Search Results (682)

UAV-based inspections are an effective way to ensure railway safety and have gained significant attention. However, images captured during complex weather conditions, such as rain, snow, or fog, often suffer from severe degradation, affecting image recognition accuracy. Existing algorithms for removing rain, snow, and fog have two main limitations: they do not adaptively learn features under varying weather complexities and struggle with managing complex noise patterns in drone inspections, leading to incomplete noise removal. To address these challenges, this study proposes a novel framework for removing rain, snow, and fog from drone images, called WeatherClean. This framework introduces a Weather Complexity Adjustment Factor (WCAF) in a parameterized adjustable network architecture to process weather degradation of varying degrees adaptively. It also employs a hierarchical multi-scale cropping strategy to enhance the recovery of fine noise and edge structures. Additionally, it incorporates a degradation synthesis method based on atmospheric scattering physical models to generate training samples that align with real-world weather patterns, thereby mitigating data scarcity issues. Experimental results show that WeatherClean outperforms existing methods by effectively removing noise particles while preserving image details. This advancement provides more reliable high-definition visual references for drone-based railway inspections, significantly enhancing inspection capabilities under complex weather conditions and ensuring the safety of railway operations. Full article

Remote sensing images are often degraded by atmospheric haze, which not only reduces image quality but also complicates information extraction, particularly in high-level visual analysis tasks such as object detection and scene classification. State-space models (SSMs) have recently emerged as a powerful paradigm for vision tasks, showing great promise due to their computational efficiency and robust capacity to model global dependencies. However, most existing learning-based dehazing methods lack physical interpretability, leading to weak generalization. Furthermore, they typically rely on spatial features while neglecting crucial frequency domain information, resulting in incomplete feature representation. To address these challenges, we propose ScaleViM-PDD, a novel network that enhances an SSM backbone with two key innovations: a Multi-scale EfficientViM with Physical Decoupling (ScaleViM-P) module and a Dual-Domain Fusion (DD Fusion) module. The ScaleViM-P module synergistically integrates a Physical Decoupling block within a Multi-scale EfficientViM architecture. This design enables the network to mitigate haze interference in a physically grounded manner at each representational scale while simultaneously capturing global contextual information to adaptively handle complex haze distributions. To further address detail loss, the DD Fusion module replaces conventional skip connections by incorporating a novel Frequency Domain Module (FDM) alongside channel and position attention. This allows for a more effective fusion of spatial and frequency features, significantly improving the recovery of fine-grained details, including color and texture information. Extensive experiments on nine publicly available remote sensing datasets demonstrate that ScaleViM-PDD consistently surpasses state-of-the-art baselines in both qualitative and quantitative evaluations, highlighting its strong generalization ability. Full article

Terahertz (THz) wireless communications can support ultra-high data rates and secure wireless links with miniaturized devices for unmanned aerial vehicle (UAV) communications. In this paper, a three-dimensional (3D) non-stationary geometry-based stochastic channel model (GSCM) is proposed for multiple-input multiple-output (MIMO) communication links between the UAVs in the THz band. The proposed channel model considers not only the 3D scattering and reflection scenarios (i.e., reflection and scattering fading) but also the atmospheric molecule absorption attenuation, arbitrary 3D trajectory, and antenna arrays of both terminals. In addition, the statistical properties of the proposed GSCM (i.e., the time auto-correlation function (T-ACF), space cross-correlation function (S-CCF), and Doppler power spectrum density (DPSD)) are derived and analyzed under several important UAV-related parameters and different carrier frequencies, including millimeter wave (mmWave) and THz bands. Finally, the good agreement between the simulated results and corresponding theoretical ones demonstrates the correctness of the proposed GSCM, and some useful observations are provided for the system design and performance evaluation of UAV-based air-to-air (A2A) THz-MIMO wireless communications. Full article

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts. Full article

With the rapid development of smart connected vehicles, vehicle network communications demand high-speed data transmission to support advanced automotive services. Millimeter Wave (mmWave) communication offers fast data rates, strong anti-interference capabilities, high precision localization and low-latency, making it suitable for high-speed in-vehicle communications. However, mmWave communication performance in vehicular networks is hindered by high path loss and frequent beam alignment updates, significantly degrading the coverage and connectivity of vehicle nodes (VNs). In addition, atmospheric propagation attenuation further deteriorates signal quality and limits system performance due to raindrop absorption and scattering. Therefore, the pure mmWave networks cannot meet the high requirements of highway vehicular communications. To address these challenges, this paper proposes a hybrid mmWave and microwave network architecture to improve VNs’ coverage and connectivity performances through the strategic deployment of Roadside Units (RSUs). Using Radio Access Technology (RAT), mmWave and microwave RSUs are symmetrically deployed on both sides of the road to communicate with VNs located at the road center. This symmetric RSUs deployment significantly improves the network reliability. Analytical expressions for coverage and connectivity in the proposed hybrid networks are derived and compared with the pure mmWave networks, accounting for rainfall attenuation. The study results show that the proposed hybrid network shows better performance than the pure mmWave network in both coverage and connectivity. Full article

