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

Kiri L. Wagstaff, You Lu, Alice Stanboli, Kevin Grimes, Thamme Gowda, Jordan Padams

AAAI 2018 pp. 7867-7872

doi:10.1609/AAAI.V32I1.11404 /aaai/2018/wagstaff2018aaai-deep/

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.

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Text

Wagstaff et al. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.11404

Markdown

[Wagstaff et al. "Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/wagstaff2018aaai-deep/) doi:10.1609/AAAI.V32I1.11404

BibTeX

@inproceedings{wagstaff2018aaai-deep,
  title     = {{Deep Mars: CNN Classification of Mars Imagery for the PDS Imaging Atlas}},
  author    = {Wagstaff, Kiri L. and Lu, You and Stanboli, Alice and Grimes, Kevin and Gowda, Thamme and Padams, Jordan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {7867-7872},
  doi       = {10.1609/AAAI.V32I1.11404},
  url       = {https://mlanthology.org/aaai/2018/wagstaff2018aaai-deep/}
}