability of depth estimation. Then, the generated polar BEV feature is further refined to attenuate radial noise and angular discontinuity. For the LiDAR branch, the polar-aware sparse feature extraction and distortion correction modules are designed to deal with the anisotropic structure and geometric distortion caused by polar voxelization. For multi-modal fusion, the region-aware cross-modal fusion strategy and polar detection head with anisotropic Gaussian center response map are developed, which achieve effective feature interaction and consistent geometry supervision. The experimental results on nuScenes show that GARF achieves 71.8% mAP and 73.7% NDS, improving the baseline by 3.3% mAP and 2.3% NDS. Meanwhile, the inference speed increases from 7.1 FPS to 8.9 FPS, and the consumption of GPU memory decreases from 41,114 MiB to 33,346 MiB. Full article

Supraglacial lake depth is a key variable for quantifying surface meltwater storage and assessing ice-shelf stability, yet spatially continuous and reliable bathymetric information remains difficult to obtain in polar regions because in situ measurements are scarce and optical imagery cannot directly provide water depth. This study develops an integrated framework for supraglacial lake identification and bathymetry retrieval by combining ICESat-2 ATL03 photon-counting lidar data with Sentinel-2 multispectral imagery. ICESat-2 lake photons were used to constrain lake-region extraction from Sentinel-2 imagery, and the photon-derived along-track depths were corrected for scattering and refraction before being converted into Sentinel-2 pixel-level depth labels. Based on these labels, four retrieval models were constructed and evaluated, including an empirical model, CatBoost, a convolutional neural network (CNN), and a residual dense network (RDN). CatBoost generated initial depth estimates, while CNN and RDN further incorporated the CatBoost-derived depth prior and Sentinel-2 multispectral features for pixel-level depth prediction. Experiments over four investigated supraglacial lakes showed that RDN achieved the best average performance across the investigated lakes, with mean R², RMSE, and MAE values of 0.927, 0.187 m, and 0.144 m, respectively. For the investigated lakes, the integration of ICESat-2 and Sentinel-2 extended discrete along-track reference-depth observations to spatially continuous bathymetry maps. Because the training and validation samples were obtained from different spatial blocks within the same four lake scenes, the reported performance primarily reflects within-lake spatial generalization under the investigated conditions, and transferability to unseen lakes remains to be evaluated. These maps may provide inputs for future lake-volume estimation and ice-shelf hydrological analyses, while their applicability to lakes with different morphological and optical conditions requires further evaluation. Full article

Lidar systems are of major importance for driver assistance systems and autonomous driving, but their obstacle detection range can be heavily impaired by adverse weather conditions like fog. To mitigate the effects of fog, the use of polarized light has been proposed. We simulated a polarized lidar system, taking Mie theory-based scattering functions for different fog types at several wavelengths into account, and investigated the possible increase in contrast between fog backscattering and target returns for different surface types, depending on the target orientation and the polarizer configuration. Fog is modeled as an infinite, homogeneous, scattering, and absorbing medium. The simulated detector registers the time-dependent radiance, resolved by scattering order. Our findings show that the suitable choice of illumination and detection polarization allows the amount of single-scattered light detected to be reduced by two to four orders of magnitude and the contrast between fog and target to be increased significantly. Crucially, this approach delivers robust contrast enhancement regardless of whether the target surface is depolarizing Lambertian or perfectly reflecting. Full article

Light detection and ranging (LiDAR) systems are pivotal for precise distance and velocity measurement, yet widespread deployment requires solutions that balance their performance, robustness, and simplicity. Here, we propose a novel chaotic LiDAR system based on a semiconductor vertical-cavity surface-emitting laser (VCSEL) with delayed orthogonal polarization feedback. By exploiting the intrinsic competition between the transverse electric (TE) and transverse magnetic (TM) modes, the system generates polarization-multiplexed dynamics: a chaotic TM mode serves as the reference, while a feedback-modulated TE mode probes the target. This all-in-one source eliminates the need for external optical modulators or complex coherent detection. The system’s dynamics are finely tunable via a half-wave ($\lambda$/2) plate in the feedback loop and the laser injection current, enabling real-time optimization of the cross-correlation signal-to-noise ratio. Experimental results demonstrate precise linear ranging with a resolution of approximately 1.2 cm. Furthermore, the system exhibits strong inherent resistance to external optical interference, maintaining accurate ranging even in the presence of a secondary laser source. This compact, tunable, and interference-resilient platform offers a promising pathway toward low-cost, high-performance LiDAR for applications in autonomous navigation, robotics, and industrial metrology. Full article

