NOAH-H, a deep-learning, terrain classification system for Mars: Results for the ExoMars Rover candidate landing sites-Reference-Cited by-同舟云学术

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NOAH-H, a deep-learning, terrain classification system for Mars: Results for the ExoMars Rover candidate landing sites

Published:2022-01 Issue: Volume:371 Page:114701
ISSN:0019-1035
Container-title:Icarus
language:en
Short-container-title:Icarus

Author:

Barrett Alexander M.,Balme Matthew R.,Woods Mark,Karachalios Spyros,Petrocelli Danilo,Joudrier Luc,Sefton-Nash Elliot

Funder

Science and Technology Facilities Council

European Space Agency

Publisher

Elsevier BV

Subject

Space and Planetary Science,Astronomy and Astrophysics

Reference52 articles.

1. Automatic, exploratory mineralogical mapping of CRISM imagery using summary product signatures;Allender;Icarus,2017

2. SegNet: a deep convolutional encoder-decoder architecture for image segmentation;Badrinarayanan;IEEE Trans. Pattern Anal. Mach. Intell.,2017

3. Transverse Aeolian Ridges (TARs) on Mars;Balme;Geomorphology,2008

4. Surface-based 3D measurements of small aeolian bedforms on Mars and implications for estimating ExoMars rover traversability hazards;Balme;Planet. Space Sci.,2018

5. Automated detection of Martian dune fields;Bandeira;IEEE Geosci. Remote Sens. Lett.,2011

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1. A new workflow for mapping dune features (outline, crestline and defects) combining deep learning and skeletonization from DEM-derived data;Geomorphology;2024-10

2. Design of robotic traverses on the Archytas Dome on the Moon;Open Research Europe;2024-06-13

3. A Framework for Real-time Generation of Multi-directional Traversability Maps in Unstructured Environments;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. The high-resolution map of Oxia Planum, Mars; the landing site of the ExoMars Rosalind Franklin rover mission;Journal of Maps;2024-03-22

5. LineaMapper: A deep learning-powered tool for mapping linear surface features on Europa;Icarus;2024-03

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