The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial Networks-Reference-Cited by-同舟云学术

The Spatially Seamless Spatiotemporal Fusion Model Based on Generative Adversarial Networks

Published:2024 Issue: Volume:17 Page:12760-12771
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:

Weng ChenYang¹^ORCID,Zhan Yulin¹^ORCID,Gu Xingfa²^ORCID,Yang Jian¹^ORCID,Liu Yan¹^ORCID,Guo Hong¹^ORCID,Lian Zilong³,Zhang Shiyuan³,Wang Zhangjie³,Zhao Xuechun³

Affiliation:

1. National Engineering Laboratory for Satellite Remote Sensing Applications, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

3. North China Institute of Aerospace Engineering, Langfang, China

Funder

National Key Research and Development Program of China

Common Application Support Platform for National Civil Space Infrastructure Land Observation Satellites

National Natural Science Foundation of China

Major Project of High Resolution Earth Observation System

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10478639.pdf?arnumber=10478639

Reference37 articles.

1. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance

2. Modeling Soil CO2 Efflux in a Subtropical Forest by Combining Fused Remote Sensing Images with Linear Mixed Effect Models

3. An Advanced Spatiotemporal Fusion Model for Suspended Particulate Matter Monitoring in an Intermontane Lake

4. Stability Analysis of Unmixing-Based Spatiotemporal Fusion Model: A Case of Land Surface Temperature Product Downscaling

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