Search Results (164)

Due to the tidal locking, the far side of the Moon is permanently turned away from the Earth. Its polarization characteristics are still poorly understood, limiting our knowledge of material composition and evolution. Previous studies have indicated a correlation between the distributions of degree of polarization (DOP) and the iron oxide (FeO) abundance on the Moon, suggesting a new approach to infer the polarization characteristics of the lunar far side from FeO abundance distribution. Three critical issues have been analyzed: (1) A linear regression model between DOP and FeO abundance is proposed based on control points from ground-based near side polarization images. (2) The DOP distribution of the lunar far side is estimated, based on the established model, revealing significant hemispheric differences in polarization characteristics. (3) The relationship between DOP and lunar phase angle is examined, with the fitted values demonstrating strong agreement with the observations in both magnitude and variation trend. These insights offer valuable guidance for comprehensive polarimetric studies of the Moon. Full article

Lunar digital elevation models (DEMs) are a fundamental data source for lunar research and exploration. However, high-resolution DEM products for the Moon are only available in some local areas, which makes it difficult to meet the needs of scientific research and missions. To this end, we have previously developed a deep learning-based method (LDEMGAN1.0) for single-image lunar DEM reconstruction. To address issues such as loss of detail in LDEMGAN1.0, this study leverages the inherent structural self-similarity of different DEM data from the same lunar terrain and proposes an improved version, named LDEMGAN2.0. During the training process, the model computes the self-similarity graph (SSG) between the outputs of the LDEMGAN2.0 generator and the ground truth, and incorporates the self-similarity loss (SSL) constraint into the network generator loss to guide DEM reconstruction. This improves the network’s capacity to capture both local and global terrain structures. Using the LROC NAC DTM product (2 m/pixel) as the ground truth, experiments were conducted in the Apollo 11 landing area. The proposed LDEMGAN2.0 achieved mean absolute error (MAE) of 1.49 m, root mean square error (RMSE) of 2.01 m, and structural similarity index measure (SSIM) of 0.86, which is 46.0%, 33.4%, and 11.6% higher than that of LDEMGAN1.0. Both qualitative and quantitative evaluations demonstrate that LDEMGAN2.0 enhances detail recovery and reduces reconstruction artifacts. Full article

Deep space exploration missions face technical challenges such as long-distance communication delays and high-precision autonomous positioning. Traditional ground-based telemetry and control as well as inertial navigation schemes struggle to meet mission requirements in the complex environment of deep space. As a vision-based autonomous navigation technology, image-based navigation enables spacecraft to obtain real-time images of the target celestial body surface through a variety of onboard remote sensing devices, and it achieves high-precision positioning using stable terrain features, demonstrating good autonomy and adaptability. Craters, due to their stable geometry and wide distribution, serve as one of the most important terrain features in deep space image-based navigation and have been widely adopted in practical missions. This paper systematically reviews the research progress of deep space image-based navigation technology, with a focus on the main sources of remote sensing data and a comprehensive summary of its typical applications in lunar, Martian, and asteroid exploration missions. Focusing on key technologies in image-based navigation, this paper analyzes core methods such as surface feature detection, including the accurate identification and localization of craters as critical terrain features in deep space exploration. On this basis, the paper further discusses possible future directions of image-based navigation technology in response to key challenges such as the scarcity of remote sensing data, limited computing resources, and environmental noise in deep space, including the intelligent evolution of image navigation systems, enhanced perception robustness in complex environments, hardware evolution of autonomous navigation systems, and cross-mission adaptability and multi-body generalization, providing a reference for subsequent research and engineering practice. Full article

Lunar orbiter image matching is a critical process for achieving high-precision lunar mapping, positioning, and navigation. However, with the Moon’s weak-texture surface and rugged terrain, lunar orbiter images generally suffer from inconsistent lighting conditions and exhibit varying degrees of non-linear intensity distortion, which pose significant challenges to image traditional matching. This paper presents a robust feature matching method based on crater neighborhood structure, which is particularly robust to changes in illumination. The method integrates deep-learning based crater detection, Crater Neighborhood Structure features (CNSFs) construction, CNSF similarity-based matching, and outlier removal. To evaluate the effectiveness of the proposed method, we created an evaluation dataset, comprising Multi-illumination Lunar Orbiter Images (MiLOIs) from different latitudes (a total of 321 image pairs). And comparative experiments have been conducted using the proposed method and state-of-the-art image matching methods. The experimental results indicate that the proposed approach exhibits greater robustness and accuracy against variations in illumination. Full article

