RDNRnet: A Reconstruction Solution of NDVI Based on SAR and Optical Images by Residual-in-Residual Dense Blocks-Reference-Cited by-同舟云学术

RDNRnet: A Reconstruction Solution of NDVI Based on SAR and Optical Images by Residual-in-Residual Dense Blocks

Published:2024 Issue: Volume:62 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Han Yifei¹^ORCID,Huang Jinliang¹,Ling Feng¹^ORCID,Gao Xinyi¹,Cai Wei²,Chi Hong¹^ORCID

Affiliation:

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

2. Hubei Association of Surveying and Mapping, Wuhan, China

Funder

Joint Funds of the National Natural Science Foundation of China

State Key Laboratory of Resources and Environmental Information System, Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/36/10354519/10400494.pdf?arnumber=10400494

Reference57 articles.

1. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients

2. Gap Filling Cloudy Sentinel-2 NDVI and NDWI Pixels with Multi-Frequency Denoised C-Band and L-Band Synthetic Aperture Radar (SAR), Texture, and Shallow Learning Techniques

3. A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products

4. High-quality vegetation index product generation: A review of NDVI time series reconstruction techniques

5. SURE-based optimum-length S-G filter to reconstruct NDVI time series iteratively with outliers removal