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

Natalia Efremova, E. Erten*

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3 Atıf (Scopus)

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar5965-5968
Sayfa sayısı4
ISBN (Elektronik)9781665403696
DOI'lar
Yayın durumuYayınlandı - 2021
Etkinlik2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium
Süre: 12 Tem 202116 Tem 2021

Yayın serisi

AdıInternational Geoscience and Remote Sensing Symposium (IGARSS)
Hacim2021-July

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???event.eventtypes.event.conference???2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Ülke/BölgeBelgium
ŞehirBrussels
Periyot12/07/2116/07/21

Bibliyografik not

Publisher Copyright:
© 2021 IEEE.

Finansman

∗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.

FinansörlerFinansör numarası
Istanbul Teknik Üniversitesi

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