Paper
31 January 2023 An automatic classification method for mapping Martian landforms
Author Affiliations +
Proceedings Volume 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022); 125051M (2023) https://doi.org/10.1117/12.2664529
Event: Earth and Space: From Infrared to Terahertz (ESIT 2022), 2022, Nantong, China
Abstract
The physiographic map can visualize spatial relations between different landforms, thus providing insights into geologic processes that shaped the present-day Martian landscape. The physiographic map of Mars surface is usually made through image interpretation, which is always labor-intensive and highly depends on the expert knowledge. In this paper, we propose an efficient and automatic classification method for characterization of landforms on Mars by using the Mars Orbiter Laser Altimeter (MOLA) digital elevation data. The proposed method was tested on a region where China's Mars probe Tianwen-1 landed. The study area covers the Nepenthes Mensae, Amenthes Planum, northern Terra Cimmeria, northern Hesperia Planum and southern Utopia Planitia region, having a size of 2250km×2750km centered at 117°E, 6°N. The obtained results confirm the effectiveness of the proposed method in describing different topographic characteristics of the Martian landforms. Note that the proposed method is completely data-driven, which can provide a rapid mapping result in large geographical regions, especially from a global perspective to reveal the Martian landform information.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qin Lu, Sicong Liu, Xiaohua Tong, Shijie Liu, Huan Xie, and Yanmin Jin "An automatic classification method for mapping Martian landforms", Proc. SPIE 12505, Earth and Space: From Infrared to Terahertz (ESIT 2022), 125051M (31 January 2023); https://doi.org/10.1117/12.2664529
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KEYWORDS
Mars

Analytical research

Associative arrays

Classification systems

Data modeling

Visualization

Principal component analysis

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