SELF-SUPERVISED LEARNING FOR CROP CLASSIFICATION USING PLANET FUSION

Abstract. Benefiting from the high cadence and spatial resolution of the new generation of Earth observation satellites, remote sensing technology is allowing us to derive more valuable information for the agricultural sector. Crop classification is one of the fundamental information derivatives from Earth observation data researchers used for food security, crop monitoring, and economic assessment. The robustness of a crop classification model to variations in environmental and management conditions due to time and location is one of the crucial requirements. To achieve this, we developed a novel self-supervised method using the advantage of unlabeled samples and transformer architectures. We used six different areas in Germany and four years to evaluate the robustness of the model. Our experiments showed that self-supervised deep learning methods could provide a significant advantage in handling these variations. In some cases, we observed around 30 percentage points improvements in F1-score performance compared to a Random Forest based model.

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch

Erschienen in
SELF-SUPERVISED LEARNING FOR CROP CLASSIFICATION USING PLANET FUSION ; volume:XLVIII-M-1-2023 ; year:2023 ; pages:309-315 ; extent:7
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLVIII-M-1-2023 (2023), 309-315 (gesamt 7)

Urheber
Senaras, C.
Holden, P.
Davis, T.
Rana, A. S.
Grady, M.
Wania, A.
de Jeu, R.

DOI
10.5194/isprs-archives-XLVIII-M-1-2023-309-2023
URN
urn:nbn:de:101:1-2023042708143240099098
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
14.08.2025, 10:53 MESZ

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Beteiligte

  • Senaras, C.
  • Holden, P.
  • Davis, T.
  • Rana, A. S.
  • Grady, M.
  • Wania, A.
  • de Jeu, R.

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