The determination of the dynamic angle of repose (DAR) of lunar regolith simulants is essential for modeling material behavior during in situ resource utilization (ISRU) processes and lunar surface operations. This study presents a methodology and dedicated test rig employing digital image processing to measure DAR for seven lunar regolith simulants, representing both Mare and Highland regions. Experiments were conducted under terrestrial gravity at rotational drum speeds of 2, 5, and 10 RPM, with standardized material fill and image capture procedures. For each simulant, lower, higher, and total DAR values were recorded, indicating complex dependencies on particle size distribution, mineralogy, and rotational speed. These measurements provide a critical dataset for numerical model calibration and the simulation of regolith handling systems under lunar conditions. The findings emphasize the necessity of selecting appropriate DAR parameters based on regolith type and operational scale to ensure accurate predictions of granular flow behavior in extraterrestrial environments. Full article

Accurate localization is a key requirement for deep-space exploration, enabling spacecraft operations with limited ground support. Upcoming commercial and scientific missions to the Moon are designed to extensively use optical measurements during low-altitude orbital phases, descent and landing, and high-risk operations, due to the versatility and suitability of these data for onboard processing. Navigation frameworks based on optical data analysis have been developed to support semi- or fully-autonomous onboard systems, enabling precise relative localization. To achieve high-accuracy navigation, optical data have been combined with complementary measurements using sensor fusion techniques. Absolute localization is further supported by integrating onboard maps of cataloged surface features, enabling position estimation in an inertial reference frame. This study presents a navigation framework for optical image processing aimed at supporting the autonomous operations of lunar orbiters. The primary objective is a comprehensive characterization of the navigation camera’s properties and performance to ensure orbit determination uncertainties remain below 1% of the spacecraft altitude. In addition to an analysis of measurement noise, which accounts for both hardware and software contributions and is evaluated across multiple levels consistent with prior literature, this study emphasizes the impact of process noise on orbit determination accuracy. The mismodeling of orbital dynamics significantly degrades orbit estimation performance, even in scenarios involving high-performing navigation cameras. To evaluate the trade-off between measurement and process noise, representing the relative accuracy of the navigation camera and the onboard orbit propagator, numerical simulations were carried out in a synthetic lunar environment using a near-polar, low-altitude orbital configuration. Under nominal conditions, the optical measurement noise was set to 2.5 px, corresponding to a ground resolution of approximately 160 m based on the focal length, pixel pitch, and altitude of the modeled camera. With a conservative process noise model, position errors of about 200 m are observed in both transverse and normal directions. The results demonstrate the estimation framework’s robustness to modeling uncertainties, adaptability to varying measurement conditions, and potential to support increased onboard autonomy for small spacecraft in deep-space missions. Full article

Accurate and robust visual localization is essential for autonomous lunar landing. This study presents a new crater-based method that addresses challenges posed by environmental uncertainties such as camera pose deviations, the number of craters within the scene, and the image brightness. Our method combines crater Geohash encoding for efficient database retrieval with an improved principal component analysis (PCA) for crater detection. The detected craters are ranked, retaining those with fewer but more accurate detections to meet localization requirements. Crucially, we introduce a consistency matching technique that exploits the linear relationship between position shifts and pixel offsets, enhancing both localization accuracy and computational efficiency. Experimental results across diverse scenes and simulation conditions demonstrate 100% matching accuracy with an average matching time under 0.8 s. Reprojection errors remain below 3 px, significantly outperforming methods like triangle similarity matching (TSM) and direct matching (DM). This validates the proposed method’s high precision and stability for near real-time lunar localization. Full article

Lunar swirls are enigmatic features on the Moon’s surface, and their formation remains debated. Previous studies suggest that the distinctive spectral characteristics of lunar swirls result from the asymmetric space weathering between their bright markings (on-swirl) and dark surrounding background (off-swirl) regions. Nanophase iron (npFe⁰), as the product of space weathering, directly reflects this varying degree of space weathering. In this study, we investigated the formation of lunar swirls from the perspective of the npFe⁰ distribution across five lunar swirls using Chang’e-1 (CE-1) Interference Imaging Spectrometer (IIM) data. Our results show that (1) on-swirl regions exhibit an obvious lower npFe⁰ abundance compared to their backgrounds; (2) the relationship between the npFe⁰ abundance in swirl dark lanes and the off-swirl regions is associated with different stages of space weathering; (3) the difference in the npFe⁰ abundance between on-swirl regions and off-swirl fresh craters could be due to their different weathering processes; and (4) there is a correlation between npFe⁰, water content, and the strength of magnetic anomalies related to lunar swirls. These findings support the view that the process of solar wind deflection leads to the preservation of swirl surfaces with reduced space weathering and provide a new perspective for comparing different swirl formation models. Full article

