Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data

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成果类型：

期刊论文

作者：

Jiang, Zhiqi;Wen, Yijun;Zhang, Gui;Wu, Xin

通讯作者：

Wen, Y.

作者机构：

[Jiang, Zhiqi] Cent South Univ Forestry & Technol, Sch Forestry, Changsha 410004, Peoples R China.

[Wen, Yijun] Cent South Univ Forestry & Technol, Fac Sci, Changsha 410004, Peoples R China.

[Zhang, Gui] Natl Forest Fire Prevent Virtual Simulat Lab Teac, Changsha 410004, Peoples R China.

[Wu, Xin] Key Lab Digital Dongting Lake Hunan Prov, Changsha 410004, Peoples R China.

通讯机构：

Faculty of Science, Central South University of Forestry and Technology, Changsha, China

语种：

英文

关键词：

random forest;red-edge remote sensing data;Sentinel-2;water extraction

期刊：

Sustainability

ISSN：

2071-1050

年：

2022

卷：

期：

页码：

3797

DOI：

10.3390/su14073797

基金类别：

Science and Technology Innovation Platform and Talent Plan Project of Hunan Province [2017TP1022]; Hunan Province Emergency Management Technology Project [2020YJ007]

机构署名：

本校为第一机构

院系归属：

林学院

摘要：

For the Sentinel-2 multispectral satellite image remote sensing data, due to the rich spatial information, the traditional water body extraction methods cannot meet the needs of practical applications. In this study, a random forest-based RF_16 optimal combination model algorithm is proposed to extract water bodies. The research process uses Sentinel-2 multispectral satellite images and DEM data as the basic data, collected 24 characteristic variable indicators (B2, B3, B4, B8, B11, B12, NDVI, MSAVI, B5, B6, B7, B8A, NDI45, MCARI, REIP, S2REP, ...

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Water Information Extraction Based on Multi-Model RF Algorithm and Sentinel-2 Image Data

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