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

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.