This study examines the mineral composition of volcanic samples similar to lunar materials, focusing on olivine and pyroxene. Using hyperspectral imaging (HSI) from 400 to 1000 nm, we created data cubes to analyze the reflectance characteristics of samples from Vulcano, a volcanically active island in the Aeolian archipelago, north of Sicily, Italy, categorizing them into nine regions of interest (ROIs) and analyzing spectral data for each. We applied various unsupervised clustering algorithms, including K-Means, hierarchical clustering, Gaussian mixture models (GMMs), and spectral clustering, to classify the spectral profiles. Principal component analysis (PCA) revealed distinct spectral signatures associated with specific minerals, facilitating precise identification. The clustering performance varied by region, with K-Means achieving the highest silhouette score of 0.47, whereas GMMs performed poorly with a score of only 0.25. Non-negative matrix factorization (NMF) aided in identifying similarities among clusters across different methods and reference spectra for olivine and pyroxene. Hierarchical clustering emerged as the most reliable technique, achieving a 94% similarity with the olivine spectrum in one sample, whereas GMMs exhibited notable variability. Overall, the analysis indicated that both the hierarchical and K-Means methods yielded lower errors in total measurements, with K-Means demonstrating superior performance in estimated dispersion and clustering. Additionally, GMMs showed a higher root mean square error (RMSE) compared to the other models. The RMSE analysis confirmed K-Means as the most consistent algorithm across all samples, suggesting a predominance of olivine in the Vulcano region relative to pyroxene. This predominance is likely linked to historical formation conditions similar to volcanic processes on the Moon, where olivine-rich compositions are common in ancient lava flows and impact-melt rocks. These findings provide a deeper context for mineral distribution and formation processes in volcanic landscapes. Full article

The Chinese Chang’E-7 (CE-7) mission is planned to land in the lunar south polar region, and then deploy a mini-flying probe to fly into the cold trap to detect the water ice. The selection of a landing site is crucial for ensuring both a safe landing and the successful achievement of its scientific objectives. This study presents a method for landing site selection in the challenging environment of the lunar south pole, utilizing multi-source remote sensing data. First, the likelihood of water ice in all cold traps within 85°S is assessed and prioritized using neutron spectrometer and hyperspectral data, with the most promising cold traps selected for sampling by CE-7’s mini-flying probe. Slope and illumination data are then used to screen feasible landing sites in the south polar region. Feasible landing sites near cold traps are aggregated into larger landing regions. Finally, high-resolution illumination maps, along with optical and radar images, are employed to refine the selection and identify the optimal landing sites. Six potential landing sites around the de Gerlache crater, an unnamed cold trap at (167.10°E, 88.71°S), Faustini crater, and Shackleton crater are proposed. It would be beneficial for CE-7 to prioritize mapping these sites post-launch using its high-resolution optical camera and radar for further detailed landing site investigation and evaluation. Full article

We propose a Multispectral Earth Polarization Imager (MEPI), which is located at the Earth–Moon system’s Lagrange point L1. The imager can be used to measure the sunlight reflected by the Earth and the Moon. The measured sunlight has specific polarization information and spectral information, which can provide strong support for a comprehensive understanding of the Earth system and the construction of a perfect Earth–Moon system model. The MEPI provides multispectral images with wavelengths of 400–885 nm, and uses four sub-aperture systems to share a main system. The imager can capture the two-dimensional shape and polarization spectral information of the entire Earth at a spatial resolution of 10 km, and all spectral images can be simultaneously acquired on a single detector. The optical system of the instrument was designed and simulated. The simulation and analysis results showed that the camera can obtain high-quality images of the Earth disc with a 2.5° field of view (FOV). The novel MEPI provides a new way to generate climate-related knowledge from the perspective of global Earth observation. The imager can also be used for lunar observation to obtain spectral polarization information on the lunar surface. In addition, it also shows great potential in other applications of space remote sensing spectral imaging. Full article

