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Geomorphological scene classification dataset of high-resolution remote sensing imagery in vegetation-covered areas

  • role： First author第一作者
  • Affiliation：

    Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China

  • Email：oysb@cug.edu.cn
  • Introduction：欧阳淑冰，研究方向为地学遥感智能解译理论和应用。E-mail： oysb@cug.edu.cn
OUYANG Shubing,  
  • role： Corresponding author通信作者
  • Affiliation：

    Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China

  • Email：wtchen@cug.edu.cn
  • Introduction：陈伟涛，研究方向为遥感地质解译及应用。E-mail： wtchen@cug.edu.cn
CHEN Weitao*,  
  • Affiliation：

    Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China

LI Xianju,  
  • Affiliation：

    Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China

DONG Yusen,  
  • Affiliation：

    Faculty of Computer Science, China University of Geosciences, Wuhan 430074, China

WANG Lizhe

Resümee

A geomorphological dataset is considered to be one of the most important data sources to realize automatic classification of geomorphology and deepening understanding of geomorphological morphology. At present, the datasets of high-precision geomorphologic origin are scarce, hindering the development of automatic geomorphological interpretation using remote sensing data techniques. In the Tianshan–Xingmeng orogenic system, which is dominated by the gully arc-basin system in northeast China, three scene datasets namely, tectonic geomorphology, volcanic lava geomorphology, and flowing geomorphology are made. These geomorphology types were formed by strong tectonic movement, volcanism from the Neozoic, and flowing water action from the Neozoic. The data set covers an area of approximately 5000 km2, including visible light remote sensing image of Sentinel-2, SRTM1 DEM, and seven geomorphological variables based on DEM extraction (hillshade, slope, DEM local average value, standard deviation, two components of aspect, and relative deviation from mean value). Each sample patch is 64×64 pixels with a spatial resolution of 10 m. A multi-modal deep learning model is proposed for classification, and the results show that the average test accuracy is 82.63%. The quality of the dataset is high. The dataset (available from https://pan.‍baidu.‍com/s/1Kzj04cU-TiofPk6pTEKENg, password: cug0) could provide fundamental data support for the automatic classification research of geomorphological causes by remote sensing and promote the development of intelligent interpretation in the geoscience community by remote sensing techniques.

Schlüsselwort

geomorphology datasets;geomorphological classification;deep learning;remote sensing scene classification;vegetation cover

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