Novelty Detection for Multispectral Images with Application to Planetary Exploration


  • Hannah R Kerner Arizona State University
  • Danika F Wellington Arizona State University
  • Kiri L Wagstaff California Institute of Technology
  • James F Bell Arizona State University
  • Chiman Kwan Arizona State University
  • Heni Ben Amor Arizona State University



In this work, we present a system based on convolutional autoencoders for detecting novel features in multispectral images. We introduce SAMMIE: Selections based on Autoencoder Modeling of Multispectral Image Expectations. Previous work using autoencoders employed the scalar reconstruction error to classify new images as novel or typical. We show that a spatial-spectral error map can enable both accurate classification of novelty in multispectral images as well as human-comprehensible explanations of the detection. We apply our methodology to the detection of novel geologic features in multispectral images of the Martian surface collected by the Mastcam imaging system on the Mars Science Laboratory Curiosity rover.




How to Cite

Kerner, H. R., Wellington, D. F., Wagstaff, K. L., Bell, J. F., Kwan, C., & Ben Amor, H. (2019). Novelty Detection for Multispectral Images with Application to Planetary Exploration. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9484-9491.



IAAI Technical Track: Emerging Papers