Search Results (172)

An iterative fractional Doppler shift and channel joint estimation algorithm is proposed for orthogonal time frequency space (OTFS) satellite communication systems. In the algorithm, we search the strongest path and estimate its fractional Doppler offset, and compensate the Doppler shift to the nearest integer to estimate the coefficient of the path. Then signal of the path and its inter-Doppler interference are reconstructed and canceled from the received data with these two estimated parameters. The estimation and cancel process are iteratively conducted until the strongest path in the remained paths is less than the predetermined threshold. The channel information can be reconstructed by the estimated parameters of the paths. The normalized mean squared error (NMSE) of the proposed channel estimation algorithm is less than 1/5 of the available algorithms at a high signal-to-noise ratio (SNR) region, and its BER has about 4dB SNR gain compared with those of the available algorithms when the bit error rate (BER) is ${10}^{-3}$. Full article

Hundreds of Japanese features of war (field positions, tunnels, and fortifications) were constructed in Hong Kong during World War II. However, most of them were poorly documented and were left unknown but still in relatively good condition because of their durable design, workmanship, and remoteness. These features of war form parts of Hong Kong’s brutal history. Conservation, at least in digital form, is worth considering. With the authors coming from multidisciplinary and varied backgrounds, this paper aims to explore these features using a scientific workflow. First, we reviewed the surviving archival sources of the Imperial Japanese Army and Navy. Second, airborne LiDAR data were used to form territory digital terrain models (DTM) based on the Red Relief Image Map (RRIM) for identifying suspected locations. Third, field expeditions of searching for features of war were conducted through guidance of Global Navigation Satellite System—Real-Time Kinetics (GNSS-RTK). Fourth, the found features were 3D-laser scanned to generate mesh models as a digital archive and validate the findings of DTM-RRIM. This study represents a reverse-engineering effort to reconstruct the planned Japanese defense tactics of guerilla fight and Kamikaze grottos that were never used in Hong Kong. Full article

Advancements in remote sensing technology have remarkably enhanced the 3D Earth surface reconstruction, which is pivotal for applications such as disaster relief, emergency management, and urban planning, etc. Although satellite imagery offers a cost-effective and extensive coverage solution for 3D reconstruction, the quality of the resulted digital surface model (DSM) heavily relies on the choice of stereo image pairs. However, current approaches of stereo Earth observation still employ a post-acquisition manner without sophisticated planning in advance, causing inefficiencies and low reconstruction quality. This paper introduces a novel quality-driven planning method for satellite stereo imaging, aiming at optimizing the search of stereo pairs to achieve high-quality 3D reconstruction. Moreover, a regression model is customized and incorporated to estimate the reconstructed point cloud geopositioning quality, based on the enhanced features of possible Earth-imaging opportunities. Experiments conducted on both real satellite images and simulated constellation data demonstrate the efficacy of the proposed method in estimating reconstruction quality beforehand and searching for optimal stereo pair combinations as the final satellite imaging schedule, which can improve the stereo quality significantly. Full article

The current methods for removing thick clouds from remote-sensing images face significant limitations, including the integration of thick cloud images with synthetic aperture radar (SAR) ground information, the provision of meaningful guidance for SAR ground data, and the accurate reconstruction of textures in cloud-covered regions. To overcome these challenges, we introduce SAR-DeCR, a novel method for thick cloud removal in satellite remote-sensing images. SAR-DeCR utilizes a diffusion model combined with the transformer architecture to synthesize accurate texture details guided by SAR ground information. The method is structured into three distinct phases: coarse cloud removal (CCR), SAR-Fusion (SAR-F) and cloud-free diffusion (CF-D), aimed at enhancing the effectiveness of the thick cloud removal. In CCR, we significantly employ the transformer’s capability for long-range information interaction, which significantly strengthens the cloud removal process. In order to overcome the problem of missing ground information after cloud removal and ensure that the ground information produced is consistent with SAR data, we introduced SAR-F, a module designed to incorporate the rich ground information in synthetic aperture radar (SAR) into the output of CCR. Additionally, to achieve superior texture reconstruction, we introduce prior supervision based on the output of the coarse cloud removal, using a pre-trained visual-text diffusion model named cloud-free diffusion (CF-D). This diffusion model is encouraged to follow the visual prompts, thus producing a visually appealing, high-quality result. The effectiveness and superiority of SAR-DeCR are demonstrated through qualitative and quantitative experiments, comparing it with other state-of-the-art (SOTA) thick cloud removal methods on the large-scale SEN12MS-CR dataset. Full article

