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001025761 1001_ $$0P:(DE-Juel1)186635$$aPatnala, Ankit$$b0$$ufzj
001025761 1112_ $$aIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium$$cPasadena$$d2023-07-16 - 2023-07-21$$wCA
001025761 245__ $$aMulti-Modal Self-Supervised Learning for Boosting Crop Classification Using Sentinel2 and Planetscope
001025761 260__ $$bIEEE$$c2023
001025761 29510 $$aIGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
001025761 300__ $$a2223 - 2226
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001025761 520__ $$aRemote sensing has enabled large-scale crop classification to understand agricultural ecosystems and estimate production yields. Since few years, machine learning is increasingly used for automated crop classification. However, most approaches apply novel algorithms to custom datasets containing information of few crop fields covering a small region and this often leads to poor models that lack generalization capability. Therefore in this work, inspired from the self-supervised learning approaches, we devised and compared different approaches for contrastive self-supervised learning using Sentinel2 and Planetscope data for crop classification. In addition, based on the dataset DENETHOR, we assembled our own dataset for the experiments.
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001025761 7001_ $$0P:(DE-Juel1)180752$$aStadtler, Scarlet$$b1$$ufzj
001025761 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin G.$$b2$$ufzj
001025761 7001_ $$0P:(DE-HGF)0$$aGall, Juergen$$b3
001025761 773__ $$a10.1109/IGARSS52108.2023.10282665
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