Search Results (110)

Lunar crater detection plays a crucial role in geological analysis and the advancement of lunar exploration. Accurate identification of craters is also essential for constructing high-resolution topographic maps and supporting mission planning in future lunar exploration efforts. However, lunar craters often suffer from insufficient feature representation due to their small size and blurred boundaries. In addition, the visual similarity between craters and surrounding terrain further exacerbates background confusion. These challenges significantly hinder detection performance in remote sensing imagery and underscore the necessity of enhancing both local feature representation and global semantic reasoning. In this paper, we propose a novel Spatial Channel Fusion and Context-Aware YOLO (SCCA-YOLO) model built upon the YOLO11 framework. Specifically, the Context-Aware Module (CAM) employs a multi-branch dilated convolutional structure to enhance feature richness and expand the local receptive field, thereby strengthening the feature extraction capability. The Joint Spatial and Channel Fusion Module (SCFM) is utilized to fuse spatial and channel information to model the global relationships between craters and the background, effectively suppressing background noise and reinforcing feature discrimination. In addition, the improved Channel Attention Concatenation (CAC) strategy adaptively learns channel-wise importance weights during feature concatenation, further optimizing multi-scale semantic feature fusion and enhancing the model’s sensitivity to critical crater features. The proposed method is validated on a self-constructed Chang’e 6 dataset, covering the landing site and its surrounding areas. Experimental results demonstrate that our model achieves an $mA{P}_{0.5}$ of 96.5% and an $mA{P}_{0.5:0.95}$ of 81.5%, outperforming other mainstream detection models including the YOLO family of algorithms. These findings highlight the potential of SCCA-YOLO for high-precision lunar crater detection and provide valuable insights into future lunar surface analysis. Full article

This study investigates the spatial distribution of transition metals—iron (Fe), titanium (Ti), and chromium (Cr)—within the Dryden crater on the Moon using hyperspectral data from the Moon Mineralogy Mapper (M³). By applying spectral parameters and false color composite techniques, geospatial maps of chromite distribution and FeO, TiO₂ wt.% distribution were generated at a resolution of ~140 m. The findings reveal distinct elemental enrichments along geomorphologically active regions such as crater walls, terraces, and central peaks, highlighting impact-driven material differentiation, the influence of morphology, degradation, and space weathering. These results enhance our understanding of lunar crustal evolution and support future exploration and resource utilization efforts. 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

This study investigates the mineral composition of the lunar Dryden Crater using Moon Mineralogy Mapper (M³) data. A RGB false-color composite reveals distinct pyroxene, anorthosite, and possibly spinel distribution patterns. Orthopyroxenes, excavated from deep crustal layers, dominate steep slopes, while plagioclase-rich materials align with magma ocean models of lunar crustal formation. Minor clinopyroxenes indicate impact melt origins. While space weathering and shock metamorphism pose analytical challenges, integrating spectral data with geological context elucidates the crater’s complex history. The resulting mineral distribution map supports targeted exploration during upcoming lunar missions, resource prospecting and resource utilization initiatives within this geologically complex region. Full article

The study of impact craters is crucial for understanding planetary evolution and geological processes, particularly small craters, which are key to reconstructing the lunar impact history. Detecting small craters, with diameters ranging from 0.2 to 2 km, remains a challenge due to the power-law distribution of crater sizes and the complex topography of the lunar surface. This work uses high-resolution lunar imagery data from the Chang’E-2 mission, with a 7 m spatial resolution, to develop a deep learning framework for small crater detection, named YOLO-SCNet. The framework combines a high-quality, diversified sample dataset, generated through data augmentation techniques, with YOLO-SCNet, specifically designed for small target detection. Key challenges in lunar crater detection, such as varying lighting conditions and complex terrains, are addressed through the innovative model architecture, which incorporates a small object detection head, dynamic anchor boxes, and multi-scale feature fusion. Experimental results demonstrate that YOLO-SCNet achieves outstanding performance in detecting small craters across different lunar regions, with precision, recall, and F1 scores of 90.2%, 88.7%, and 89.4%, respectively. The framework offers a scalable solution for constructing a global lunar crater catalog (≥0.2 km) and can be extended to other planetary bodies like Mars and Mercury, significantly supporting future planetary exploration and mapping efforts. Full article

We conducted a detailed study using multi-source data to date the mare activity and lobate scarp formation within the Jules Verne crater on the Moon. In previous studies, the Jules Verne crater has been classified as a pre-Nectarian impact crater. Our analysis indicates that it has an absolute model age (AMA) of ${4.21}_{-0.034}^{+0.032}$ Ga. After its formation, a magmatic intrusion event created floor fractures, followed by two basaltic eruption events—one at 3.4 Ga and another at 2.6 Ga. Subsequently, around 1.4 billion years ago, lunar seismic activity likely took place in this region, resetting the surface ages of the crater floor fractures and surrounding areas, as evidenced by the scarp. Full article