Stratospheric airship navigation requires accurate wind field measurements at a ~20 km altitude, where low pressure and density limit the effectiveness of conventional wind sensors. To address this, we present a compact direct-detection Rayleigh Doppler wind lidar based on the molecular double-edge technique. The system utilizes a 532 nm fiber-coupled pulsed laser (0.5 W, 5 ns) and a fixed-cavity dual-channel Fabry–Perot etalon as the frequency discriminator. A liquid crystal variable retarder (LCVR) combined with a polarization beam splitter (PBS) enables non-mechanical, high-speed beam switching between two orthogonal line-of-sight (LOS) directions for horizontal wind measurement. Systematic tests are performed in controlled wind fields within Mie-dominated and Rayleigh-dominated regimes. The lidar effectively captures the sharp radial velocity profiles at wind speeds up to 7.6 m/s. Comparative experiments with a reference anemometer show that the system delivers reliable performance at 0.48 m range resolution, with measurement uncertainty below 0.34 m/s. With its compact, lightweight, and high-precision design, the developed lidar demonstrates reliable wind measurement capability under laboratory conditions, indicating its potential for future deployment on stratospheric airships. Full article

Atmospheric dust is an important contributor to atmospheric particulate matter (PM) and a significant factor to air quality degradation worldwide. In this study, we assess atmospheric dust as an air quality hazard to the world population. Near-surface dust concentrations are quantified through the synergy of the “LIdar climatology of Vertical Aerosol Structure” (LIVAS) atmospheric dust data record, established based on Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol profiles, and European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 information on planetary boundary layer (PBL). The health risk to the global population is estimated using empirically derived epidemiological exposure–response relationships that approximate the association between PM concentrations and adverse health outcomes. Our findings reveal elevated health risks in regions affected by major desert sources or over densely populated and highly industrialized regions. Approximately nine-out-of-ten (~91% or ~6.8 billion people) of the global population experience total dust concentrations below the World Health Organization (WHO) annual-mean PM₁₀ guideline. However, a substantially larger proportion of approximately one-out-of-three (~33.5% or ~2.5 billion people) is exposed to submicrometer-mode dust concentrations exceeding the respective PM_2.5 recommended threshold. The study highlights atmospheric dust as an important air quality hazard and demonstrates the value of satellite observations for assessing trustworthy health risks and supporting environmental policies. Full article

LiDAR-based place recognition is critical for long-term autonomous navigation in Global Navigation Satellite System (GNSS)-denied environments, yet existing methods struggle to balance accuracy and efficiency under substantial yaw rotations. This paper proposes a robust framework based on a multi-channel polar bird’s-eye-view (BEV) representation. Under yaw-dominated revisits, the polar BEV image transforms yaw rotation into cyclic column shifts, providing a useful structural prior for rotation-equivariant feature extraction. Raw point clouds are projected onto polar BEV grids encoding density, height, and intensity. A rotation-equivariant feature extractor comprising a Radial Compression Module and a rotation-equivariant Transformer module captures long-range azimuthal dependencies via Conditional Positional Encoding and Circular Relative-Position Bias. The equivariant features are aggregated by NetVLAD into a compact global descriptor, trained end-to-end with a hard-example mining triplet loss. Extensive experiments on the public KITTI and NCLT datasets, as well as our self-constructed LiDAR Place Recognition Revisit (LPRR) dataset, demonstrate competitive performance on KITTI and superior performance on NCLT and LPRR among the compared methods. The proposed framework achieves a favorable trade-off between performance and computational cost, and shows promising cross-dataset generalization on the evaluated NCLT and LPRR datasets without fine-tuning. Full article

To address the limited constraints of ground-based lidar with few channels in retrieving aerosol microphysical parameters in urban atmospheres, this study developed a method to retrieve aerosol volume size distribution and effective radius from a 355/532/1064 nm triple-wavelength elastic-scattering, single-polarization lidar system. The method uses 3β + 2α optical quantities as input constraints, applies Mie scattering theory as the forward model, parameterizes the volume size distribution with B-spline functions, and achieves stable solutions through Tikhonov regularization and cross-validation. To reduce uncertainties in prior parameters, including the complex refractive index, particle size range, and lidar ratio, an optimization strategy based on parameter search, retrieval reconstruction, and error minimization was introduced. Numerical simulations showed that the method reproduced the main features of a bimodal lognormal aerosol volume size distribution with good feasibility and stability. Two case studies further showed fine-mode dominance and decreasing extinction coefficient, depolarization ratio, and effective radius with height under good air quality conditions, but enhanced coarse-mode contribution and effective radius in the upper cloud-influenced layer under lightly polluted conditions, as inferred from the combined variations in RSCS, extinction coefficient, depolarization ratio, and effective radius. Full article

