THE ADDED VALUE OF CYCLE-GAN FOR AGRICULTURE STUDIES

Ecre Sener*, Emre Çolak, Esra Erten, Gülsen Taskin

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

It is significant to monitor the phenological stages of agricultural crops with accurate and up-to-date information. In monitoring the phenological phases of some crops, optical remote sensing data offers significant spectral information and outstanding feature identification. However, a continuous time series of optical remote sensing data is difficult to obtain due to the weather dependency of optical acquisitions. In this paper, the feasibility of transfer learning between the features of Sentinel-1 and Sentinel-2 is evaluated to reduce these difficulties. A feature translation based on deep learning (DL) method, namely Cycle-Consistent Generative Adversarial Networks (cycle-GAN), was applied between Sentinel-1 and Sentinel-2 data. In order to evaluate the effect of the cycle-GAN method on crop type mapping and identification, Random Forest classification was applied to four different cases (Real SAR, Fake Optical + Real SAR, Real Optical, and Real Optical + Real SAR).

Original languageEnglish
Title of host publicationIGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages7039-7042
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

∗Thanks to Doktar for providing in-situ information. This work was supported by the Research Fund of the Istanbul Technical University, project Number:43018.

FundersFunder number
Istanbul Teknik Üniversitesi

    Keywords

    • Agriculture
    • Consistent adversarial networks
    • Cycle-GAN
    • Random forest classification
    • Sentinel-1
    • Sentinel-2

    Fingerprint

    Dive into the research topics of 'THE ADDED VALUE OF CYCLE-GAN FOR AGRICULTURE STUDIES'. Together they form a unique fingerprint.

    Cite this