Generative adversarial network augmentation for solving the training data imbalance problem in crop classification-Reference-Cited by-同舟云学术

Generative adversarial network augmentation for solving the training data imbalance problem in crop classification

Published:2023-10-29 Issue:11 Volume:14 Page:1129-1138
ISSN:2150-704X
Container-title:Remote Sensing Letters
language:en
Short-container-title:Remote Sensing Letters

Author:

Shumilo Leonid¹^ORCID,Okhrimenko Anton²^ORCID,Kussul Nataliia²³^ORCID,Drozd Sofiia²,Shkalikov Oleh²

Affiliation:

1. Department of Geographical Sciences, University of Maryland, College Park, USA

2. Department of Mathematical Modelling and Data Analysis, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine

3. Department of Space Information Technologies and Systems, Space Research Institute, national Academy of Science of Ukraine and State Space Agency of Ukraine, Kyiv, Ukraine

Funder

National Research Foundation of Ukraine

NASA LCLUC

NASA FINESST

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Earth and Planetary Sciences (miscellaneous)

Link

https://www.tandfonline.com/doi/pdf/10.1080/2150704X.2023.2275551

Reference19 articles.

1. GAN generation of synthetic multispectral satellite images

2. Synthesis of Satellite-Like Urban Images From Historical Maps Using Conditional GAN

3. Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program

4. Bowles C. L. Chen R. Guerrero et al. 2018. “Gan augmentation: Augmenting training data using generative adversarial networks.” arXiv preprint arXiv :1810.10863002

5. Pix2pix Conditional Generative Adversarial Network with MLP Loss Function for Cloud Removal in a Cropland Time Series

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