用主成分分析自动生成月球陨石坑模板，提升导航精度。
Principal Component Analysis for Lunar Crater Detection
- 基于陨石坑数字高程图的主成分分析生成模板
- 在模拟月面图像上检测性能优于人工模板
- 适合月球着陆器自动导航系统使用
光学导航是月球轨道器和着陆器任务的关键。由于月表陨石坑丰富且已有大量陨石坑目录，基于图像的陨石坑识别成为光学导航的有前途技术。此外，由于月球陨石坑形态相对一致，模板匹配被视为一种有前景的识别方法。本文提出EigenCrater，一种基于陨石坑数字高程图（DEMs）主成分分析的自动化陨石坑模板生成方法。我们在模拟月面图像上验证了该方法在检测与定位性能上优于人工选取的模板。
原文摘要 · Abstract (English)
Optical navigation is a critical component for lunar orbiter and lander missions. Image-based crater identification has emerged as a promising technology for optical navigation due to the abundance of craters on the lunar surface and the availability of extensive crater catalogs. Moreover, due to the relative morphological homogeneity among lunar craters, template matching has been identified as a promising approach for identification. In this paper, we propose EigenCrater, an automated crater template generation method based on principal component analysis of crater digital elevation maps (DEMs). We demonstrate superior detection and position estimation performance relative to hand-picked templates on simulated lunar imagery.
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