Search Results (28)

Lunar dark mantle deposits (DMDs), formed by explosive volcanic activity on the Moon, are typically composed of glass- and iron-rich pyroclastic materials, with slight variations in color, crystallinity, and TiO₂ concentration by region. This paper proposes a method for identifying DMDs using the YOLOv8 deep learning model, enhanced by the introduction of a multi-scale feature extraction (MSFE) module with an attention mechanism, which improves the model’s ability to detect targets at different scales. First, a DMD dataset was constructed using Lunar Reconnaissance Orbiter (LRO) data, with manual annotations of DMD regions and lunar image slicing to optimize computational efficiency. The YOLOv8 architecture, with the incorporated MSFE module, was then used to improve model accuracy in complex terrain. The experimental results showed that the improved DM-YOLO model achieved a precision (P) of 83.9%, a recall (R) of 83.2%, and a mean average precision (mAP@0.5) of 84.2%, representing increases of 15.2%, 14.4%, and 14.0%, respectively, over those obtained with the original YOLOv8 model. The predicted results were preliminarily verified using FeO abundance data and further confirmed by analysis of M³ spectral absorption features, showing strong consistency with known DMDs in terms of both chemical composition and mineralogical characteristics. Observations showed that DMDs were located primarily in the low- and mid-latitude regions of the Moon, with most deposits found in the lunar highlands. The findings suggest that the DM-YOLO model has significant potential for providing technical support for lunar exploration and resource development, particularly for identifying small-scale features that are difficult to annotate. Full article

The Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) and Korea Pathfinder Lunar Orbiter (KPLO) ShadowCam provide high-resolution (0.5–2 m per pixel) images of the Moon. These high-resolution images facilitate the creation of highly detailed controlled mosaics, which can be used for applications such as regional geomorphic maps, crater size-frequency distribution analysis, and mission planning. We establish the methodology used to produce most of our LROC NAC and ShadowCam regional controlled mosaics, conduct an analysis of the accuracy of our controlled mosaics, and discuss the utility of these products. This accuracy analysis includes a comprehensive analysis of the bundle adjustment results for both our LROC NAC and ShadowCam controlled mosaics and a comprehensive analysis of the positional accuracy of our LROC NAC controlled mosaics, with median positional offsets of our NAC controlled mosaics being <12 m in latitude and <5 m in longitude. Full article

Future lunar south pole missions face dual challenges of highly variable illumination and rugged terrain that directly constrain rover mobility and energy sustainability. To address these issues, this study proposes a dynamic illumination-constrained spatio-temporal A* (DIC3D-A*) path-planning algorithm that jointly optimizes terrain safety and illumination continuity in polar environments. Using high-resolution digital elevation model data from the Lunar Reconnaissance Orbiter Laser Altimeter, a 1300 m × 1300 m terrain model with 5 m/pixel spatial resolution was constructed. Hourly solar visibility for November–December 2026 was computed based on planetary ephemerides to generate a dynamic illumination dataset. The algorithm integrates slope, distance, and illumination into a unified heuristic cost function, performing a time-dependent search in a 3D spatiotemporal state space. Simulation results show that, compared with conventional A* algorithms considering only terrain or distance, the DIC3D-A* algorithm improves CSDV by 106.1% and 115.1%, respectively. Moreover, relative to illumination-based A* algorithms, it reduces the average terrain roughness index by 17.2%, while achieving shorter path length and faster computation than both the Rapidly-exploring Random Tree Star and Deep Q-Network baselines. These results demonstrate that dynamic illumination is the dominant environmental factor affecting lunar polar rover traversal and that DIC3D-A* provides an efficient, energy-aware framework for illumination-adaptive navigation in upcoming missions such as Chang’E-7. Full article

