Search Results (131)

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

As the planet most similar to Earth in the solar system, Mars holds an important role in exploring significant scientific problems, such as the evolution of the solar system and the origins of life. Research on Mars mainly rely on planetary remote sensing technology, among which thermal infrared remote sensing is of great studying significance. This technology enables the recording of Martian thermal radiation properties. However, the current spatial resolution of Mars thermal infrared remote sensing images remains relatively low, limiting the detection of fine-scale thermal anomalies and the generation of higher-precision surface compositional maps. While updating extraterrestrial exploration satellites can help enhancing the spatial resolution of thermal infrared images, this method entails high cost and long update cycles, making improvement difficult to conduct in the short term. To address this issue, this paper proposes a super-resolution reconstruction method for Mars thermal infrared remote sensing images integrating multi-source data. First, based on the principle of domain adaptation, we introduced a method using highly correlated visible light images as auxiliary to enhance the spatial resolution of thermal infrared images. Then, a multi-sources data integration method is designed to constrain the thermal radiation flux of resulting images, ensuring the radiation distribution remains consistent with the original low-resolution thermal infrared images. Through both subjective and objective evaluations, our method is demonstrated to significantly enhance the spatial resolution of existing Mars thermal infrared images. It optimizes the quality of existing data, increasing the resolution of the original thermal infrared images by four times. In doing so, it not only recovers finer texture details to produce better visual effects than typical super-resolution methods, but also maintains the consistency of thermal radiation flux, with the error after applying the consistency constraint reduced by nearly tenfold, ensuring the applicability of the results for scientific research. Full article

The $\mathsf{\mu }$PPET [mu($\mathsf{\mu }$)on Probe with J-PET] project aims to investigate the “Muon Puzzle” seen in cosmic ray air showers. This puzzle arises from the observation of a significantly larger number of muons on Earth’s surface than that predicted by the current theoretical models. The investigated hypothesis is based on recently observed asymmetries in the parameters for the strong interaction cross-section and trajectory of an outgoing particle due to projectile–target polarization. The measurements require detailed information about muons at the ground level, including their track and charge distributions. To achieve this, the two PET scanners developed at the Jagiellonian University in Krakow (Poland), the J-PET detectors, will be employed, taking advantage of their well-known resolution and convenient location for detecting muons that reach long depths in the atmosphere. One station will be used as a muon tracker, while the second will reconstruct the core of the air shower. In parallel, the existing hadronic interaction models will be modified and fine-tuned based on the experimental results. In this work, we present the conceptualization and preliminary designs of $\mathsf{\mu }$PPET. Full article

In this study, a rare 3.49-carat yellow diamond was analyzed to reconstruct the geological processes that led to its distinctive form. The diamond exhibits growth and dissolution features, indicating a complex history. To preserve the sample’s integrity, non-destructive analytical techniques—including VIS, UV–Vis–NIR, and IR spectroscopy—were employed. The yellow coloration of the diamond is attributed to the presence of N3 and N2 defects. Additionally, other defects such as N₃VH⁰ centers and platelets were detected; however, the latter do not contribute to the coloration. The observations of the etch pits and surface microreliefs suggest that the diamond underwent size reduction due to dissolution events, which also altered its crystal habit over time. The diamond’s initial mixed-habit morphology evolved into a more complex one through a series of growth and dissolution processes that began during mantle storage. Furthermore, the presence of brown surface stains indicates radiation damage, likely acquired during its residence in alluvial deposits at the Earth’s surface. Full article

With advancement in Unmanned Aerial Vehicle (UAV) photogrammetry, productivity in construction management can now be achieved with accuracy and is less labour-intensive. In the basement construction of buildings, prudent earthwork activities are often necessary, setting the basis of the building footprint. As such, monitoring earthwork volume estimation becomes important to avoid over- or under-cutting the earth. Conventional methods by means of land surveying are time-consuming, labour-intensive, and susceptible to varying degrees of accuracy. Moreover, earthwork sites often have multiple activities ongoing that increase the complexity of volume estimation through land surveying. This study explores the use of UAV photogrammetry to estimate earthwork excavation volume in a complex urban earthwork site in Singapore over time and discusses the feasibility, challenges and productivity enhancements of integrating the technology into the construction process. In this study, the earthwork site and controlled trials show that the models reconstructed with UAV photogrammetry data can produce volume measurements that fulfil the stakeholder’s accuracy tolerance of 5% between the estimated and actual volume. The filtering of unwanted objects in the model, such as columns, cranes and trucks, was successful but was insufficient for objects that occluded large areas of the soil surface. The integration of UAV photogrammetry with a highly automated acquisition and processing workflow for earthwork monitoring brings about productivity enhancements in time and labour efforts and improves the efficiency and consistency of models. Furthermore, the digitalisation of earthwork sites into point clouds and three-dimensional (3D) models increases data visualisation and accessibility, facilitates project team collaboration, and enables cross-platform compatibility into Building Information Modelling (BIM), which can significantly aid in reporting and decision-making processes. Full article

