Hierarchical ship detection method for space-borne SAR image

Abstract

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.

Keywords

• ships
• object detection
• image representation
• image classification
• synthetic aperture radar
• feature extraction
• radar imaging
• learning (artificial intelligence)
• numerous space-borne sar images
• higher detection accuracy
• hierarchical ship detection method
• space-borne sar image
• ship sizes
• novel hierarchical ship detection framework
• edge-enhanced maximally stable extremal regions
• ship candidates
• simple shape analysis
• ship targets