Ocean temperature and salinity are core elements influencing ocean dynamics and biogeochemical cycles, critical to climate change and ocean process studies. In recent years, Argo floats and satellite remote sensing data have provided key support for observing and reconstructing three-dimensional (3D) ocean temperature and salinity. However, due to the challenges and high costs of in situ observations and the limitation of satellite measurements to surface data, effectively combining multi-source data to enhance the reconstruction accuracy of 3D temperature and salinity remains a significant challenge. In this study, we propose a VI-UNet model that incorporates a Vision Transformer module into UNet model and apply it to reconstruct 3D temperature and salinity in the equatorial oceans (20°S–20°N, 20°E–60°W) at depths from 1 to 6000 m using sea surface data acquired by satellites. In addition, we also investigate the impact of incorporating significant wave height (SWH) on the reconstruction of temperature and salinity. The results demonstrate that the VI-UNet model performs remarkably well in reconstructing temperature and salinity, achieving maximum reductions in root mean square error (RMSE) of up to 40% and 100%, respectively. Additionally, incorporating SWH enhances model accuracy, particularly in the upper 1000 m. Full article

Accurate weather data are very important for the navigation of ships. However, due to the insufficient coverage of the maritime network, the high cost of satellite communication, and the limited bandwidth, it is difficult for ships to obtain high-resolution weather data during route planning. This challenge greatly limits the accuracy and effectiveness of ship navigation. To solve this problem, this paper proposes a marine weather data reconstruction model based on deep super-resolution. Firstly, the model uses a convolutional neural network to extract features from wind speed and wave height data. Secondly, the model uses SRResNet as the reconstruction framework and effectively captures the complex nonlinear feature relationship in weather data through the residual block structure to realize the fine reconstruction of low-resolution weather data. In addition, the attention mechanism is integrated into the model to dynamically adjust the weights of different weather features, which further enhances the attention to key features. The results show that the model has a good effect on the super-resolution reconstruction of weather data. The PSNR, SSIM, GMSD, and FSIM of wave height reconstruction are 49.73 dB, 0.9949, 0.0082, and 0.9999, respectively, and the PSNR, SSIM, GMSD, and FSIM of wind speed reconstruction are 41.52 dB, 0.9797, 0.0400, and 0.9997, respectively. Based on the reconstructed data, route planning can effectively reduce the navigation distance of the ship and avoid unnecessary detours, thus saving fuel consumption and reducing operating costs. Full article

Novel view synthesis of remote sensing scenes from satellite images is a meaningful but challenging task. Due to the wide temporal span of image acquisition, satellite image collections often exhibit significant appearance variations, such as seasonal changes and shadow movements, as well as transient objects, making it difficult to reconstruct the original scene accurately. Previous work has noted that a large amount of image variation in satellite images is caused by changing light conditions. To address this, researchers have proposed incorporating the direction of solar rays into neural radiance fields (NeRF) to model the amount of sunlight reaching each point in the scene. However, this approach fails to effectively account for seasonal variations and suffers from a long training time and slow rendering speeds due to the need to evaluate numerous samples from the radiance field for each pixel. To achieve fast, efficient, and high-quality novel view synthesis for multi-temporal satellite scenes, we propose SatGS, a novel method that leverages 3D Gaussian points for scene reconstruction with an appearance-adaptive adjustment strategy. This strategy enables our model to adaptively adjust the seasonal appearance features and shadow regions of the rendered images based on the appearance characteristics of the training images and solar angles. Additionally, the impact of transient objects is mitigated through the use of visibility maps and uncertainty optimization. Experiments conducted on WorldView-3 images demonstrate that SatGS not only renders superior image quality compared to existing State-of-the-Art methods but also surpasses them in rendering speed, showcasing its potential for practical applications in remote sensing. Full article

Pansharpening is a remote sensing image fusion technique that combines a high-resolution (HR) panchromatic (PAN) image with a low-resolution (LR) multispectral (MS) image to produce an HR MS image. The primary challenge in pansharpening lies in preserving the spatial details of the PAN image while maintaining the spectral integrity of the MS image. To address this, this article presents a generative adversarial network (GAN)-based approach to pansharpening. The GAN discriminator facilitated matching the generated image’s intensity to the HR PAN image and preserving the spectral characteristics of the LR MS image. The performance in generating images was evaluated using the peak signal-to-noise ratio (PSNR). For the experiment, original LR MS and HR PAN satellite images were partitioned into smaller patches, and the GAN model was validated using an 80:20 training-to-testing data ratio. The results illustrated that the super-resolution images generated by the SRGAN model achieved a PSNR of 31 dB. These results demonstrated the developed model’s ability to reconstruct the geometric, textural, and spectral information from the images. Full article

