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Deep Learning-Based Man-Made Object Detection from Hyperspectral Data

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Advances in Visual Computing (ISVC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9474))

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Abstract

Hyperspectral sensing, due to its intrinsic ability to capture the spectral responses of depicted materials, provides unique capabilities towards object detection and identification. In this paper, we tackle the problem of man-made object detection from hyperspectral data through a deep learning classification framework. By the effective exploitation of a Convolutional Neural Network we encode pixels’ spectral and spatial information and employ a Multi-Layer Perceptron to conduct the classification task. Experimental results and the performed quantitative validation on widely used hyperspectral datasets demonstrating the great potentials of the developed approach towards accurate and automated man-made object detection.

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Acknowledgements

This research was funded from European Unions FP7 under grant agreement n.313161, eVACUATE Project (www.evacuate.eu)

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Authors and Affiliations

  1. Technical University of Crete, University Campus, 73100, Kounoupidiana, Chania, Greece

    Konstantinos Makantasis

  2. National Technical University of Athens, Zografou Campus, 15780, Athens, Greece

    Konstantinos Karantzalos & Anastasios Doulamis

  3. Institute of Communication and Computer Systems, Athens, Greece

    Konstantinos Loupos

Authors
  1. Konstantinos Makantasis
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  2. Konstantinos Karantzalos
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  3. Anastasios Doulamis
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  4. Konstantinos Loupos
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Corresponding author

Correspondence to Konstantinos Makantasis .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, Nevada, USA

    Darko Koracin

  5. University of Houston, Houston, Texas, USA

    Ioannis Pavlidis

  6. IBM T.J. Watson Research Center, Yorktown Heights, New York, USA

    Rogerio Feris

  7. Purdue University, West Lafayette, Indiana, USA

    Tim McGraw

  8. Side Effects Software, Santa Monica, California, USA

    Mark Elendt

  9. The DiVE, Durham, North Carolina, USA

    Regis Kopper

  10. Texas A&M University, College Station, Texas, USA

    Eric Ragan

  11. Kent State University, Kent, Ohio, USA

    Zhao Ye

  12. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Gunther Weber

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Makantasis, K., Karantzalos, K., Doulamis, A., Loupos, K. (2015). Deep Learning-Based Man-Made Object Detection from Hyperspectral Data. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_64

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  • DOI: https://doi.org/10.1007/978-3-319-27857-5_64

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27856-8

  • Online ISBN: 978-3-319-27857-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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