A novel ship classification network with cascade deep features for line-of-sight sea data

In ship classification, selecting distinctive features and designing a proper classifier are two key points of the process. As a lack of most of the studies, these two essential points are considered separately. In this study, our proposal includes joint feature extraction, selection, and classifier design framework to build a novel deep cascade network for ship classification. We propose a transfer learning-based deep feature extraction using cascade Convolutional Neural Network architecture to convert the input image to multi-dimensional feature maps. The distributions of the MUTual Information (MUTInf) based feature selection algorithm compose a distinctive feature set originated for a public ship imagery dataset. The dataset consists of five specific classes of ships most existed in the maritime domain. A quadratic kernel-based non-linear Support Vector Machine is the designed classifier. Extensive experiments on the benchmark dataset indicate that the proposed framework can integrate the optimal feature set and a well-designed classifier to increase the performance of the classification process in ship imagery. In the experiments, the proposed method achieves an overall accuracy of 95.06%. The ship classes are also performed high classification performances into cargo, military, carrier, cruise, and tanker with an accuracy of 88.26%, 98.38%, 98.38%, 98.78%, and 91.50%, respectively. In addition, MUTInf feature selection reduces the features at a rate of 50.04%. These results show that the proposed method provides the highest performance value with less number of elements and outperforms state-of-the-art methods.