The permanently shadowed regions of the lunar South Pole have become a key target for international lunar exploration due to their unique scientific value and engineering challenges. In order to effectively screen suitable landing zones near the lunar South Pole, this research proposes a comprehensive evaluation method based on a self-organizing map (SOM). Using multi-source remote sensing data, the method classifies and analyzes candidate landing zones by combining scientific purposes (such as hydrogen abundance, iron oxide abundance, gravity anomalies, water ice distance analysis, and geological features) and engineering constraints (such as Sun visibility, Earth visibility, slope, and roughness). Through automatic clustering, the SOM model finds the important regions. Subsequently, it integrates with a supervised learning model, a random forest, to determine the feature importance weights in more detail. The results from the research indicate the following: the areas suitable for landing account for 9.05%, 5.95%, and 5.08% in the engineering, scientific, and synthesized perspectives, respectively. In the weighting analysis of the comprehensive data, the weights of Earth visibility, hydrogen abundance, kilometer-scale roughness, and slope data all account for more than 10%, and these are thought to be the four most important factors in the automated site selection process. Furthermore, the kilometer-scale roughness data are more important in the comprehensive weighting, which is in line with the finding that the kilometer-scale roughness data represent both surface roughness from an engineering perspective and bedrock geology from a scientific one. In this study, a local examination of typical impact craters is performed, and it is confirmed that all 10 possible landing sites suggested by earlier authors are within the appropriate landing range. The findings demonstrate that the SOM-model-based analysis approach can successfully assess lunar South Pole landing areas while taking multiple constraints into account, uncovering spatial distribution features of the region, and offering a rationale for choosing desired landing locations. 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

The study of secondary craters on the Moon is vital for understanding lunar impact dynamics and surface evolution. However, this task is complicated by sample imbalance, with primary crater samples outnumbering those of secondary craters, and by the reliance on time-intensive manual methods or limited automated techniques. While many previous studies have focused on the manual or automated differentiation of secondary craters, few have addressed the interpretation of variables and models. In this study, we propose a machine-learning-based approach using the CatBoost algorithm to classify craters based on variables extracted from Digital Elevation Model (DEM) data. These variables include those from previous research as well as new ones introduced here, such as slope and density with Gaussian summation. Despite data imbalance and noise, the model achieves a classification accuracy of 0.8788, with a precision of 0.7922, a recall rate of 0.7412, and an F1 score of 0.7658 for secondary craters. To enhance interpretations, Shapley additive explanations (SHAP) and partial dependence plots (PDPs) are applied to evaluate variable importance and visualize the marginal effects of key variables, indicating the density variables playing a key role in crater classification. 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

This study investigates the detectability of a putative layer of regolith containing water ice in the lunar polar regions using ground penetrating radar (GPR). Numerical simulations include realistic variations in the relative permittivity of the lunar regolith, considering both density and, for the first time, the effects of temperature on permittivity profiles. We follow the case of previous theoretical studies of water migration, which suggest that water ice accumulates at depths ranging from a few centimeters to tens of centimeters, appropriate depths to explore using GPR. In particular, frequency-modulated continuous wave (FMCW) radar is well-suited for this purpose due to its high range resolution and robust signal-to-noise ratio. This study evaluates two scenarios for the presence of lunar water ice: (1) a layer of regolith containing water ice at a depth of 5 cm, with a thickness of 5 cm, and (2) a layer of regolith containing water ice at a depth of 20 cm, with a thickness of 10 cm. Our computational results show that FMCW GPR, equipped with a dynamic range of 90 dB, is capable of detecting reflections from the interfaces of these layers, even under conditions of low water ice content and using antennas with low directivity. In addition, optimized antenna offsets improve the resolution of the upper and lower interfaces, particularly when applied to the surface of ancient crater ejecta. This study highlights the critical importance of understanding subsurface density and temperature structures for the accurate detection of water-ice-bearing regolith layers. Full article

Lunar-rover path planning is a key topic in lunar exploration research, with safety and computational efficiency critical for achieving long-distance planning. This paper proposes a distributed path-planning method that considers multiple lunar environmental factors, addressing the issues of inadequate safety considerations and low computational efficiency in current research. First, a set of safety evaluation rules is constructed by considering factors such as terrain slope, roughness, illumination, and rock abundance. Second, a distributed path-planning strategy based on a safety-map tile pyramid (DPPS-STP) is proposed, using a weighted A* algorithm with hash table-based open and closed lists (OC-WHT-A*) on a Spark cluster for efficient and safer path planning. Additionally, high-resolution digital orthophoto maps (DOM) are utilized for small crater detection, enabling more refined path planning built upon the overall mission-planning result. The method was validated in four lunar regions with distinct characteristics. The results show that DPPS-STP, which considers multiple environmental factors, effectively reduces the number of hazardous nodes and avoids crater obstacles. For long-distance tasks, it achieves an average speedup of up to 11.5 times compared to the single-machine OC-WHT-A*, significantly improving computational efficiency. 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

SAR data from the lunar spaceborne Reconnaissance Orbiter’s (LRO) Mini-RF and Chandrayaan-1’s Mini-SAR provide valuable insights into the properties of the lunar surface. However, public lunar SAR data products are not properly registered and are limited by localization issues. Existing registration methods for Earth SAR have proven to be inadequate in their robustness for lunar data registration. And current research on methods for lunar SAR has not yet focused on producing globally registered datasets. To solve these problems, this article introduces a robust automatic registration method tailored for S-band Level-1 Mini-RF and Mini-SAR data with the assistance of lunar DEM. A simulated SAR image based on real lunar DEM data is first generated to assist the registration work, and then an offset calculation approach based on normalized cross-correlation (NCC) and specific processing, including background removal, is proposed to achieve the registration between the simulated image, and the real image. When applying Mini-RF images and Mini-SAR images, high robustness and good accuracy are exhibited, which produces fully registered datasets. After processing using the proposed method, the average error between Mini-RF images and DEM references was reduced from approximately 3000 m to about 100 m. To further explore the additional improvement of the proposed method, the registered lunar SAR datasets are used for further analysis, including a review of the circular polarization ratio (CPR) characteristics of anomalous craters. Full article