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

被引:5
|
作者
Wright, Jack [1 ]
Barrett, Alexander M. [2 ]
Fawdon, Peter [2 ]
Favaro, Elena A. [2 ]
Balme, Matthew R. [2 ]
Woods, Mark J. [3 ]
Karachalios, Spyros [3 ]
机构
[1] European Space Agcy ESA, European Space Astron Ctr ESAC, Camino Bajodel Castillo S-N, Madrid 28692, Spain
[2] Open Univ, Sch Phys Sci, Milton Keynes, Bucks, England
[3] SCISYS Ltd, Chippenham, England
来源
基金
英国科研创新办公室;
关键词
Mars' surface; geomorphology; Jezero crater; machine learning; deep learning; rover planning;
D O I
10.1080/17445647.2022.2095935
中图分类号
P9 [自然地理学]; K9 [地理];
学科分类号
0705 ; 070501 ;
摘要
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.
引用
收藏
页码:484 / 496
页数:13
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