A novel ship classification approach for high resolution SAR images based on the BDA-KELM classification model

ABSTRACT

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.

Acknowledgements

This work is supported by National Natural Science Foundation of China (grant numbers 61590921 and 61603336), Zhejiang Province Natural Science Foundation (Y16B040003), Shanghai Aerospace Science and Technology Innovation Fund (E81502) and Aerospace Science and Technology Innovation Fund of China Aerospace Science and Technology Corporation (E81601), and their supports are thereby acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the 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];