Yunfei Li

Orcid: 0000-0003-1734-5008

According to our database1, Yunfei Li authored at least 16 papers between 2019 and 2025.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

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Bibliography

2025
Low-Rank and Sparse Representation Meet Deep Unfolding: A New Interpretable Network for Hyperspectral Change Detection.
IEEE Trans. Geosci. Remote. Sens., 2025

Combining Filling and Fusion Strategies for Generating Synthetic Daily Landsat Time Series Image on Google Earth Engine.
IEEE Trans. Geosci. Remote. Sens., 2025

HSACT: A hierarchical semantic-aware CNN-Transformer for remote sensing image spectral super-resolution.
Neurocomputing, 2025

2024
A Novel Multiplatform Spatiotempoal Data Fusion Approach for Remote Sensing Imagery Based on Parameter Selection.
IEEE Trans. Geosci. Remote. Sens., 2024

Large-scale and high-resolution paddy rice intensity mapping using downscaling and phenology-based algorithms on Google Earth Engine.
Int. J. Appl. Earth Obs. Geoinformation, 2024

Accelerate spatiotemporal fusion for large-scale applications.
Int. J. Appl. Earth Obs. Geoinformation, 2024

2023
Occluded Scene Classification via Cascade Supervised Contrastive Learning.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023

Removing Influence of MODIS Strip Noise in Spatiotemporal Fusion of Remote Sensing Imagery.
IEEE Geosci. Remote. Sens. Lett., 2023

2022
Enhanced Spatiotemporal Fusion via MODIS-Like Images.
IEEE Trans. Geosci. Remote. Sens., 2022

Pansharpening-Based Spatio-Temporal Fusion for Predicting Intense Surface Changes.
IEEE Trans. Geosci. Remote. Sens., 2022

Fusing Sentinel-2 and Landsat-8 Surface Reflectance Data via Pixel-Wise Local Normalization.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

A Noise Proof Strategy for Spatio-Temporal Fusion of Remote Sensing Imagery.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2022

2021
A Extremely Fast Spatio-Temporal Fusion Method for Remotely Sensed Images.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2020
A new sensor bias-driven spatio-temporal fusion model based on convolutional neural networks.
Sci. China Inf. Sci., 2020

Spatio-temporal fusion for remote sensing data: an overview and new benchmark.
Sci. China Inf. Sci., 2020

2019
A New Spatio-Temporal Fusion Method for Remotely Sensed Data Based on Convolutional Neural Networks.
Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, 2019


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