Scientific publications
Mbietieu Amos Mb.* (Corresponding author)
Other affiliations: Sorbonne Université · Unité de Modélisation Mathématique et Informatique des Systèmes Complexes · Institut de Recherche pour le Développement
T. M.
Other affiliations: Sorbonne Université · Unité de Modélisation Mathématique et Informatique des Systèmes Complexes · Institut de Recherche pour le Développement
Kouamou Georges Edouard
Other affiliations: Sorbonne Université · Unité de Modélisation Mathématique et Informatique des Systèmes Complexes · Institut de Recherche pour le Développement
Science of Remote Sensing
Advancements in remote sensing and artificial intelligence are transforming maritime object detection in High-Resolution Satellite Imagery (HRSI), with critical applications in environmental monitoring, maritime security, and sustainable ocean management. However, detecting ships in HRSI remains challenging due to variations in object size, orientation, and environmental conditions. This study introduces Ship Feature Pyramid Network (ShipFPN), a novel deep learning architecture that integrates spectral convolutions, adaptive feature pyramids, and robust data augmentation techniques to enhance detection accuracy and generalizability. ShipFPN is evaluated on the ShipRSImageNet dataset, demonstrating significant improvements over existing FPN-based models in precision, recall, and Generalized Intersection over Union (GIoU). While this study focuses on ship detection, ShipFPN’s architecture is designed to be adaptable for detecting other maritime objects. By improving the accuracy and robustness of ship detection, this work contributes to enhanced marine traffic monitoring, environmental protection, and climate resilience in satellite-based remote sensing. • ShipFPN improves detection of small ships in high-resolution satellite imagery. • Our FPN architecture uses adaptive fusion and attention-based enhancement. • The model achieves higher mAP on ShipRSImageNet than classic FPN baselines.
