论文标题
自动化火山口检测与人类水平的性能
Automated crater detection with human level performance
论文作者
论文摘要
火山口编目是地质映射的重要但耗时的部分。我们提出了一种自动化的火山口检测算法（CDA），该算法与专家人类研究人员竞争，数百次竞争。 CDA使用多个神经网络来处理数字地形模型和热红外图像，以识别和定位火星表面的陨石坑。我们使用额外的后处理过滤器来完善和删除潜在的虚假火山口检测，与Lee（2019）相比，我们的精度和召回率提高了10％。现在，我们发现直径高于3公里的已知陨石坑中有80％，并确定了7,000个潜在的新陨石坑（占鉴定陨石坑的13％）。我们的目录和其他独立目录之间的中位差异在位置和直径为2-4％，与其他录音机间比较并存。 CDA已用于处理火星的全球地形图和红外图像，并且可以在https://doi.org/10.5683/sp2/cfunii上获得软件和生成的全球目录。
Crater cataloging is an important yet time-consuming part of geological mapping. We present an automated Crater Detection Algorithm (CDA) that is competitive with expert-human researchers and hundreds of times faster. The CDA uses multiple neural networks to process digital terrain model and thermal infra-red imagery to identify and locate craters across the surface of Mars. We use additional post-processing filters to refine and remove potential false crater detections, improving our precision and recall by 10% compared to Lee (2019). We now find 80% of known craters above 3km in diameter, and identify 7,000 potentially new craters (13% of the identified craters). The median differences between our catalog and other independent catalogs is 2-4% in location and diameter, in-line with other inter-catalog comparisons. The CDA has been used to process global terrain maps and infra-red imagery for Mars, and the software and generated global catalog are available at https://doi.org/10.5683/SP2/CFUNII.