The solar radiation pressure (SRP) model, as a key factor affecting the precise orbit determination (POD) accuracy of navigation satellites, is related to the state and optical properties of the satellite surface. This study establishes a high-precision SRP model for BDS-3 medium earth orbit (MEO) satellites manufactured by the China Academy of Space Technology based on the satellite engineering parameters, which comprises the satellites’ size and optical properties measured before launch. Then, the physical-based SRP model is re-constructed into the body-fixed coordinate as the function of the Sun elongation angle. The use of the hybrid SRP model, combining the reconstructed SRP model and the 5-parameter ECOM, results in a better POD performance. The orbit results, validated using satellite laser ranging (SLR) observations, show that the radial precision of approximately 3–4 cm can be achieved, with a reduction of the bias by up to 38% and a removal of the systematic error related to the Sun elongation angle in SLR residuals. Considering the possible degradation of the reconstructed SRP model with the engineering parameters, the evolution of SRP accelerations along with orbit quality based on a time series from over 5 years was studied. The results indicate that a variation of the total SRP acceleration for the BDS-3 satellites is minor and there is no apparent degradation in validations of 2019–2023, which proved the reliability and usability of the proposed SRP model for the BDS-3 MEO satellites. Full article

Remote sensing (RS) spectral time series provide a substantial source of information for the regular and cost-efficient monitoring of the Earth’s surface. Important monitoring tasks include land use and land cover classification, change detection, forest monitoring and crop type identification, among others. To develop accurate solutions for RS-based applications, often supervised shallow/deep learning algorithms are used. However, such approaches usually require fixed-length inputs and large labeled datasets. Unfortunately, RS images acquired by optical sensors are frequently degraded by aerosol contamination, clouds and cloud shadows, resulting in missing observations and irregular observation patterns. To address these issues, efforts have been made to implement frameworks that generate meaningful representations from the irregularly sampled data streams and alleviate the deficiencies of the data sources and supervised algorithms. Here, we propose a conceptually and computationally simple representation learning (RL) approach based on autoencoders (AEs) to generate discriminative features for crop type classification. The proposed methodology includes a set of single-layer AEs with a very limited number of neurons, each one trained with the mono-temporal spectral features of a small set of samples belonging to a class, resulting in a model capable of processing very large areas in a short computational time. Importantly, the developed approach remains flexible with respect to the availability of clear temporal observations. The signal derived from the ensemble of AEs is the reconstruction difference vector between input samples and their corresponding estimations, which are averaged over all cloud-/shadow-free temporal observations of a pixel location. This averaged reconstruction difference vector is the base for the representations and the subsequent classification. Experimental results show that the proposed extremely light-weight architecture indeed generates separable features for competitive performances in crop type classification, as distance metrics scores achieved with the derived representations significantly outperform those obtained with the initial data. Conventional classification models were trained and tested with representations generated from a widely used Sentinel-2 multi-spectral multi-temporal dataset, BreizhCrops. Our method achieved $77.06\%$ overall accuracy, which is $\sim 6\%$ higher than that achieved using original Sentinel-2 data within conventional classifiers and even $\sim 4\%$ better than complex deep models such as OmnisCNN. Compared to extremely complex and time-consuming models such as Transformer and long short-term memory (LSTM), only a $3\%$ reduction in overall accuracy was noted. Our method uses only $6.8k$ parameters, i.e., $\sim 400x$ fewer than OmnicsCNN and $\sim 27x$ fewer than Transformer. The results prove that our method is competitive in terms of classification performance compared with state-of-the-art methods while substantially reducing the computational load. Full article

Variable aperture sampling reconstruction matrices have a history of being computationally intensive due to the need to compute a full matrix inverse. In the field of remote sensing, several spaceborne radiometers and scatterometers, which have irregular sampling and variable apertures, use iterative techniques to reconstruct measurements of the Earth’s surface. However, many of these iterative techniques tend to over-amplify noise features outside the reconstructable bandwidth. Because the reconstruction of discrete samples is inherently bandlimited, solving a bandlimited inverse can focus on recovering signal features and prevent the over-amplification of noise outside the signal bandwidth. To approximate a bandlimited inverse, we apply bandlimited constraints to several well-known iterative reconstruction techniques: Landweber iteration, additive reconstruction technique (ART), Richardson–Lucy iteration, and conjugate gradient descent. In the context of these iterative techniques, we derive an iterative method for inverting variable aperture samples, taking advantage of the regular and irregular content of variable apertures. We find that this iterative method for variable aperture reconstruction is equivalent to solving a bandlimited conjugate gradient descent algorithm. Full article