Automated detection of impact craters is essential for planetary surface studies, yet it remains a challenging task due to variable morphology, degraded rims, complex geological settings, and inconsistent illumination conditions. This study presents a novel crater detection methodology designed for large-scale analysis of Lunar Reconnaissance Orbiter Wide-Angle Camera (WAC) imagery. The framework integrates several key components: automatic region-of-interest (ROI) selection to constrain the search space, Canny edge detection to enhance crater rims while suppressing background noise, and a modified Hough transform that efficiently localizes elliptical features by restricting votes to edge points validated through local fitting. Candidate ellipses are then refined through a two-stage adjustment, beginning with L1-norm fitting to suppress the influence of outliers and fragmented edges, followed by least-squares optimization to improve geometric accuracy and stability. The methodology was tested on four representative Wide-Angle Camera (WAC) sites selected to cover a range of crater sizes (between ~1 km and 50 km), shapes, and geological contexts. The results showed detection rates between 82% and 91% of manually identified craters, with an overall mean of 87%. Covariance analysis confirmed significant reductions in parameter uncertainties after refinement, with standard deviations for center coordinates, shape parameters, and orientation consistently decreasing from the L1 to the L2 stage. These findings highlight the effectiveness and computational efficiency of the proposed approach, providing a reliable tool for automated crater detection, lunar morphology studies, and future applications to other planetary datasets. Full article

Craters are among the most prominent and significant geomorphological features on the lunar surface. The complex and variable environment of the lunar surface, which is characterized by diverse textures, lighting conditions, and terrain variations, poses significant challenges to existing crater detection methods. To address these challenges, this study introduces DBYOLO, an innovative deep learning framework designed for lunar crater detection, leveraging a dual-backbone feature fusion network, with two key innovations. The first innovation is a lightweight dual-backbone network that processes Lunar Reconnaissance Orbiter Camera (LROC) CCD images and Digital Terrain Model (DTM) data separately, extracting texture and edge features from CCD images and terrain depth features from DTM data. The second innovation is a feature fusion module with attention mechanisms that is used to dynamically integrate multi-source data, enabling the efficient extraction of complementary information from both CCD images and DTM data, enhancing crater detection performance in complex lunar surface environments. Experimental results demonstrate that DBYOLO, with only 3.6 million parameters, achieves a precision of 77.2%, recall of 70.3%, mAP50 of 79.4%, and mAP50-95 of 50.4%, representing improvements of 3.1%, 1.8%, 3.1%, and 2.6%, respectively, over the baseline model before modifications. This showcases an overall performance enhancement, providing a new solution for lunar crater detection and offering significant support for future lunar exploration efforts. Full article

Accurate computation of external heat flux is critical for spacecraft thermal analysis and thermal control system design. The traditional method, which adopted the uniform planetary infrared radiation model (UPIRM), is inadequate for lunar orbital missions due to the extreme planetary surface temperature variations. This study proposes an external heat flux calculation method for lunar orbits by integrating a non-uniform lunar surface temperature model derived from Lunar Reconnaissance Orbiter (LRO) Diviner radiometric data. Specifically, the lunar surface temperature data were first fitted as functions of latitude ($\psi$) and position angles ($\zeta$) through data regression analysis. Then, a comprehensive mathematical framework is established to analyze solar radiation, lunar albedo, and lunar infrared radiation components, incorporating orbital parameters such as beta angle ($\beta$), orbital inclination ($i$) and so on. Coordinate transformations and numerical integration techniques are employed to evaluate heat flux distributions across cuboidal orbiter surfaces. It is found that the lunar infrared radiation heat flux manifests pronounced fluctuation, peaking at 1023 W/m² near the lunar noon region while plummeting to 20 W/m² near the midnight region under the orbital parameters investigated in this study. This study demonstrates the essential role of the non-uniform planetary infrared radiation model (NUPIRM) in enhancing prediction accuracy by contrast, offering foundational references for thermal management in future lunar and deep-space exploration spacecraft. Full article

The Narrow-Angle Cameras (NACs) onboard the Lunar Reconnaissance Orbiter Camera (LROC) capture lunar images that play a crucial role in current lunar exploration missions. Among these images, those of the Moon’s permanently shadowed regions (PSRs) are highly noisy, obscuring the lunar topographic features within these areas. While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. To address this issue, we employ a physical noise model based on the imaging principles of the LROC NACs to generate noisy pairs of images for the Moon’s PSRs, simulating realistic lunar imagery. Furthermore, inspired by the ideas of full-scale skip connections and self-attention models (Transformers), we propose a denoising method based on deep information convolutional neural networks. Using a dataset synthesized through the physical noise model, we conduct a comparative analysis between the proposed method and existing state-of-the-art denoising approaches. The experimental results demonstrate that the proposed method can effectively recover topographic features obscured by noise, achieving the highest quantitative metrics and superior visual results. 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