Accurate mapping of aboveground biomass (AGB) in mountainous forests is essential for carbon stock assessment and ecological management, yet remains challenging due to the difficulty of linking local high-precision observations with regionally continuous coverage. To address this issue, we developed a hierarchical framework integrating local reference construction, UAV–GEDI bridging, footprint-level modeling, and regional continuous mapping, applied to the mountainous forests of Simao District, Pu’er City, Yunnan Province, China. Field plot measurements and UAV-borne LiDAR data were first used to construct a local AGB reference product, which was then transferred to the GEDI footprint scale through geolocation correction and footprint-scale quality control, yielding 252 valid bridging samples across three UAV flight zones, with approximately 65% originating from the TYH zone. Among five candidate models evaluated for GEDI footprint-level AGB estimation, the Stacking ensemble model performed best, with a pooled out-of-fold R² of 0.736 and RMSE of 24.15 Mg ha⁻¹, and was subsequently applied to 89,579 GEDI footprints across the study area. For regional continuous mapping, the empirical Bayesian kriging regression prediction (EBKRP) scheme combining Landsat TCW, Sentinel-2 IRECI, and the Sentinel-1 polarization ratio achieved the best external validation performance, with R² of 0.622 and RMSE of 26.05 Mg ha⁻¹ based on 61 independent field plots. These results indicate that the proposed hierarchical framework effectively bridges local high-precision observations and regional continuous AGB mapping in complex mountainous forest environments, offering a systematic methodological reference for GEDI-based forest carbon monitoring. 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

We developed a reference look-up table (RLUT) of particles of spheroidal shape. This RLUT will be used in our lidar-data inversion algorithm we have developed in the past 25 years for the retrieval of microphysical parameters of non-spherical particles from 3β + 2α + 3δ optical datasets measured with Raman/HSRL lidar. The optical datasets are described by particle backscatter coefficients (β) at three wavelengths λ = 355, 532, and 1064 nm, particle extinction coefficients (α) at two wavelengths λ = 355 and 532 nm, and particle linear depolarization ratios (PLDRs, δ) at three wavelengths λ = 355, 532, and 1064 nm. The RLUT contains 64,032 synthetic 3β + 2α + 3δ—datasets calculated on the basis of a light-scattering model of randomly oriented spheroids and spheroid particle size distributions described by different particle complex refractive indices (CRIs) and lognormal functions with different Gauss parameters such as mean radius (μ) and standard deviation (σ). We investigate major features of the RLUT such as information content encoded in the 3β + 2α + 3δ datasets, conditionality, determinacy and the sensitivity of the retrievals to the underlying measurement errors. We find that major features of the sphere and spheroid RLUTs are similar; however, extra information is encoded in the PLDRs. The PLDR spectrum on the domain λ ∈ [355; 1064] μm contains significant information about the size of spheroid particles. The analysis of the information content is more productive if we use the cross-polarized backscatter-related Ångström exponent (CrPBAE) at the wavelength pairs 355 and 532 nm [${\dot{\beta }}^{\perp }$(355/532)] and the wavelength pairs 532 and 1064 nm [${\dot{\beta }}^{\perp }$(532/1064)]. In particular, the cycloid-like behavior of the interdependency ${\dot{\beta }}^{\perp }$(355/532) versus ${\dot{\beta }}^{\perp }$(532/1064), i.e., hysteresis, means that non-spherical particle size changes. Full article

Extrinsic chiral metasurfaces offer a promising route for controlling chiroptical responses through incident angle variation, yet the simultaneous realization of strong circular dichroism and full-space polarization beam splitting remains challenging. In this work, we propose an all-dielectric extrinsic chiral metasurface that leverages obliquely incident terahertz waves to break in-plane symmetry, thereby activating out-of-plane multipoles and inducing strong spin-selective scattering. At an incident angle of 30°, the metasurface achieves efficient full-space separation of left- and right-handed circularly polarized waves, with a circular dichroism peak exceeding 0.7 near 0.48 THz. Moreover, by varying the incident angle or operating frequency, the polarization state of the reflected wave can be continuously tuned from linear to elliptical to nearly circular, as visualized on the Poincaré sphere. This angle-dependent, full-space polarization manipulation capability highlights the potential of the proposed metasurface for applications in advanced terahertz imaging, LiDAR, and integrated photonic systems. Full article