Open AccessHierarchical ship detection method for space‐borne SAR image
Author(s) -
Tang Wei,
Zhao Baojun,
Tang Linbo,
Nan Jinghong
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0424
Subject(s) - computer science , clutter , false alarm , artificial intelligence , feature (linguistics) , image (mathematics) , task (project management) , pattern recognition (psychology) , feature vector , constant false alarm rate , enhanced data rates for gsm evolution , computer vision , space (punctuation) , remote sensing , radar , geography , telecommunications , engineering , linguistics , philosophy , systems engineering , operating system
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.