Planetary photogrammetry remains an important technical means of producing high-precision planetary maps. High-quality control networks are fundamental to successful bundle adjustment. However, current software tools used by the planetary mapping community to construct and edit control networks exhibit very low efficiency. Moreover, redundant and invalid control points in the control network can further increase the time required for the bundle adjustment process. Due to a lack of targeted algorithm optimization, existing software tools and methods are unable to meet the photogrammetric processing requirements of massive planetary remote sensing images. To address these issues, we first proposed an efficient control network construction framework based on approximate orthoimage matching and hash quick search. Next, to effectively reduce the redundant control points in the control network and decrease the computation time required for bundle adjustment, we then proposed a control network-thinning algorithm based on a K-D tree fast search. Finally, we developed an automatic detection method based on ray tracing for identifying invalid control points in the control network. To validate the proposed methods, we conducted photogrammetric processing experiments using both the Lunar Reconnaissance Orbiter (LRO) narrow-angle camera (NAC) images and the Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRIS-REx) PolyCam images; we then compared the results with those derived from the famous open-source planetary photogrammetric software, the United States Geological Survey (USGS) Integrated Software for Imagers and Spectrometers (ISIS) version 8.0.0. The experimental results demonstrate that the proposed methods significantly improve the efficiency and quality of constructing control networks for large-scale planetary images. For thousands of planetary images, we were able to speed up the generation and editing of the control network by more than two orders of magnitude. Full article

The physical properties of lunar regolith are crucial for exploration planning, hazard assessment, and characterizing scientific targets at global and polar scales. The dielectric constant, a key property, offers insights into lunar material distribution within the regolith and serves as a proxy for identifying volatile-rich regoliths. Miniature radio frequency (Mini-RF) on the Lunar Reconnaissance Orbiter (LRO) provides a potential tool for mapping the lunar regolith’s physical nature and assessing the lunar volatile repository. This study presents global and polar S-band Mini-RF dielectric signatures of the Moon, obtained through a novel deep learning inversion model applied to Mini-RF mosaics. We achieved good agreement between training and testing of the model, yielding a coefficient of determination (R² value) of 0.97 and a mean squared error of 0.27 for the dielectric constant. Significant variability in the dielectric constant is observed globally, with high-Ti mare basalts exhibiting lower values than low-Ti highland materials. However, discernibility between the South Pole–Aitken (SPA) basin and highlands is not evident. Despite similar dielectric constants on average, notable spatial variations exist within the south and north polar regions, influenced by crater ejecta, permanently shadowed regions, and crater floors. These dielectric differences are attributed to extensive mantling of lunar materials, impact cratering processes, and ilmenite content. Using the east- and west-looking polar mosaics, we estimated an uncertainty (standard deviation) of 1.01 in the real part and 0.03 in the imaginary part of the dielectric constant due to look direction. Additionally, modeling highlights radar backscatter sensitivity to incidence angle and dielectric constant at the Mini-RF wavelength. The dielectric constant maps provide a new and unique perspective of lunar terrains that could play an important role in characterizing lunar resources in future targeted human and robotic exploration of the Moon. Full article

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km². The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m. Full article

