低太阳高度角月球影像的陨石坑自动提取方法

陨石坑是月球表面最重要的地形特征,对于研究月球的地质状况和地理状况具有重要的意义。充分考虑低太阳高度角影像中陨石坑特殊的拓扑结构和灰度分布特点,提出一种新的基于光照方向的陨石坑边缘连接方法,综合边缘线判别、边缘线组合、陨石坑判别的陨石坑筛选三步法,形成一套完整的低太阳高度角月球影像陨石坑自动提取流程。用低太阳高度角Lunar Orbit影像进行实验,检测成功率达到了73.9%,表明该方法对于低太阳高度角月球影像陨石坑的自动提取有较好的适用性。     

Titanium estimates of the central peaks of lunar craters: Implications for sub-surface lithology of moon

Central peaks of 24 lunar craters, having mafic rocks, were studied to estimate their average titanium content and infer the nature of the subsurface lithologies. Titanium contents were derived from Clementine UV–Vis data (415, 750 nm) following the approach of Lucey et al. [Lucey, P.G., Blewett, D.T. and Jolliff, B.L., Lunar iron and titanium abundance algorithms based on final processing of Clementine ultraviolet–visible images, J. Geophys. Res.106 (E8), 20297–20,305, 2000]. _TiO2${\mathrm{TiO}}_2$ content exceeding 1 wt% suggests presence of mantle derived mafic sub-surface rock types (plutonic/volcanic) within the central peaks. Even though, the algorithm used for deriving titanium content is susceptible to variation in topography and sun angle, especially at higher latitudes, careful selection and analyses of data for regions within the central peaks revealed compositional heterogeneities. The results indicate a preponderance of mafic lithologies with low _TiO2${\mathrm{TiO}}_2$ content (<1 wt%) in the central peaks of lunar craters populating the equatorial region. Average titanium content of central peaks can serve as a useful tracer for distinguishing mantle derived mafic subsurface lithologies from those formed during global magma ocean episode.     

Detection of sub-kilometer craters in high resolution planetary images using shape and texture features

Counting craters is a paramount tool of planetary analysis because it provides relative dating of planetary surfaces. Dating surfaces with high spatial resolution requires counting a very large number of small, sub-kilometer size craters. Exhaustive manual surveys of such craters over extensive regions are impractical, sparking interest in designing crater detection algorithms (CDAs). As a part of our effort to design a CDA, which is robust and practical for planetary research analysis, we propose a crater detection approach that utilizes both shape and texture features to identify efficiently sub-kilometer craters in high resolution panchromatic images. First, a mathematical morphology-based shape analysis is used to identify regions in an image that may contain craters; only those regions - crater candidates - are the subject of further processing. Second, image texture features in combination with the boosting ensemble supervised learning algorithm are used to accurately classify previously identified candidates into craters and non-craters. The design of the proposed CDA is described and its performance is evaluated using a high resolution image of Mars for which sub-kilometer craters have been manually identified. The overall detection rate of the proposed CDA is 81%, the branching factor is 0.14, and the overall quality factor is 72%. This performance is a significant improvement over the previous CDA based exclusively on the shape features. The combination of performance level and computational efficiency offered by this CDA makes it attractive for practical application.