Jezero crater, Mars: application of the deep learning NOAH-H terrain classification system

We applied a deep learning terrain classification system, the ‘Novelty or Anomaly Hunter – HiRISE’ (NOAH-H), originally developed for the ExoMars landing sites in Oxia Planum and Mawrth Vallis, to the Mars 2020 Perseverance rover landing site in Jezero crater. NOAH-H successfully classified the terrain in four HiRISE images of Jezero even though the landforms in the Jezero study area were slightly different from those in the training dataset. We mosaicked the NOAH-H classified rasters and compared them with a manually generated photogeological map, and with Perseverance rover and Ingenuity helicopter images. We find that grouped NOAH-H classes correspond well with the humanmade map and that individual classes are corroborated by the available ground-truth images. We conclude that our NOAH-H products can be refined for feeding into traversability analysis of the ExoMars Rosalind Franklin rover landing site at Oxia Planum and that they can also be used to aid the photogeological mapping process.

我们将原本为欧空局ExoMars计划奥克夏平原与莫尔茨峡谷着陆点研发的深度学习地形分类系统“新奇异常探测器——HiRISE”（NOAH-H），应用于火星2020任务毅力号火星车的杰泽罗撞击坑着陆区域。尽管杰泽罗研究区域的地貌与训练数据集的地貌存在细微差异，NOAH-H仍成功完成了该区域4幅HiRISE影像的地形分类工作。我们将NOAH-H分类得到的栅格数据进行影像镶嵌，并将镶嵌结果与人工制作的摄影地质图，以及毅力号火星车、机智号直升机的实测影像进行了比对。研究结果显示，分组后的NOAH-H分类类别与人工摄影地质图的匹配度极佳，且各单个分类类别均可通过现有实地验证影像得到佐证。综上，本研究生成的NOAH-H分类产品可经优化后用于奥克夏平原ExoMars罗莎琳德·富兰克林号火星车着陆点的通行性分析，同时也可辅助摄影地质制图工作。