Accurately and timely estimating three-dimensional ocean states is crucial for improving operational ocean forecasting capabilities. Although satellite observations provide valuable evolutionary information, they are confined to surface-level variables. While in situ observations can offer subsurface information, their spatiotemporal distribution is highly uneven, making it difficult to obtain complete three-dimensional ocean structures. This study developed an operational-oriented lightweight framework for three-dimensional ocean state reconstruction by integrating multi-source observations through a computationally efficient multivariate empirical orthogonal function (MEOF) method. The MEOF method can extract physically consistent multivariate ocean evolution modes from high-resolution reanalysis data. We utilized these modes to further integrate satellite remote sensing and buoy observation data, thereby establishing physical connections between the sea surface and subsurface. The framework was tested in the South China Sea, with optimal data integration schemes determined for different reconstruction variables. The experimental results demonstrate that the sea surface height (SSH) and sea surface temperature (SST) are the key factors determining the subsurface temperature reconstruction, while the sea surface salinity (SSS) plays a primary role in enhancing salinity estimation. Meanwhile, current fields are most effectively reconstructed using SSH alone. The evaluations show that the reconstruction results exhibited high consistency with independent Argo observations, outperforming traditional baseline methods and effectively capturing the vertical structure of ocean eddies. Additionally, the framework can easily integrate sparse in situ observations to further improve the reconstruction performance. The high computational efficiency and reasonable reconstruction results confirm the feasibility and reliability of this framework for operational applications. Full article

Monitoring volumetric glacier change requires cost-effective and accessible methods to generate multi-temporal digital elevation models (DEMs). We present Planet4Stereo, an open-source photogrammetry pipeline developed to generate DEMs from low-cost PlanetScope images, exploiting the high temporal repetition rate of the constellation for stereo reconstruction. Our approach enables multi-temporal 3D change detection using the freely available NASA Ames Stereo Pipeline (ASP), making the pipeline particularly valuable for geoscientists. We applied Planet4Stereo in two case studies: the Shisper glacier (Karakoram, Pakistan) for surge investigation and the Bøverbrean glacier (Smørstabb Massif, Norway) for change detection. The results from Shisper are in good agreement with previous studies using the same images but proprietary methods. The accuracy of the DEM of Bøverbrean was evaluated using high-precision LiDAR data, revealing varying deviations across terrain types, with higher errors in steep shadowed areas. Additionally, the change detection analysis confirmed the expected glacier retreat. Our results show that Planet4Stereo produces DEMs with comparable accuracy to commercial software and is freely accessible and easy to use. As both ASP and the PlanetScope satellites evolve, future work could refine the pipeline’s stereo-matching capabilities and evaluate its performance with next-generation satellite data. Full article

In a global landscape where the launch of satellites into space is growing exponentially, there is an increasing demand for propulsion solutions to perform various types of maneuvers. In this context, the present study aims to investigate a 3D-printed ABS (Acrylonitrile Butadiene Styrene)-based fuel for use in a 10 N-scale hybrid rocket in order to promote cost-effective and environmentally friendly access to space. As this material is currently unknown in this field and lacks a thermodynamic database, characterization of its pyrolysis process was carried out through a mixed approach combining experimental data and numerical simulations. The experiments show excellent performance of the H₂O₂-3D-printed ABS pair; despite the lack of information on its thermodynamically relevant quantities, it was possible to accurately reconstruct the fuel consumption profile as well as its regression rate and the spatial and temporal average values using the numerical model and Arrhenius parameters derived in this work. The methodology and results obtained herein represent tools that can be useful for the design of small-scale rockets using 3D-printed ABS-based fuels as well as a starting point for the development and analysis of the complex geometries made possible through additive manufacturing. Full article

