Few-Shot Object Detection (FSOD) allows fast adaptation of an object detection model to new classes of objects using few examples per class. This has many applications, in particular in satellite and aerial observation, as it allows learning from experts who can only annotate a few examples for new classes and helps migrate models across tasks. In this work, we present a technique to improve the performance of FSOD in remote sensing by defining a contrastive loss that utilizes parts of objects. For this, we generate, what we call, Object Parts Proposals (OPPs) on the fly for each novel class, and use them to learn more robust features with an additional contrastive objective. We observe that training with OPPs brings a consistent improvement over the state-of-the-art when evaluating on the DIOR dataset.The code is available at https://github.com/arthurchevalley/Improving-FSOD-on-RSI-using-Sub-Parts.