BIOPHYSICAL PARAMETER ESTIMATION USING EARTH OBSERVATION DATA IN A MULTI-SENSOR DATA FUSION APPROACH: CYCLEGAN

Natalia Efremova, E. Erten*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Water management and up-to-date soil moisture (SM) information are crucial to ensure agricultural activities in dry-land farming regions. In this context, remote sensing imagery coupled with machine learning techniques can provide large scale SM information if there is enough data for training, which is really limited in reality. In this paper, we explored the potential of cycle-consistent Generative Adversarial Network (GAN) for data augmentation for training machine learning algorithms, which try to model spatial and temporal dependencies between the SM prediction (output) and the remote sensing imagery (input features). Specifically, the freely available SAR (Sentinel-1) and optical (Sentinel-2) time series data were evaluated together to predict SM using GANs. The experiments demonstrate that the proposed methodology outperforms the compared state-of-the-art methods if there is not enough data to train a regression convolutional neural networks (CNN) to predict SM content.

Original languageEnglish
Title of host publicationIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5965-5968
Number of pages4
ISBN (Electronic)9781665403696
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Duration: 12 Jul 202116 Jul 2021

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2021-July

Conference

Conference2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/TerritoryBelgium
CityBrussels
Period12/07/2116/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

Funding

∗This work was supported by the Space Research and Innovation Network for Technology (SPRINT) under project ID 1243832 and by the Research Fund of the Istanbul Technical University. Project Number:43018.

FundersFunder number
Istanbul Teknik Üniversitesi

    Keywords

    • Autoencoders
    • CNN
    • CycleGAN
    • PCA
    • Ridge regression
    • Sentinel-1
    • Sentinel-2
    • Soil moisture
    • Support vector regression

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