The intensified charge-coupled device (ICCD), known for its exceptional low-light detection performance and time-gating capability, has been widely applied in remote Raman spectroscopy systems. However, existing ICCDs face significant challenges in meeting the comprehensive requirements of high gating speed, high sensitivity, high resolution, miniaturization, and adaptability to extreme environments for the upcoming lunar remote Raman spectroscopy missions. To address these challenges, this study developed a microstrip photocathode (MP-ICCD) specifically designed for lunar remote Raman spectroscopy. A comprehensive testing method was also proposed to evaluate critical performance parameters, including optical gating width, optimal gain voltage, and relative resolution. The MP-ICCD was integrated into a prototype remote Raman spectrometer equipped with a 40 mm aperture telescope and tested under outdoor sunlight conditions. The experimental results demonstrated that the developed MP-ICCD successfully achieved a minimum optical gating width of 6.0 ns and an optimal gain voltage of 870 V, with resolution meeting the requirements for Raman spectroscopy detection. Under outdoor solar illumination, the prototype remote Raman spectrometer utilizing the MP-ICCD accurately detected the Raman spectra of typical lunar minerals, including quartz, olivine, pyroxene, and plagioclase, at a distance of 1.5 m. This study provides essential technical support and experimental validation for the application of MP-ICCD in lunar Raman spectroscopy missions. Full article

Raman spectroscopy is capable of precisely identifying and analyzing the composition and properties of samples collected from the lunar surface, providing crucial data support for lunar scientific research. However, in situ Raman spectroscopy on the lunar surface faces challenges such as weak Raman scattering from targets, alongside requirements for lightweight and long-distance detection. To address these challenges, time-gated Raman spectroscopy (TG-LRS) based on a passively Q-switched pulsed laser and a linear intensified charge-coupled device (ICCD), which enable simultaneous signal amplification and background suppression, has been developed to evaluate the impact of key operational parameters on Raman signal detection and to explore miniaturization optimization. The TG-LRS system includes a 40 mm zoom telescope, a passively Q-switched 532 nm pulsed laser, a fiber optic delay line, a miniature spectrometer, and a linear ICCD detector. It achieves an electronic gating width under 20 ns. Within a detection range of 1.1–3.0 m, the optimal delay time varies linearly from 20 to 33 ns. Raman signal intensity increases with image intensifier gain, while the signal-to-noise ratio peaks at a gain range of 800–900 V before declining. Furthermore, the effects of focal depth, telescope aperture, laser energy, and integration time were studied. The Raman spectra of lunar minerals were successfully obtained in the lab, confirming the system’s ability to suppress solar background light. This demonstrates the feasibility of in situ Raman spectroscopy on the lunar surface and offers strong technical support for future missions. Full article

Advanced navigation capabilities are essential for precise landing operations, enabling access to critical lunar sites and supporting future lunar infrastructure. To achieve accurate positioning, innovative navigation methods leveraging neural network frameworks are being developed to detect distinctive lunar surface features, such as craters, from imaging data. By matching detected features with known landmarks stored in an onboard reference database, key navigation measurements are retrieved to refine the spacecraft trajectory, enabling real-time planning for hazard avoidance. This work presents a crater-based navigation system for planetary descent operations, which leverages a robust machine learning approach for crater detection in optical images. A thorough analysis of the attainable detection accuracies was performed by evaluating the network performance on diverse sets of synthetic images rendered at different illumination conditions through a custom Blender-based pipeline. Simulation campaigns, based on the JAXA Smart Lander for Investigating Moon mission, were then carried out to demonstrate the system’s performance, achieving final position errors consistent with 3 − $\sigma$ uncertainties lower than 100 m on the horizontal plane at altitudes as low as 10 km. This level of accuracy is key to achieving enhanced control during the approach and vertical descent phases, thereby ensuring operational safety and facilitating precise landing. Full article

Safe and reliable lunar landings are crucial for future exploration of the Moon. The regolith ejected by a lander’s rocket exhaust plume represents a significant obstacle in achieving this goal. It prevents spacecraft from reliably utilizing their navigation sensors to monitor their trajectory and spot emerging surface hazards as they near the surface. As part of NASA’s 2024 Human Lander Challenge (HuLC), the team at the University of Michigan developed an innovative concept to help mitigate this issue. We developed and implemented a machine learning (ML)-based sensor fusion system, ARC-LIGHT, that integrates sensor data from the cameras, lidars, or radars that landers already carry but disable during the final landing phase. Using these data streams, ARC-LIGHT will remove erroneous signals and recover a useful detection of the surface features to then be used by the spacecraft to correct its descent profile. It also offers a layer of redundancy for other key sensors, like inertial measurement units. The feasibility of this technology was validated through development of a prototype algorithm, which was trained on data from a purpose-built testbed that simulates imaging through a dusty environment. Based on these findings, a development timeline, risk analysis, and budget for ARC-LIGHT to be deployed on a lunar landing was created. Full article