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月球形貌遥感测绘发展现状与未来展望
刘成保, 薄正, 张鹏, 周米玉, 刘琬玥, 黄荣, 牛冉, 叶真, 杨瀚哲, 刘世杰, 韩东旭, 林茜
地球信息科学学报 ›› 2025, Vol. 27 ›› Issue (4) : 801-819.
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月球形貌遥感测绘发展现状与未来展望
Current Status and Future Prospects of Lunar Topographic Remote Sensing and Mapping
({{custom_author.role_en}}), {{javascript:window.custom_author_en_index++;}}【意义】月球形貌遥感测绘是保障月球探测任务安全实施和推动月球科学研究的关键手段，对于理解月球地质演化和地月系统的形成具有重要意义。【进展】近年来，随着国内外对月球探测的兴趣与投入不断增加，遥感技术的创新推动了月球形貌测绘精度和覆盖范围的显著提升，各类遥感任务获取了大量多源、多模态和多尺度的数据，为技术突破奠定了基础。然而，数据量和复杂性的急剧增加，带来了形貌测绘处理的严峻挑战。本文全面综述了月球形貌遥感测绘的发展现状，重点梳理第二次探月热潮以来月球遥感探测任务的实施与数据获取情况，系统总结了激光高度计测高、光学摄影测量以及合成孔径雷达地形测量等关键测绘技术的最新研究进展与应用。【展望】对月球形貌遥感测绘领域的发展趋势与未来可能面临的挑战进行了深入探讨和展望，针对传感器能力提升、月球绝对参考框架优化、多源数据融合精细建模、海量遥感数据智能高效处理、以及推动科学应用水平发展的前景等方面给出了建议。
[Significance] Lunar remote sensing is a critical method to ensure the safety and success of lunar exploration missions while advancing lunar scientific research. It plays a significant role in understanding the Moon's geological evolution and the formation of the Earth-Moon system. Accurate lunar topographic maps are essential for mission planning, including landing site selection, navigation, and resource identification. These maps also provide valuable data for studying planetary processes and the history of the solar system. [Progress] In recent years, with growing global interest and investment in lunar exploration, remarkable progress has been made in remote sensing technology. These advancements have significantly improved the precision, resolution, and coverage of lunar topographic mapping. Various lunar remote sensing missions, such as China's Chang'e program, NASA's Lunar Reconnaissance Orbiter, and missions by other space agencies, have acquired substantial amounts of multi-source, multi-modal, and multi-scale data. This wealth of data has laid a solid foundation for technological breakthroughs. For instance, high-resolution laser altimetry, optical photogrammetry, and synthetic aperture radar have provided detailed datasets, enabling refined mapping of the Moon's surface. However, the dramatic increase in data volume, complexity, and heterogeneity presents challenges for effective processing, integration, and application in topographic mapping. This paper provides a comprehensive overview of the current state of lunar topographic remote sensing and mapping, focusing on the implementation and data acquisition capabilities of major lunar remote sensing missions during the second wave of lunar exploration. It systematically summarizes the latest research progress in key surveying and mapping technologies, including laser altimetry, which enables precise elevation measurements; optical photogrammetry, which reconstructs surface features using high-resolution imagery; and synthetic aperture radar, which provides unique insights into topographic and subsurface structures. [Prospect] In addition to reviewing recent advancements, the paper discusses future trends and challenges in the field. Key recommendations include enhancing sensor functionality and performance metrics to improve data quality, optimizing the lunar absolute reference framework for consistency and accuracy, leveraging multi-source data fusion for fine-scale modeling, expanding scientific applications of lunar topography, and developing intelligent and efficient methods to process massive amounts of remote sensing data. These efforts will not only support upcoming lunar exploration missions, such as China's manned lunar landing program scheduled for 2030, but also contribute to a deeper understanding of the Moon and its relationship with Earth.
月球遥感 / 轨道器影像 / 形貌测绘 / 摄影测量 / 激光测高 / 数字高程模型 / 数字正射影像 {{custom_keyword}} /
lunar remote sensing / orbiter imagery / topographic mapping / photogrammetry / laser altimetry / Digital Elevation Model / Digital Orthophoto Map {{custom_keyword}} /
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The lunar South Pole region’s limited illumination, extensive shadowed regions, and homogenous surface features with weak textures pose significant challenges to stereoscopic image matching and 3D terrain reconstruction. To address these issues, an Efficient Confidence-guided Stereo Matching (ECSM) algorithm was proposed. This algorithm improved matching precision and efficiency by assessing the confidence of non-support points, updating the support point dataset, constructing a triangulated mesh, and recalculating disparities within triangle vertices based on their confidence levels. On this basis, a photogrammetric method for lunar 3D terrain reconstruction was established. High-resolution image data from the Lunar Reconnaissance Orbiter’s Narrow Angle Camera was utilized for validation experiments conducted in the vicinity of the Shackleton crater within the lunar South Pole region. Qualitative and quantitative analyses of disparity maps and Digital Elevation Model (DEM) generated from different stereo matching algorithms demonstrated the reliability of the proposed algorithm in regions with weak and repetitive textures. Comparative analyses with the German Aerospace Center’s DEM and NASA’s Lunar Orbiter Laser Altimeter DEM (LDEM) for the same region revealed significant consistency in elevation and slope information, affirming the practicality and effectiveness of the proposed method. This study provides a methodological foundation for landing site selection for lunar South Pole explorations.
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This paper describes the Semi-Global Matching (SGM) stereo method. It uses a pixelwise, Mutual Information based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement and multi-baseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed.A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2s on typical test images. An in depth evaluation of the Mutual Information based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.
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