In InSAR processing, optimizing baselines by selecting appropriate interferometric pairs is crucial for ensuring interferogram quality and improving InSAR monitoring accuracy. However, in multi-temporal InSAR processing, the quality of interferometric pairs is constrained by spatiotemporal baseline parameters and surface scattering characteristics. Traditional selection methods, such as those based on average coherence thresholding, consider only a single factor and do not account for the interactions among multiple factors. This study introduces a principal component analysis (PCA) method to comprehensively analyze four factors: temporal baseline, spatial baseline, NDVI difference, and coherence, scientifically setting weights to achieve precise selection of interferometric pairs. Additionally, the GACOS (Generic Atmospheric Correction Online Service) atmospheric correction product is applied to further enhance data quality. Taking the Haikou Phosphate Mine area in Kunming, Yunnan, as the study area, surface deformation information was extracted using the SBAS-InSAR technique, and the spatiotemporal characteristics of subsidence were analyzed. The research results show the following: (1) compared with other methods, the PCA-based interferometric pair optimization method significantly improves the selection performance. The minimum value decreases to 0.248 rad, while the mean and standard deviation are reduced to 1.589 rad and 0.797 rad, respectively, effectively suppressing error fluctuations and enhancing the stability of the inversion; (2) through comparative analysis of the effective pixel ratio and standard deviation of deformation rates, as well as a comprehensive evaluation of the deformation rate probability density function (PDF) distribution, the PCA optimization method maintains a high effective pixel ratio while enhancing sensitivity to surface deformation changes, indicating its advantage in deformation monitoring in complex terrain areas; (3) the combined analysis of spatial autocorrelation (Moran’s I coefficient) and spatial correlation coefficients (Pearson and Spearman) verified the advantages of the PCA optimization method in maintaining spatial structure and result consistency, supporting its ability to achieve higher accuracy and stability in complex surface deformation monitoring. In summary, the PCA-based baseline optimization method significantly improves the accuracy of SBAS-InSAR in surface subsidence monitoring, fully demonstrating its reliability and stability in complex terrain areas, and providing a solid technical support for dynamic monitoring of surface subsidence in mining areas. Full article

A small-scale supersonic flight experiment vehicle is being developed at Muroran Institute of Technology as a flying testbed for verification of innovative technologies for high-speed atmospheric flights, which are essential to next-generation aerospace transportation systems. Its baseline configuration M2011 with a cranked-arrow main wing with an inboard and outboard leading edge sweepback angle of 66 and 61 degrees and horizontal and vertical tails has been proposed. Its aerodynamics caused by attitude motion are required to be clarified for six-degree-of-freedom flight capability prediction and autonomous guidance and control. This study concentrates on characterization of such aerodynamics caused by rolling rates in the subsonic regime. A mechanism for rolling a wind-tunnel test model at various rolling rates and arbitrary pitch angle is designed and fabricated using a programmable stepping motor and an equatorial mount. A series of subsonic wind-tunnel tests and preliminary CFD analysis are carried out. The resultant static derivatives have sufficiently small scatter and agree quite well with the static wind-tunnel tests in the case of a small pitch angle, whereas the static directional stability deteriorates in the case of large pitch angles and large nose lengths. In addition, the resultant dynamic derivatives agree well with the CFD analysis and the conventional theory in the case of zero pitch angle, whereas the roll damping deteriorates in the case of large pitch angles and proverse yaw takes place in the case of a large nose length. Full article

