FedShip: Federated Learning for Ship Detection From Multi-Source Satellite Images

Detecting ships from satellite imagery is vital for maritime surveillance. Most current methods rely on deep learning (DL), which requires a large number of high-quality annotated images to train accurate models. Since satellite imagery comes from various sensors, DL-based ship detection algorithms need to perform well across different sensor types. However, privacy concerns, especially with commercial images, limit data, and annotation sharing. Federated learning (FL) offers a promising solution for collaborative learning while addressing these concerns. Despite its potential, research on FL for ship detection is still sparse. This study implements and evaluates three FL models for detecting ships using multi-source optical satellite images, spanning high to low resolution. Our experiments on two distinct datasets demonstrate that FL models significantly enhance detection performance without centralizing data. Source codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ffyyytt/FLYOLO</uri>.