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

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Abstract

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.

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The processed dataset is already available in public and properly referenced in the bibliography list.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Firat University, Elazig, 23119, Turkey

    Ferhat Ucar

  2. Department of Electrical and Electronics Engineering, Malatya Turgut Ozal University, Malatya, 44210, Turkey

    Deniz Korkmaz

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  1. Ferhat Ucar
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  2. Deniz Korkmaz
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Correspondence to Ferhat Ucar.

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Ucar, F., Korkmaz, D. A novel ship classification network with cascade deep features for line-of-sight sea data. Machine Vision and Applications 32, 73 (2021). https://doi.org/10.1007/s00138-021-01198-2

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  • DOI: https://doi.org/10.1007/s00138-021-01198-2

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