Multimed Tools Appl DOI 10.1007/s11042-017-4952-y Multiple features learning for ship classification in optical imagery Longhui Huang 1 · Wei Li 1 · Chen Chen 2 · Fan Zhang 1 · Haitao Lang 3 Received: 21 November 2016 / Revised: 10 May 2017 / Accepted: 14 June 2017 © Springer Science+Business Media, LLC 2017 Abstract The sea surface vessel/ship classification is a challenging problem with enor- mous implications to the world’s global supply chain and militaries. The problem is similar to other well-studied problems in object recognition such as face recognition. However, it is more complex since ships’ appearance is easily affected by external factors such as lighting or weather conditions, viewing geometry and sea state. The large within-class variations in some vessels also make ship classification more complicated and challenging. In this paper, we propose an effective multiple features learning (MFL) framework for ship classification, which contains three types of features: Gabor-based multi-scale completed local binary pat- terns (MS-CLBP), patch-based MS-CLBP and Fisher vector, and combination of Bag of visual words (BOVW) and spatial pyramid matching (SPM). After multiple feature learning, feature-level fusion and decision-level fusion are both investigated for final classification. In the proposed framework, typical support vector machine (SVM) classifier is employed This work was supported by the National Key Research and Development Program of China under Grant 2016YFB0501501, and partly by the Higher Education and High-Quality and World-Class Universities under Grant PY201619.  Wei Li liwei089@ieee.org Chen Chen chenchen@crcv.ucf.edu Fan Zhang zhangf@mail.buct.edu.cn Haitao Lang langht@mail.buct.edu.cn 1 College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China 2 Center for Research in Computer Vision, University of Central Florida, Orlando, FL, USA 3 Faculty of Science, Beijing University of Chemical Technology, Beijing 100029, China