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.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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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)
- Creator
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Senaras, C.
Holden, P.
Davis, T.
Rana, A. S.
Grady, M.
Wania, A.
de Jeu, R.
- DOI
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10.5194/isprs-archives-XLVIII-M-1-2023-309-2023
- URN
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urn:nbn:de:101:1-2023042708143240099098
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:53 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Senaras, C.
- Holden, P.
- Davis, T.
- Rana, A. S.
- Grady, M.
- Wania, A.
- de Jeu, R.
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