A Dual-Perspective Spatiotemporal Fusion Model for Remote Sensing Images by Discriminative Learning of the Spatial and Temporal Mapping-Reference-Cited by-同舟云学术

A Dual-Perspective Spatiotemporal Fusion Model for Remote Sensing Images by Discriminative Learning of the Spatial and Temporal Mapping

Published:2024 Issue: Volume:17 Page:12505-12520
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Qian Zhonghua¹,Yue Linwei¹^ORCID,Xie Xiao²^ORCID,Yuan Qiangqiang³^ORCID,Shen Huanfeng⁴^ORCID

Affiliation:

1. Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen, China

2. Key Laboratory for Environment Computation and Sustainability of Liaoning Province, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China

3. School of Geodesy and Geomatics, Wuhan University, Wuhan, China

4. School of Resource and Environmental Sciences, Wuhan University, Wuhan, China

Funder

Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation

Ministry of Natural Resources

Beijing Nova Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10595407.pdf?arnumber=10595407

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1. Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach

2. Addressing spatio-temporal resolution constraints in Landsat and MODIS-based mapping of large-scale floodplain inundation dynamics

3. Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain

4. Spatio-temporal fusion for remote sensing data: an overview and new benchmark

5. Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions