期刊
JOURNAL OF MAPS
卷 19, 期 1, 页码 -出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/17445647.2022.2112777
关键词
Machine learning; Mars surface; geomorphology; ExoMars; deep learning; rover planning
This study presents a DL-based map classification system for the landing site on Mars. By training a DL network with the developed hierarchical scheme, the system achieved high agreement with manually mapped areas. The resulting map is presented in both descriptive classes and interpretive groups, allowing for intuitive analysis by human users.
We present a map of Oxia Planum, Mars, the landing site for the ExoMars Rover. This shows surface texture and aeolian bedform distribution, classified using a deep learning (DL) system. A hierarchical classification scheme was developed, categorising the surface textures observed at the site. This was then used to train a DL network, the 'Novelty or Anomaly Hunter - HiRISE' (NOAH-H). The DL applied the classification scheme across a wider area than could have been mapped manually. The result showed strong agreement with human-mapped areas reserved for validation. The resulting product is presented in two ways, representing the two principle levels of the classification scheme. 'Descriptive classes' are purely textural in nature, making them compatible with a machine learning approach. These are then combined into 'interpretive groups', broader thematic classes, which provide an interpretation of the landscape. This step allows for a more intuitive analysis of the results by human users.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据