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Detection of Ship from Satellite Images Using Deep Convolutional Neural Networks with Improved Median Filter

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Book cover Artificial Intelligence Techniques for Satellite Image Analysis

Part of the book series: Remote Sensing and Digital Image Processing ((RDIP,volume 24))

Abstract

Detection of maritime object is of greater attention in the field of satellite image processing applications in order to ensure the security and traffic control. Even though several approaches were built in the past few years, still it requires proper revamp in the architecture to focus toward the reduction of barriers to improve the performance of ship identification or appropriate vessel detection. The inference due to cluttered scenes, clouds, and islands in between the ocean is the greater challenge during the classification of ship or vessel. In this paper, we proposed a novel ship detection method called deep neural method which works very faster and based on the concept on deep learning methodology. Experimental results provide the better accuracy, and time complexity also reduces little further when compared to the traditional method.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    S. Iwin Thanakumar Joseph & D. Sujitha Juliet

  2. Department of Information Technology, Annamalai University, Chidambaram, Tamil Nadu, India

    J. Sasikala

Authors
  1. S. Iwin Thanakumar Joseph
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  2. J. Sasikala
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  3. D. Sujitha Juliet
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Corresponding author

Correspondence to S. Iwin Thanakumar Joseph .

Editor information

Editors and Affiliations

  1. Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

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Joseph, S.I.T., Sasikala, J., Juliet, D.S. (2020). Detection of Ship from Satellite Images Using Deep Convolutional Neural Networks with Improved Median Filter. In: Hemanth, D. (eds) Artificial Intelligence Techniques for Satellite Image Analysis. Remote Sensing and Digital Image Processing, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-24178-0_4

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