摘要 : Ship detection from space-borne SAR image is a challenging task. The main challenges in this task include the disturbance of sea clutter, island, coastline, and variability in ship sizes. Here, the authors propose a novel hierarch... 展开 Ship detection from space-borne SAR image is a challenging task. The main challenges in this task include the disturbance of sea clutter, island, coastline, and variability in ship sizes. Here, the authors propose a novel hierarchical ship detection framework to overcome the above challenges. Initially, the edge-enhanced maximally stable extremal regions (E-MSER) is employed as ship candidates. Then, through some simple shape analysis, they can eliminate the obvious false alarms which are not conformed to the shape feature of ship. Finally, candidates are represented by an effective multiple features learning framework for discriminating true ship targets. Experimental results on numerous space-borne SAR images demonstrate their proposed scheme that outperforms other traditional methods with higher detection accuracy and lower false alarm. 收起