With the rapid growth of the marine economy and the increasing demand for maritime security, ship target detection has become critically important in both military and civilian applications. However, in complex remote sensing scenarios, challenges such as visual similarity among ships, subtle inter-class differences, and the continual emergence of new categories make traditional closed-world detection methods inadequate. To address these issues, this paper proposes an open-world detection framework for remote sensing ships. The framework integrates two key modules: (1) a Fine-Grained Feature and Extreme Value-based Unknown Recognition (FEUR) module, which leverages tail distribution modeling and adaptive thresholding to achieve precise detection and effective differentiation of unknown ship targets; and (2) a Joint Optimization-based Incremental Learning (JOIL) module, which employs hierarchical elastic weight constraints to differentially update the backbone and detection head, thereby alleviating catastrophic forgetting while incorporating new categories with only a few labeled samples. Extensive experiments on the FGSRCS dataset demonstrate that the proposed method not only maintains high accuracy on known categories but also significantly outperforms mainstream open-world detection approaches in unknown recognition and incremental learning. This work provides both theoretical value and practical potential for continuous ship detection and recognition in complex open environments.

Anti-UAV multi-object tracking in remote sensing videos is challenging because UAV targets are small, weakly textured, and often affected by cluttered backgrounds, abrupt motion, occlusion, and intermittent visibility. To address these challenges, we formulate anti-UAV multi-object tracking as a context-aware identity prediction task, in which target identities and locations are inferred from historical trajectory priors instead of current-frame observations alone. Under this formulation, we propose a dual-track parallel tracking framework. The adaptive identity disambiguation (AID) module combines motion cues with appearance features according to their estimated reliability, improving short-term association when visual evidence is weak. In parallel, the motion-evolution temporal memory (METM) module models trajectory dynamics using motion anomaly detection and time-decayed memory, enabling spatiotemporal recovery after occlusion, temporary disappearance, or abrupt motion. The outputs of the two branches are integrated by a unified identity decision layer to produce stable tracking results. Experiments are conducted on the public 4th Anti-UAV Benchmark Track-3 and our newly constructed Anti-UAV Multi-Object Tracking dataset, AU-MOT. On the 4th Anti-UAV Benchmark Track-3, our method achieves 63.6% HOTA and 64.1% IDF1, outperforming the strongest competing method by 3.5% and 3.9%, respectively, while reducing identity switches and track fragments by 20.8% and 23.8%. On AU-MOT, it achieves 67.2% HOTA and 67.8% IDF1, with 20.2% fewer identity switches and 22.3% fewer track fragments. These results demonstrate its effectiveness under long-range observation, weak target appearance, cluttered backgrounds, abrupt motion, and intermittent target visibility.