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Feature Learning HyperNEAT: Evolving Neural Networks to Extract Features for Classification of Maritime Satellite Imagery

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Book cover Information Processing in Cells and Tissues (IPCAT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9303))

Abstract

Imagery analysis represents a significant aspect of maritime domain awareness; however, the amount of imagery is exceeding human capability to process. Unfortunately, the maritime domain presents unique challenges for machine learning to automate such analysis. Indeed, when object recognition algorithms observe real-world data, they face hurdles not present in experimental situations. Imagery from such domains suffers from degradation, have limited examples, and vary greatly in format. These limitations are present satellite imagery because of the associated constraints in expense and capability. To this end, the Hypercube-based NeuroEvolution of Augmenting Topologies approach is investigated in addressing some such challenges for classifying maritime vessels from satellite imagery. Results show that HyperNEAT learns features from such imagery that allows better classification than Principal Component Analysis (PCA). Furthermore, HyperNEAT enables a unique capability to scale image sizes through the indirect encoding.

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Acknowledgments

This work was supported and funded by the SSC Pacific Naval Innovative Science and Engineering (NISE) Program.

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Authors and Affiliations

  1. SPAWAR Systems Center Pacific, 53560 Hull Street, San Diego, CA, USA

    Phillip Verbancsics & Josh Harguess

Authors
  1. Phillip Verbancsics
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  2. Josh Harguess
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Corresponding author

Correspondence to Phillip Verbancsics .

Editor information

Editors and Affiliations

  1. Heriot-Watt University, Edinburgh, United Kingdom

    Michael Lones

  2. University of York, York, United Kingdom

    Andy Tyrrell

  3. University of York, York, United Kingdom

    Stephen Smith

  4. Natural Selection, Inc., San Diego, California, USA

    Gary Fogel

A Result Standard Deviations

A Result Standard Deviations

Table 3. Standard deviation of training and testing classification performance by normalization approach.
Full size table

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Verbancsics, P., Harguess, J. (2015). Feature Learning HyperNEAT: Evolving Neural Networks to Extract Features for Classification of Maritime Satellite Imagery. In: Lones, M., Tyrrell, A., Smith, S., Fogel, G. (eds) Information Processing in Cells and Tissues. IPCAT 2015. Lecture Notes in Computer Science(), vol 9303. Springer, Cham. https://doi.org/10.1007/978-3-319-23108-2_18

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  • DOI: https://doi.org/10.1007/978-3-319-23108-2_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23107-5

  • Online ISBN: 978-3-319-23108-2

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