@inproceedings{f54f8a05e4dd4a7f928f30dc0edfa085,
title = "Improving Generalization for Few-Shot Remote Sensing Classification with Meta-Learning",
abstract = "In Remote Sensing (RS) classification, generalization ability is one of the measure that characterizes the success of Machine Learning (ML) models, but is often impeded by the scarse availability of annotated training data. Annotated RS samples are expensive to obtain and can present large disparities when produced by different annotators. In this paper, we utilize Few-Shot Learning (FSL) with meta-learning to address the challenge of generalization using limited amount of training information. The data used in this paper is leveraged from different datasets that have diverse distributions, that means distinct feature spaces. We tested our approach on publicly available RS benchmark datasets to perform few-shot RS image classification using meta-learning. The results of the experiments suggest that our approach is able to generalize well on the unseen data even with limited number of training samples and reasonable training time.",
keywords = "Few-shot learning, classification, deep learning, meta-learning, remote sensing",
author = "Surbhi Sharma and Ribana Roscher and Morris Riedel and Shahbaz Memon and Gabriele Cavallaro",
note = "Funding Information: Part of this work was performed in the ADMIRE project, which received funding from the European Union{\textquoteright}s Horizon 2020 JTI-EuroHPC research and innovation programme under the grant agreement no. 956748. The authors gratefully acknowledge the computing resources from the DEEP-EST project, which received funding from the European Union{\textquoteright}s Horizon 2020 research and innovation programme under the grant agreement no. 754304. The work is partly funded by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab ”AI4EO – Artificial Intelligence for Earth Observation: Reasoning, Uncertainties, Ethics and Beyond” (Grant number: 01DD20001). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884699",
language = "English",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5061--5064",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
address = "United States",
}