期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 38, 期 23, 页码 6457-6476出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2017.1356487
关键词
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资金
- National Natural Science Foundation of China [61590921, 61603336]
- Zhejiang Province Natural Science Foundation [Y16B040003]
- Shanghai Aerospace Science and Technology Innovation Fund [E81502]
- Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation [E81601]
Ship classification based on synthetic aperture radar (SAR) images is a crucial component in maritime surveillance. In this article, the feature selection and the classifier design, as two key essential factors for traditional ship classification, are jointed together, and a novel ship classification model combining kernel extreme learning machine (KELM) and dragonfly algorithm in binary space (BDA), named BDA-KELM, is proposed which conducts the automatic feature selection and searches for optimal parameter sets (including the kernel parameter and the penalty factor) for classifier at the same time. Finally, a series of ship classification experiments are carried out based on high resolution TerraSAR-X SAR imagery. Other four widely used classification models, namely k-Nearest Neighbour (k-NN), Bayes, Back Propagation neural network (BP neural network), Support Vector Machine (SVM), are also tested on the same dataset. The experimental results shows that the proposed model can achieve a better classification performance than these four widely used models with an classification accuracy as high as 97% and encouraging results of other three multi-class classification evaluation metrics.
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