Multi-Modal Self-Supervised Learning for Boosting Crop Classification Using Sentinel2 and Planetscope

Patnala, Ankit; Gall, Juergen; Stadtler, Scarlet; Schultz, Martin G.

doi:10.1109/IGARSS52108.2023.10282665

Contribution to a conference proceedings/Contribution to a book

FZJ-2024-03129

Multi-Modal Self-Supervised Learning for Boosting Crop Classification Using Sentinel2 and Planetscope

Patnala, A.FZJ* ; Stadtler, S.FZJ* ; Schultz, M. G.FZJ* ; Gall, J.

2023
IEEE

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, 16 Jul 2023 - 21 Jul 2023 IEEE 2223 - 2226 (2023) [10.1109/IGARSS52108.2023.10282665]

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Please use a persistent id in citations: doi:10.1109/IGARSS52108.2023.10282665 doi:10.34734/FZJ-2024-03129

Abstract: Remote 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.

Note: ISBN: 979-8-3503-2010-7

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