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.

在船舶分类中，选择独特的特征并设计适当的分类器是该过程的两个关键点。由于缺乏大多数研究，因此将这两个要点分别考虑。在这项研究中，我们的建议包括联合特征提取，选择和分类器设计框架，以构建用于船舶分类的新型深层级联网络。我们提出使用级联卷积神经网络体系结构将基于输入学习的深度特征提取，以将输入图像转换为多维特征图。基于MUTual信息（MUTInf）的特征选择算法的分布构成了源自公共船舶图像数据集的独特特征集。该数据集由海洋领域中最存在的五种特定类别的船舶组成。基于二次核的非线性支持向量机是设计的分类器。在基准数据集上进行的大量实验表明，所提出的框架可以集成最佳特征集和精心设计的分类器，以提高船舶图像分类过程的性能。在实验中，提出的方法实现了95.06％的整体精度。船级在货物，军事，航母，邮轮和油轮上的表现也很高，准确度分别为88.26％，98.38％，98.38％，98.78％和91.50％。此外，MUTInf功能选择以50.04％的比率减少功能。这些结果表明，所提出的方法以较少的元素数提供了最高的性能值，并且优于最新方法。