Unmixing-Based Spatiotemporal Image Fusion Based on the Self-Trained Random Forest Regression and Residual Compensation-Reference-Cited by-同舟云学术

Unmixing-Based Spatiotemporal Image Fusion Based on the Self-Trained Random Forest Regression and Residual Compensation

Published:2023 Issue: Volume:61 Page:1-19
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Li Xiaodong¹^ORCID,Wang Yalan¹,Zhang Yihang¹,Hou Shuwei²,Zhou Pu¹^ORCID,Wang Xia³,Du Yun¹,Foody Giles M.⁴^ORCID

Affiliation:

1. Key Laboratory for Environment and Disaster Monitoring and Evaluation, Hubei, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, China

2. China Academy of Space Technology (Xi’an), Xi’an, China

3. CAS Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, China

4. School of Geography, University of Nottingham, Nottingham, U.K.

Funder

Natural Science Foundation of China

International Science and Technology Cooperation Project from Hubei Province, China

Key Research Program of Frontier Sciences, Chinese Academy of Sciences

Young Top-Notch Talent Cultivation Program of Hubei Province

Hubei Provincial Natural Science Foundation of China for Distinguished Young Scholars

Key Research and Development Project of Hubei Province, China

Application Foundation Frontier Project of Wuhan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10231141.pdf?arnumber=10231141

Reference83 articles.

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4. Challenges in using land use and land cover data for global change studies

5. Global Consequences of Land Use

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