High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels-Reference-Cited by-同舟云学术

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High-Resolution Land-Cover Mapping Based on a Cross-Resolution Deep Learning Framework and Available Low-Resolution Labels

Published:2024 Issue: Volume:17 Page:1839-1856
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:

Qi Xiaoman¹^ORCID,Peng Junhuan¹^ORCID,Wang Yuebin¹^ORCID,Qi Xiaotong²^ORCID,Peng Yun³

Affiliation:

1. School of Land Science and Technology, China University of Geosciences, Beijing, China

2. School of Marine Technology and Geomatics, Jiangsu Ocean University, Lian Yungang, China

3. POWERCHINA Zhongnan Engineering Corporation Limited, Changsha, China

Funder

National Natural Science Foundation of China

Shanxi Transportation Holdings Group Company Limited

Natural Science Foundation of the Higher Education Institutions of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/10330207/10360200.pdf?arnumber=10360200

Reference63 articles.

1. Breaking the resolution barrier: A low-to-high network for large-scale high-resolution land-cover mapping using low-resolution labels

2. Cross-resolution national-scale land-cover mapping based on noisy label learning: A case study of China

3. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017

4. GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery

5. Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017

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