Multisource Classification of Meridiani Planum's Aeolian Landscape Using HiRISE and Opportunity Images Analysis Based on Deep Learning-Reference-Cited by-同舟云学术

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Multisource Classification of Meridiani Planum's Aeolian Landscape Using HiRISE and Opportunity Images Analysis Based on Deep Learning

Published:2022 Issue: Volume:15 Page:9963-9975
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Choromanski Kamil¹^ORCID,Kozakiewicz Joanna²^ORCID,Sobucki Mateusz³^ORCID,Pilarska-Mazurek Magdalena¹^ORCID,Olszewski Robert¹^ORCID

Affiliation:

1. Faculty of Geodesy and Cartography, Warsaw University of Technology, Warsaw, Poland

2. Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland

3. Faculty of Geography and Geology, Jagiellonian University, Krakow, Poland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9656571/09956010.pdf?arnumber=9956010

Reference55 articles.

1. Object-based dune analysis: Automated dune mapping and pattern characterization for Ganges Chasma and Gale crater, Mars;va;Geomorphology,2015

2. Detection of sand dunes on Mars using a regular vine-based classification approach

3. Ultrahigh resolution topographic mapping of Mars with MRO HiRISE stereo images: Meter-scale slopes of candidate Phoenix landing sites

4. An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery

5. The Ames Stereo Pipeline: NASA's Open Source Software for Deriving and Processing Terrain Data

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