We analyzed the main colorimetric characteristics of lights on Mars’ surface from 3139 total spectral irradiances provided by the COMIMART model (J. Space Weather Space Clim. 5, A33, 2015), modifying the parameters of ‘solar zenith angle’ and ‘opacity’, related to the time of day and the amount of dust in the atmosphere of Mars, respectively. Lights on Mars’ surface have chromaticities that are mainly located below the Planckian locus, correlated color temperature in the range of 2333 K–5868 K, and CIE 2017 color fidelity indices above 93. For the 24 samples in the X-Rite ColorChecker^® and an extreme dust opacity change from 0.1 to 8.1 in the atmosphere, the average color inconstancy generated by the change in Mars’ light using the chromatic adaptation transform CIECAT16 was about 5 and 8 CIELAB units for solar zenith angles of 0° and 72°, respectively. We propose a method to compute total spectral irradiances on the surface of Mars from a selected value of correlated color temperature in the range of 2333 K–5868 K. This method is analogous to the one currently adopted by the International Commission on Illumination to compute daylight illuminants on the surface of Earth (CIE 015:2018, clause 4.1.2). The average accuracy of 3139 reconstructed total spectral irradiances using the proposed method was 0.9999558 using GFC (J. Opt. Soc. Am. A 14, 1007–1014, 1997) and 0.0009 ΔE_u^′v^′ units, a value just below noticeable chromaticity differences perceptible by human observers at 50% probability. Total spectral irradiances proposed by Barnes for five correlated temperatures agreed with those obtained from the current proposed method: on the average, GFC = 0.9979521 and 0.0023 ΔE_^′v^′ units. Full article

In this paper, we propose a 3D Digital Surface Model (DSM) reconstruction method from uncalibrated Multi-view Satellite Stereo (MVSS) images, where Rational Polynomial Coefficient (RPC) sensor parameters are not available. While recent investigations have introduced several techniques to reconstruct high-precision and high-density DSMs from MVSS images, they inherently depend on the use of geo-corrected RPC sensor parameters. However, RPC parameters from satellite sensors are subject to being erroneous due to inaccurate sensor data. In addition, due to the increasing data availability from the internet, uncalibrated satellite images can be easily obtained without RPC parameters. This study proposes a novel method to reconstruct a 3D DSM from uncalibrated MVSS images by estimating and integrating RPC parameters. To do this, we first employ a structure from motion (SfM) and 3D homography-based geo-referencing method to reconstruct an initial DSM. Second, we sample 3D points from the initial DSM as references and reproject them to the 2D image space to determine 3D–2D correspondences. Using the correspondences, we directly calculate all RPC parameters. To overcome the memory shortage problem while running the large size of satellite images, we also propose an RPC integration method. Image space is partitioned to multiple tiles, and RPC estimation is performed independently in each tile. Then, all tiles’ RPCs are integrated into the final RPC to represent the geometry of the whole image space. Finally, the integrated RPC is used to run a true MVSS pipeline to obtain the 3D DSM. The experimental results show that the proposed method can achieve 1.455 m Mean Absolute Error (MAE) in the height map reconstruction from multi-view satellite benchmark datasets. We also show that the proposed method can be used to reconstruct a geo-referenced 3D DSM from uncalibrated and freely available Google Earth imagery. Full article

Archaeological bone chemical composition is modified post-mortem by diagenesis processes, and over decades, several authors have proposed different protocols to avoid post-depositional contamination that can carry to misleading interpretations about the lifestyle and origin of ancient populations. In this work, a methodological approach based on rare earth elements analysis was developed to determine diagenetic alterations on femurs, humeri, and skull surfaces, and internal layers from thirteen individuals exhumed during fieldwork in the Fatih Sultan Mehmet Mosque at Rozafa Castle (Shkodër, Albania). Major, minor, and trace elements, including rare earth elements, were measured employing spectrometric techniques, and the obtained data were statistically processed by principal component analysis and one-way ANOVA to select the best preserved bones. The results show that in general, the internal parts of bones, especially skulls, suffered post-depositional chemical contamination. Finally, to show the effectiveness of the proposed approach, a diet reconstruction employing log(Sr/Ca) and Zn/Ca was tested, obtaining results that are in line with the literature describing a diet based on a mixed economy, mostly agricultural products with low protein intakes. Full article

