Forest Disturbance Detection via Self-Supervised and Transfer Learning With Sentinel-1&2 Images-Reference-Cited by-同舟云学术

Forest Disturbance Detection via Self-Supervised and Transfer Learning With Sentinel-1&2 Images

Published:2024 Issue: Volume:17 Page:4751-4767
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:

Kuzu Rdvan Salih¹^ORCID,Antropov Oleg²^ORCID,Molinier Matthieu²^ORCID,Dumitru Corneliu Octavian¹^ORCID,Saha Sudipan³^ORCID,Zhu Xiao Xiang⁴^ORCID

Affiliation:

1. Remote Sensing Technology Institute, German Aerospace Center, Weßling, Germany

2. VTT Technical Research Centre of Finland Ltd., Espoo, Finland

3. Yardi School of Artificial Intelligence, Indian Institute of Technology Delhi, New Delhi, India

4. Chair of Data Science in Earth Observation, Technical University of Munich, Munich, Germany

Funder

European Space Agency

RepreSent project

ESA Network of Resources Initiative with remote sensing data sponsorship

Helmholtz Association's Initiative and Networking Fund

Helmholtz Artificial Intelligence Cooperation Unit

HAICORE@FZJ partition

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

Reference85 articles.