Canopy chlorophyll content (CCC) is a key indicator for assessing the carbon sequestration capacity and material cycling efficiency of ecosystems, and its accurate retrieval holds significant importance for analyzing ecosystem functioning. Although numerous destructive and remote sensing methods have been developed to estimate CCC, the accurate estimation of CCC remains a significant challenge in mountainous regions with complex terrain and heterogeneous vegetation types. Through the synergistic analysis of ground hyperspectral and Sentinel-2 data, this study employed Pearson correlation analysis and spectral resampling techniques to identify Sentinel-2 blue band B1 (443 nm) and red band B4 (665 nm) as chlorophyll-sensitive bands through spectral matching with the hyperspectral reflectance of typical grassland vegetation. Based on this, we developed a new four-band vegetation index (VI), the Dual Red-edge and Coastal Aerosol Vegetation Index (DRECAVI), for estimating the CCC of heterogeneous grasslands in the middle section of the Tianshan Mountains. DRECAVI incorporates red-edge anti-saturation modules (bands B4 and B7) and aerosol correction modules (bands B1 and B8). In order to test the performance of the new index, we compared it with eight commonly used indices and a hybrid model, the Sentinel-2 Biophysical Processor (S2BP). The results indicated the following: (1) DRECAVI demonstrated the highest accuracy in CCC retrieval for mountainous vegetation (R² = 0.74, RMSE = 16.79, MAE = 12.50) compared to other VIs and hybrid methods, effectively mitigating saturation effects in high biomass areas and capturing a weak bimodal distribution pattern of CCC in the montane meadow. (2) The blue band B1 enhances atmospheric correction robustness by suppressing aerosol scattering, and the red-edge band B7 overcomes the sensitivity limitations of conventional red-edge indices (such as NDVI705, CIred-edge, and NDRE), demonstrating the potential application of the synergy mechanism between the blue band and the red-edge band. (3) Although the S2BP achieved high accuracy (R² = 0.73, RMSE = 19.83, MAE = 14.71) without saturation effects and detected a bimodal distribution of CCC in the montane meadow of the study area, its algorithmic complexity hindered large-scale operational applications. In contrast, DRECAVI maintained similar precision while reducing algorithmic complexity, making it more suitable for regional-scale grassland dynamic monitoring. This study confirms that the synergistic use of multi-source data effectively overcomes the limitations of the spectral–spatial resolution of a single data source, providing a novel methodology for the precision monitoring of mountain ecosystems. Full article

In recent years, spaceborne Global Navigation Satellite System reflectometry (GNSS-R) technology has made significant progress in the fields of Earth observation and remote sensing, with a wide range of applications, important research value, and broad development prospects. However, despite existing research focusing on the application of spaceborne GNSS-R L1-level data, the potential value of raw intermediate-frequency (IF) signals has not been fully explored for special applications that require a high accuracy and spatiotemporal resolution. This article provides a comprehensive overview of the current status of the measurement of raw IF signals from spaceborne GNSS-R in multiple application fields. Firstly, the development of spaceborne GNSS-R microsatellites launch technology is introduced, including the ability of microsatellites to receive GNSS signals and receiver technique, as well as related frequency bands and technological advancements. Secondly, the key role of coherence detection in spaceborne GNSS-R is discussed. By analyzing the phase and amplitude information of the reflected signals, parameters such as scattering characteristics, roughness, and the shape of surface features are extracted. Then, the application of spaceborne GNSS-R in inland water monitoring is explored, including inland water detection and the measurement of the surface height of inland (or lake) water bodies. In addition, the widespread application of group delay sea surface height measurement and carrier-phase sea surface height measurement technology in the marine field are also discussed. Further research is conducted on the progress of spaceborne GNSS-R in the retrieval of ice height or ice sheet height, as well as tropospheric parameter monitoring and the study of atmospheric parameters. Finally, the existing research results are summarized, and suggestions for future prospects are put forward, including improving the accuracy of signal processing and reflection signal analysis, developing more advanced algorithms and technologies, and so on, to achieve more accurate and reliable Earth observation and remote sensing applications. These research results have important application potential in fields such as environmental monitoring, climate change research, and weather prediction, and are expected to provide new technological means for global geophysical parameter retrieval. Full article

Enhancing low-light remote sensing images is crucial for preserving the accuracy and reliability of downstream analyses in a wide range of applications. Although numerous enhancement algorithms have been developed, many fail to effectively address the challenges posed by non-uniform illumination in low-light scenes. These images often exhibit significant brightness inconsistencies, leading to two primary problems: insufficient enhancement in darker regions and over-enhancement in brighter areas, frequently accompanied by color distortion and visual artifacts. These issues largely stem from the limitations of existing methods, which insufficiently account for non-uniform atmospheric attenuation and local brightness variations in reflectance estimation. To overcome these challenges, we propose a robust enhancement method based on non-uniform illumination compensation and the Atmospheric Scattering Model (ASM). Unlike conventional approaches, our method utilizes ASM to initialize reflectance estimation by adaptively adjusting atmospheric light and transmittance. A weighted graph is then employed to effectively handle local brightness variation. Additionally, a regularization term is introduced to suppress noise, refine reflectance estimation, and maintain balanced brightness enhancement. Extensive experiments on multiple benchmark remote sensing datasets demonstrate that our approach outperforms state-of-the-art methods, delivering superior enhancement performance and visual quality, even under complex non-uniform low-light conditions. Full article