Satellite multi-view stereo (MVS) is a fundamental task in large-scale Earth surface reconstruction. Recently, learning-based multi-view stereo methods have shown promising results in this field. However, these methods are mainly developed by transferring the general learning-based MVS framework to satellite imagery, which lacks consideration of the specific terrain features of the Earth’s surface and results in inadequate accuracy. In addition, mainstream learning-based methods mainly use equal height interval partition, which insufficiently utilizes the height hypothesis surface, resulting in inaccurate height estimation. To address these challenges, we propose an end-to-end terrain feature-aware height estimation network named SA-SatMVS for large-scale Earth surface multi-view stereo, which integrates information across different scales. Firstly, we transform the Sobel operator into slope feature-aware kernels to extract terrain features, and a dual encoder–decoder architecture with residual blocks is applied to incorporate slope information and geometric structural characteristics to guide the reconstruction process. Secondly, we introduce a pixel-wise unequal interval partition method using a Laplacian distribution based on the probability volume obtained from other scales, resulting in more accurate height hypotheses for height estimation. Thirdly, we apply an adaptive spatial feature extraction network to search for the optimal fusion method for feature maps at different scales. Extensive experiments on the WHU-TLC dataset also demonstrate that our proposed model achieves the best MAE metric of 1.875 and an RMSE metric of 3.785, which constitutes a state-of-the-art performance. Full article

The atmosphere is a complex nonlinear system, with the information of its temperature, water vapor, pressure, and cloud being crucial aspects of remote-sensing data analysis. There exist intricate interactions among these internal components, such as convection, radiation, and humidity exchange. Atmospheric phenomena span multiple spatial and temporal scales, from small-scale thunderstorms to large-scale events like El Niño. The dynamic interactions across different scales, along with external disturbances to the atmospheric system, such as variations in solar radiation and Earth surface conditions, contribute to the chaotic nature of the atmosphere, making long-term predictions challenging. Grasping the intrinsic chaotic dynamics is essential for advancing atmospheric analysis, which holds profound implications for enhancing meteorological forecasts, mitigating disaster risks, and safeguarding ecological systems. To validate the chaotic nature of the atmosphere, this paper reviewed the definitions and main features of chaotic systems, elucidated the method of phase space reconstruction centered on Takens’ theorem, and categorized the qualitative and quantitative methods for determining the chaotic nature of time series data. Among quantitative methods, the Wolf method is used to calculate the Largest Lyapunov Exponents, while the G–P method is used to calculate the correlation dimensions. A new method named Improved Saturated Correlation Dimension method was proposed to address the subjectivity and noise sensitivity inherent in the traditional G–P method. Subsequently, the Largest Lyapunov Exponents and saturated correlation dimensions were utilized to conduct a quantitative analysis of FY-4A and Himawari-8 remote-sensing infrared observation data, and ERA5 reanalysis data. For both short-term remote-sensing data and long-term reanalysis data, the results showed that more than 99.91% of the regional points have corresponding sequences with positive Largest Lyapunov exponents and all the regional points have correlation dimensions that tended to saturate at values greater than 1 with increasing embedding dimensions, thereby proving that the atmospheric system exhibits chaotic properties on both short and long temporal scales, with extreme sensitivity to initial conditions. This conclusion provided a theoretical foundation for the short-term prediction of atmospheric infrared radiation field variables and the detection of weak, time-sensitive signals in complex atmospheric environments. Full article

Land surface temperature (LST) has a wide application in Earth Science-related fields, and spatial downscaling is an important method to retrieve high-resolution LST data. However, existing LST downscaling methods have difficulties in simultaneously constructing and expressing spatial non-stationarity, spatial autocorrelation, and complex non-linearity during the LST downscaling process, which limits the performance of the models. Moreover, there is a lack of research on high-resolution nighttime land surface temperature (NLST) reconstruction based on spatial downscaling, which does not meet the data needs for urban-scale nighttime urban heat island (UHI) studies. Therefore, this study combined Geographically Neural Network Weighted Regression (GNNWR) with Area-to-Point Kriging interpolation (ATPK) to propose a Geographically Neural Network Weighted Regression Kriging (GNNWRK) model for NLST downscaling. To verify the model’s generality and robustness, this study selected four study areas with different landform and climate type for NLST spatial downscaling experiments. The GNNWRK was compared with four benchmark downscaling methods, including TsHARP, Random Forest, Geographically Weighted Regression, and GNNWR. The results show that compared to these four benchmark methods, the GNNWRK method has higher accuracy in NLST downscaling, with a maximum Pearson’s Correlation Coefficient (Pcc) of 0.930 and a minimum Root Mean Square Error (RMSE) of 0.886 K. Moreover, the validation based on MODIS NLST data and ground-measured NLST data also indicates that the GNNWRK model can obtain more accurate, high-resolution NLST with richer and more detailed texture. This enhances the potential of NLST in studying the effects of urban nighttime heat islands at a finer scale. Full article