Learning Crop-Type Mapping From Regional Label Proportions in Large-Scale SAR and Optical Imagery-Reference-Cited by-同舟云学术

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Learning Crop-Type Mapping From Regional Label Proportions in Large-Scale SAR and Optical Imagery

Published:2023 Issue: Volume:61 Page:1-15
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Cué La Rosa Laura Elena¹^ORCID,Oliveira Dário Augusto Borges²^ORCID,Ghamisi Pedram³^ORCID

Affiliation:

1. Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Freiberg, Germany

2. Data Science in Earth Observation, Technical University of Munich (TUM), Munich, Germany

3. Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology, Freiberg, Germany

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10268386.pdf?arnumber=10268386

Reference52 articles.

1. Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

2. Deep learning in remote sensing applications: A meta-analysis and review

3. A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications

4. Deep Learning for the Earth Sciences

5. Autoencoders, minimum description length and Helmholtz free energy;Hinton

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2. Remote Sensing Contributions to Water-Related Risk Management within "Return", the Italian National Program on Environmental Risks in the Next Generation EU Framework;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

3. Land Suitability Assessment Based on Feature-Level Fusion of Sentinel-1 and Sentinel-2 Imagery: A Case Study of the Honam Region of Iran;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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