Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas

Authors

  • Kiri Wagstaff California Institute of Technology
  • You Lu California Institute of Technology
  • Alice Stanboli California Institute of Technology
  • Kevin Grimes California Institute of Technology
  • Thamme Gowda California Institute of Technology; Information Sciences Institute, University of Southern California
  • Jordan Padams California Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v32i1.11404

Keywords:

machine learning, image classification, planetary science

Abstract

NASA has acquired more than 22 million images from the planet Mars. To help users find images of interest, we developed a content-based search capability for Mars rover surface images and Mars orbital images. We started with the AlexNet convolutional neural network, which was trained on Earth images, and used transfer learning to adapt the network for use with Mars images. We report on our deployment of these classifiers within the PDS Imaging Atlas, a publicly accessible web interface, to enable the first content-based image search for NASA’s Mars images.

AAAI 2018 Proceedings Cover

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Published

2018-04-27

How to Cite

Wagstaff, K., Lu, Y., Stanboli, A., Grimes, K., Gowda, T., & Padams, J. (2018). Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). https://doi.org/10.1609/aaai.v32i1.11404

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

IAAI18 - Emerging