Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management-Reference-Cited by-同舟云学术

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Antropov Oleg¹,Molinier Matthieu¹,Kuzu Rıdvan Salih²,Hughes Lloyd³,Rußwurm Marc³,Tuia Devis³,Dumitru Corneliu Octavian²,Ge Shaojia⁴,Saha Sudipan⁵,Zhu Xiao Xiang⁶

Affiliation:

1. VTT Technical Research Centre of Finland,VTT,Finland,FI-02044

2. Remote Sensing Technology Institute,German Aerospace Center,Weßling,Germany,82234

3. Ecole Polytechnique Fédérale de Lausanne (EPFL),Sion,Switzerland

4. Nanjing University of Science and Technology,Nanjing,China,210094

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

6. Technical University of Munich,Data Science in Earth Observation,Munich,Germany,80333

Funder

European Space Agency

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282283.pdf?arnumber=10282283

Reference17 articles.

1. Unsupervised Deep Change Vector Analysis for Multiple-Change Detection in VHR Images

2. Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery

3. Momentum Contrast for Unsupervised Visual Representation Learning

4. Boreal Forest Snow Damage Mapping Using Multi-Temporal Sentinel-1 Data

5. Mapping tree species proportions from satellite imagery using spectral–spatial deep learning

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