  Classifying and segmenting patterns from a limited number of examples is a
significant challenge in remote sensing and earth observation due to the
difficulty in acquiring accurately labeled data in large quantities. Previous
studies have shown that meta-learning, which involves episodic training on
query and support sets, is a promising approach. However, there has been little
attention paid to direct fine-tuning techniques. This paper repurposes
contrastive learning as a pre-training method for few-shot learning for
classification and semantic segmentation tasks. Specifically, we introduce a
generator-based contrastive learning framework (GenCo) that pre-trains
backbones and simultaneously explores variants of feature samples. In
fine-tuning, the auxiliary generator can be used to enrich limited labeled data
samples in feature space. We demonstrate the effectiveness of our method in
improving few-shot learning performance on two key remote sensing datasets:
Agriculture-Vision and EuroSAT. Empirically, our approach outperforms purely
supervised training on the nearly 95,000 images in Agriculture-Vision for both
classification and semantic segmentation tasks. Similarly, the proposed
few-shot method achieves better results on the land-cover classification task
on EuroSAT compared to the results obtained from fully supervised model
training on the dataset.
