Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)-Reference-Cited by-同舟云学术

Fine-grained wetland classification for national wetland reserves using multi-source remote sensing data and Pixel Information Expert Engine (PIE-Engine)

Published:2023-11-27 Issue:1 Volume:60 Page:
ISSN:1548-1603
Container-title:GIScience & Remote Sensing
language:en
Short-container-title:GIScience & Remote Sensing

Author:

Liu Han¹²^ORCID,Liao Tongkui³,Wang Yu⁴^ORCID,Qian Xiaoming³,Liu Xiaochen³,Li Chengming³,Li Shiwei³,Guan Zhanlei³,Zhu Lijue³,Zhou Xiaoyuan³,Liu Chong³,Hu Tengyun⁵,Luo Ming¹

Affiliation:

1. Key Laboratory of Land Consolidation and Rehabilitation, Technology Innovation Center for Land Engineering, Land Consolidation and Rehabilitation Center, Ministry of Natural Resources, Beijing, China

2. Key Laboratory of Digital Mapping and Land Information Application, Ministry of Natural Resources, Wuhan, China

3. Piesat Information Technology Co, Ltd, Beijing, China

4. National Disaster Reduction Center of China, Ministry of Emergency Management, Beijing, China

5. Beijing Municipal Institute of City Planning and Design, Beijing, China

Funder

National Natural Science Foundation of China

Open Research Fund Program of Key Laboratory of Digital Mapping and Land Information Application, Ministry of Natural Resources

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2023.2286746

Reference68 articles.

1. Superpixels and Polygons Using Simple Non-iterative Clustering

2. Forty Years of Wetland Status and Trends Analyses in the Great Lakes Using Landsat Archive Imagery and Google Earth Engine

3. Assessment, M. E. 2005. Ecosystems and Human Well-Being: Wetlands and Water. Washington: World Resources Institute.

4. Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data

5. Ensemble selection from libraries of models

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