Multi-Modal Self-Supervised Learning for Boosting Crop Classification Using Sentinel2 and Planetscope
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.
- Published in:
IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium - Type:
Inproceedings - Authors:
Patnala, Ankit; Stadtler, Scarlet; Schultz, Martin G.; Gall, Jürgen - Year:
2024 - Source:
https://ieeexplore.ieee.org/abstract/document/10282665
Citation information
Patnala, Ankit; Stadtler, Scarlet; Schultz, Martin G.; Gall, Jürgen: Multi-Modal Self-Supervised Learning for Boosting Crop Classification Using Sentinel2 and Planetscope, IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 2024, https://ieeexplore.ieee.org/abstract/document/10282665, Patnala.etal.2024a,
@Inproceedings{Patnala.etal.2024a,
author={Patnala, Ankit; Stadtler, Scarlet; Schultz, Martin G.; Gall, Jürgen},
title={Multi-Modal Self-Supervised Learning for Boosting Crop Classification Using Sentinel2 and Planetscope},
booktitle={IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium},
url={https://ieeexplore.ieee.org/abstract/document/10282665},
year={2024},
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...}}