The SPICAV IR spectrometer aboard the Venus Express orbiter measured spectra of the 1.1 and 1.18 μm atmospheric transparency windows at the Venus night side in 2006–2014. The long-term measurements encompassed the major part of the Venus globe, including polar latitudes. For the first time, the H₂O volume mixing ratio in the deep Venus atmosphere at about 10–16 km has been retrieved for the entire SPICAV IR dataset using a radiative transfer model with multiple scattering. The retrieved H₂O volume mixing ratio is found to be sensitive to different approximations of the H₂O and CO₂ absorption lines’ far wings and assumed surface emissivity. The global average of the H₂O abundance retrieved for different parameters ranges from 23.6 ± 1.0 ppmv to 27.7 ± 1.2 ppmv. The obtained values are consistent with recent studies of water vapor below the cloud layer, showing the H₂O mixing ratio below 30 ppmv. Within the considered dataset, the zonal mean of the H₂O mixing ratio does not vary significantly from 60° S to 75° N, except for a 2 ppmv decrease noted at high latitudes. The H₂O local time distribution is also uniform. The 8-year observation period revealed no significant long-term trends or periodicities. Full article

Aerosols play a crucial role in air quality, climate regulation, and public health; their timely monitoring is hence fundamental. The aerosol optical depth (AOD) is the parameter used to investigate the spatial–temporal distribution of aerosols from space. Specifically, the AOD retrieved from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) is suitable for aerosol investigation at a local scale by exploiting its high spatial resolution (1 km × 1 km). In this study, the MAIAC AOD retrieval over Rome (Italy) was validated for the first time, using ground-based data provided by an AERONET station operating in a semi-rural environment close to the city, over a time series from January 2001 to December 2022. Moreover, AOD trends were evaluated in a study area encompassing Rome and its surroundings, characterized by a transition zone between urban and rural environments. The results show a general underestimation of the MAIAC AOD; specifically, the validation process highlighted the less accurate performance of the algorithm under higher aerosol loading and with predominantly coarse mode aerosol. Interesting results were obtained concerning the influence of the geometrical configuration of satellite acquisition on the accuracy of the MAIAC product. In particular, the solar zenith angle, the relative azimuth and the scattering angle between the principal plane of the sun and satellite synergistically influence retrievals. Finally, the spatial distribution of the AOD shows a decreasing trend over the 2001–2022 period and a strong influence of the city of Rome over the whole study area. Full article

Aerosol optical properties and radiative forcing critically influence Earth’s climate, particularly in semi-arid regions. This study investigates these properties in Yinchuan, Northwest China, focusing on aerosol optical depth (AOD), single-scattering albedo (SSA), Ångström Index, and direct radiative forcing (DRF) using 2023 CE-318 sun photometer data, HYSPLIT trajectory analysis, and the SBDART model. Spring AOD peaks at 0.58 ± 0.15 (500 nm) due to desert dust, with coarse-mode particles dominating, while summer SSA reaches 0.94, driven by fine-mode aerosols. Internal mixing of dust and anthropogenic aerosols significantly alters DRF through enhanced absorption, with spring surface DRF at −101 ± 22W m⁻² indicating strong cooling and internal mixing increasing atmospheric DRF to 52.25W m⁻². These findings elucidate dust–anthropogenic interactions’ impact on optical properties and radiative forcing, offering critical observations for semi-arid climate research. Full article

Haze caused by atmospheric scattering often leads to color distortion, reduced contrast, and diminished clarity, which significantly degrade the quality of remote sensing images. To address these issues, we propose a novel network called DWTMA-Net that integrates discrete wavelet transform with multi-dimensional attention, aiming to restore image information in both the frequency and spatial domains to enhance overall image quality. Specifically, we design a wavelet transform-based downsampling module that effectively fuses frequency and spatial features. The input first passes through a discrete wavelet block to extract frequency-domain information. These features are then fed into a multi-dimensional attention block, which incorporates pixel attention, Fourier frequency-domain attention, and channel attention. This combination allows the network to capture both global and local characteristics while enhancing deep feature representations through dimensional expansion, thereby improving spatial-domain feature extraction. Experimental results on the SateHaze1k, HRSD, and HazyDet datasets demonstrate the effectiveness of the proposed method in handling remote sensing images with varying haze levels and drone-view scenarios. By recovering both frequency and spatial details, our model achieves significant improvements in dehazing performance compared to existing state-of-the-art approaches. Full article