Spaceborne LiDAR systems are crucial for Earth observation but face hardware constraints, thus limiting resolution and data processing. We propose integrating compressed sensing and diffusion generative models to reconstruct high-resolution satellite LiDAR data within the Hyperheight Data Cube (HHDC) framework. Using a randomized illumination pattern in the imaging model, we achieve efficient sampling and compression, reducing the onboard computational load and optimizing data transmission. Diffusion models then reconstruct detailed HHDCs from sparse samples on Earth. To ensure reliability despite lossy compression, we analyze distortion metrics for derived products like Digital Terrain and Canopy Height Models and evaluate the 3D reconstruction accuracy in waveform space. We identify image quality assessment metrics—ADD_GSIM, DSS, HaarPSI, PSIM, SSIM4, CVSSI, MCSD, and MDSI—that strongly correlate with subjective quality in reconstructed forest landscapes. This work advances high-resolution Earth observation by combining efficient data handling with insights into LiDAR imaging fidelity. Full article

A spaceborne microwave radiometer has a low spatial resolution limited by its antenna size. Enhancing the spatial resolution of data acquired by such sensors can improve the quality of subsequent applications. To improve the spatial resolution of the Microwave Radiation Imager (MWRI) onboard the Fengyun 3D satellite, this study used a Scatterometer Image Reconstruction (SIR) algorithm to generate resolution-enhanced swath brightness temperature data based on redundant information from overlaps between scanning points. These swath data have a higher pixel resolution that can reach 1/4 of the sampling frequency. The quality of reconstructed images, evaluated through visual comparison and quantitative analysis, revealed reasonable potential for providing more detailed depictions of surface information. Statistical analysis revealed a lower root mean square deviation of 0.8 K and a bias of 0.04 K following the SIR process. Analysis of the pixel spatial response function confirmed that the enhanced data have substantially finer spatial resolution than that of Level-1 data for 10–89 GHz vertical/horizontal channels, with an improvement of 9–39% in effective resolution. The findings of this study show that the SIR algorithm has potential for enhancing the quality of MWRI data and for widening the application domain to satellite product development, satellite data assimilation for numerical weather prediction, and other related fields. Full article

Accurate, scalable forestry insights are critical for implementing carbon credit-based reforestation initiatives and data-driven ecosystem management. However, existing forest quantification methods face significant challenges: hand measurement is labor-intensive, time-consuming, and difficult to trust; satellite imagery is not accurate enough; and airborne LiDAR remains prohibitively expensive at scale. In this work, we introduce ForestSplat: an accurate and scalable reforestation monitoring, reporting, and verification (MRV) system built from consumer-grade drone footage and 3D Gaussian Splatting. To evaluate the performance of our approach, we map and reconstruct a 200-acre mangrove restoration project in the Jobos Bay National Estuarine Research Reserve. ForestSplat produces an average mean absolute error (MAE) of 0.17 m and mean error (ME) of 0.007 m compared to canopy height maps derived from airborne LiDAR scans, using 100× cheaper hardware. We hope that our proposed framework can support the advancement of accurate and scalable forestry modeling with consumer-grade drones and computer vision, facilitating a new gold standard for reforestation MRV. Full article

The development of low earth orbit (LEO) satellites has provided the possibility to improve the accuracy of wide-area global navigation satellite system (GNSS) tomography; however, the existing LEO constellations were not designed to consider the effect on the improvement of the accuracy of GNSS computerized ionospheric tomography (CIT). In this paper, we use simulated observations to reconstruct a global higher-resolution three-dimensional (3D) ionospheric electron density (IED) model with a voxel-based model to explore the combined effects of heterogeneous LEO constellations on CIT. The results are as follows: (1) The number of grids crossed by rays increases after adding LEO satellites at different altitudes, while in the altitude interval of 100–300 km, the gain percentage increases with the number of LEO satellites at all four altitudes (300 km, 500 km, 800 km, and 1000 km). (2) The root mean square (RMS) gain percentages are positive after adding LEO satellite observations at 300 km and 500 km altitudes. Whereas, after adding LEO satellite observations at 800 km and 1000 km altitude, the RMS gain percentages from 100–300 km are negative. (3) From the overall percentage gain, the percentage RMS gain of all six plans exceeds 25%, with planB (96/96/60/30 LEO satellites at 300/500/800/1000 km, respectively) having the smallest percentage RMS gain of 27.31% and planA (192/96/60/30 LEO satellites at 300/500/800/1000 km, respectively) having the largest percentage RMS gain of 32.42%. Considering the LEO satellite launch maintenance cost for the enhancement effect of heterogeneous LEO constellations on CIT, this paper demonstrates that planA can effectively improve the accuracy of the 3D IED model. Full article