全月球撞击坑形貌特征的识别与多指标表达

全月球撞击坑数据是支撑月表形貌研究以及月球工程探测和科学研究的重要基础性数据,其全面性、精确性是衡量一个国家科技水平甚至综合国力的重要指标。基于嫦娥一号、Clementine多光谱等探月数据,智能化人工提取了全月球106030个直径大于500 m的撞击坑,在IAU公布的有名称的撞击坑及其描述形貌特征指标的基础上,建立了描述全月球撞击坑形貌特征的指标体系并获得了所有撞击坑指标的属性值。针对现阶段尚无一套完整的全月球撞击坑数据库的现状,采用面向对象的Geodatabase数据模型组织全月球撞击坑数据,构建了包含位置、大小、形状、坡度、方向、中央峰、辐射纹等7个大类52个小类指标的全关系型数据库,其可对撞击坑空间数据和属性数据进行一体化管理,实现了对撞击坑数据的存储、检索、处理和应用,为其他月球科学研究基于数据库进行数据挖掘和应用提供了海量基础数据,为后续月球其他资源数据库的构建提供借鉴。今后将进一步完善更新维护全月球数据库的工作。

Identification and morphologic expression of Lunar impact craters

Lunar crater data is the basic support data for the study on lunar morphology, the exploration of moon as well as the other lunar scientific research. Its comprehensiveness and accuracy are the measurement of a naiton's science and technology level, and even important indicator of overall national strength. In this paper based on the Chang'E-1 image, DEM and Clementine UV/Vis data, we detected 106,030 craters with a diameter of more than 500 m in the method of image processing combined with professional knowledge. According to the accuracy assessment like size-frequency cumulative curves and geometric error evaluation, the extracted craters only had an average error of 15.50 km and an error rate of 10.85% was also limited in the accuracy range. On the basis of named craters and their morphological parameters, which were organized by the International Astronomical Union, we set up a complete morphological parameter system consisted of 106,030 craters' morphological parameter attribute data. Furthermore we applied the object-oriented data model namely Geodatabase to organize all the lunar craters data for there is no set of mature database containing the most comprehensive information of lunar crater. This lunar crater database organizes the spatial and attributes data to construct an entire relations database system including the spatial part and the attribute part. The location, spatial relationship and other spatial data were converted to raster form stored in the spatial database and the data characterized craters were positioned in the attribute database which described seven categories of 52 indicators such as the overall size, shape, slope, direction, central peak and radiation grain of the crater. This database not only integrates the management of spatial data and attribute data, but also achieves the function of data storage, retrieval, processing and application. This work can provide mass basic data for data mining and applications of lunar science research and a reference for other resources database construction. The lunar crater database will be better perfected by updating and maintenance works in the further research.