High-alumina (HA) mare basalts play a critical role in lunar mantle differentiation. Although remote sensing methods have speculated their potential presence regions based on sample FeO and TiO₂ compositions, the location and distribution characteristics of HA basalts have not been provided. In this study, the compositions of exposed rocks in Mare Imbrium were determined using Lunar Reconnaissance Orbiter (LRO) Diviner oxides and Lunar Prospector Gamma-Ray Spectrometer (LP-GRS) Thorium (Th) products. The exposed HA basalts were identified based on laboratory lithology classification criteria and Al₂O₃ abundance. The HA basalt units were mapped based on lunar topographic data, and their morphological geological characteristics were calculated based on elevation data. The results show that there are 8406 HA basalt pixels and 17 original units formed by volcanic eruptions in Mare Imbrium. The statistics of their morphology characteristics show that the HA basalts are widely distributed in the northern part of Mare Imbrium, and their compositions have a large range of variation. These units have different area and volume, and the layers formed were discontinuous. The characteristic analysis shows that the aluminum-bearing volcanic activities in Mare Imbrium were irregular. The eruptions of four different source regions occurred in three phases, and the scale and extent of the eruptions were different. The results in this study provide reliable evidence for the heterogeneity of the lunar mantle and contribute valuable information to the formation process of early lunar mantle materials. Full article

Chang’e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of the Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. In particular, the crater size–frequency distribution information of the landing area is critical to understanding the provenance of the CE-6 lunar samples to be returned and can be used to verify and refine the lunar chronology model by combining with the radioisotope ages of the relevant samples. In this research, a digital orthophoto map (DOM) mosaic with resolution of 3 m/pixel of the CE-6 landing area was generated from the 743 Narrow Angle Camera of the Lunar Reconnaissance Orbiter Camera. Based on the DOM, craters were extracted by an automated method and checked manually. A total of 770,731 craters were extracted in the whole area of 246 km × 135 km, 511,484 craters of which were within the mare area. Systematic analyses of the crater distribution, completeness, spatial density, and depth-to-diameter ratio were conducted. Geologic model age estimation was carried out in the mare area that was divided into three geologic units according to the TiO₂ abundance. The result showed that the east part of the mare had the oldest model age of $\mu {3.27}_{-0.045}^{+0.036}$ Ga, and the middle part of the mare had the youngest model age of $\mu {2.49}_{-0.073}^{+0.072}$ Ga. The crater catalogue and the surface model age analysis results were used to support topographic and geologic analyses of the pre-selected landing area of the CE-6 mission before the launch and will contribute to further scientific researches after the lunar samples are returned to Earth. Full article

As a radiant light source within the dynamic range of most spacecraft payloads, the Moon provides an excellent reference for on-orbit radiometric calibration. This research hinges on the precise simulation of lunar spectral irradiances and Earth-based Moon observation geometry. The paper leverages the Hapke model to simulate the temporal changes in lunar spectral irradiances, utilizing datasets obtained from the Lunar Reconnaissance Orbiter Camera (LROC). The research also details the transformation process from the lunar geographic coordinate system to the instantaneous projection coordinate system, thereby delineating the necessary observational geometry. The insights offered by this study have the potential to enhance future in-orbit spacecraft calibration procedures, thereby boosting the fidelity of data gathered from satellite observations. Full article

We demonstrate the creation of a large area of high-resolution (260 × 209 km² at 1 m/pixel) DTM mosaic from the Lunar Reconnaissance Orbiter Camera (LROC) Narrow Angle Camera (NAC) images over the Chang’E-4 landing site at Von Kármán crater using an in-house deep learning-based 3D modelling system developed at University College London, called MADNet, trained with lunar orthorectified images and digital terrain models (DTMs). The resultant 1 m DTM mosaic is co-aligned with the Chang’E-2 (CE-2) and the Lunar Orbiter Laser Altimeter (LOLA)—SELenological and Engineering Explorer (SELENE) blended DTM product (SLDEM), providing high spatial and vertical congruence. In this paper, technical details are briefly discussed, along with visual and quantitative assessments of the resultant DTM mosaic product. The LROC NAC MADNet DTM mosaic was compared with three independent DTM datasets, and the mean differences and standard deviations are as follows: PDS photogrammetric DTM at 5 m grid-spacing had a mean difference of −0.019 ± 1.09 m, CE-2 DTM at 20 m had a mean difference of −0.048 ± 1.791 m, and SLDEM at 69 m had a mean difference of 0.577 ± 94.940 m. The resultant LROC NAC MADNet DTM mosaic, alongside a blended LROC NAC and CE-2 MADNet DTM mosaic and a separate LROC NAC, orthorectified image mosaic, are made publicly available via the ESA planetary science archive’s guest storage facility. Full article