Inverse synthetic aperture radar (ISAR) is a common radar imaging technique used to characterize and classify non-cooperative targets. Traditional classification approaches use geometric features extracted from the images of known targets to form a training dataset that is later used to classify an unknown target. While these approaches work reasonably well, deep learning-based techniques have demonstrated significant improvements over conventional processing schemes in many areas of radar. However, the application of ISAR image classification is difficult when there are only small training datasets available. In this article, we address the small dataset problem by utilizing transfer learning. Different approaches are considered that can take advantage of the ship aspect angle to improve the overall stability and improve the final classification result. The new classification results are then compared with a traditional classification approach and a simple three-layer convolutional neural network. In addition, to better understand how the neural networks are working, saliency maps are used to visualize the trained network.


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    Title :

    Classification of ISAR Ship Imagery Using Transfer Learning


    Contributors:


    Publication date :

    2024-02-01


    Size :

    5909514 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Ship identification in sequential ISAR imagery

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    Automatic recognition of ISAR ship images

    Musman, S. / Kerr, D. / Bachmann, C. | Tema Archive | 1996


    Automatic recognition of ISAR ship images

    Musman, S. / Kerr, D. / Bachmann, C. | IEEE | 1996


    Automatic Recognition of ISAR Ship Images

    Musman, S. | Online Contents | 1996


    Automatic classification of infrared ship imagery

    Kovar, J.J. / Knecht, J. / Chenoweth, D. | Tema Archive | 1981