植被覆盖区高精度遥感地貌场景分类数据集

欧阳淑冰; 陈伟涛; 李显巨; 董玉森; 王力哲

doi:10.11834/jrs.20221385

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植被覆盖区高精度遥感地貌场景分类数据集
Geomorphological scene classification dataset of high-resolution remote sensing imagery in vegetation-covered areas
2022年26卷第4期页码：606-619
收稿：2021-06-10，

纸质出版：2022-04-07
DOI： 10.11834/jrs.20221385
稿件说明：

移动端阅览

欧阳淑冰，陈伟涛，李显巨，董玉森，王力哲.2022.植被覆盖区高精度遥感地貌场景分类数据集.遥感学报，26（4）： 606-619 DOI： 10.11834/jrs.20221385.

Ouyang S B，Chen W T，Li X J，Dong Y S and Wang L Z. 2022. Geomorphological scene classification dataset of high-resolution remote sensing imagery in vegetation-covered areas. National Remote Sensing Bulletin， 26（4）：606-619 DOI： 10.11834/jrs.20221385.

摘要

地貌数据集是实现地貌自动分类和加深对地貌形态学认识的重要支撑数据之一。当前缺乏高精度地貌成因类数据集，制约了地貌遥感自动解译的发展。本文在中国东北地区以沟—弧—盆体系为主的天山—兴蒙造山系中，针对强烈的构造运动和新生代以来的火山作用、流水作用形成的地貌成因类型，制作了构造地貌、火山熔岩地貌和流水地貌3类场景数据集（GOS10m）。数据集覆盖面积约5000 km

，包括哨兵2号可见光遥感影像、SRTM1 DEM及基于DEM提取的7个地貌形态参数（山体晕渲图、坡度、DEM局部平均中值、标准偏差、坡向—向北方向偏移量、坡向—向东方向偏移量和相对偏离平均值）。单张样本图为64像素×64像素，空间分辨率为10 m。采用多模态深度学习神经网络对数据进行训练并分类，平均测试精度可达到82.63%，表明构建的数据集具有较高的质量。可为地貌成因遥感自动分类研究以及推动遥感地貌智能解译的向前发展，提供数据集支撑。

Abstract

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 km

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.

关键词

Keywords

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