A novel ship classification approach for high resolution SAR images based on the BDA-KELM classification model. Issue 23 (2nd December 2017)

• Record Type: Journal Article
• Authors: Wu, Jun; Zhu, Yu; Wang, Zhicheng; Song, Zhengji; Liu, Xinggao; Wang, Wenhai; Zhang, Zeyin; Yu, Yusheng; Xu, Zhipeng; Zhang, Tianjian; Zhou, Jiehan
• Is Part Of: International journal of remote sensing. Volume 38:Issue 23(2017)
• Page Start: 6457
• Page End: 6476
• Publication Date: 2017-12-02
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1356487
• Languages: English
• ISSNs: 0143-1161

Abstract

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.