Novelty Detection for Multispectral Images with Application to Planetary Exploration
Abstract
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.
Cite
Text
Kerner et al. "Novelty Detection for Multispectral Images with Application to Planetary Exploration." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33019484Markdown
[Kerner et al. "Novelty Detection for Multispectral Images with Application to Planetary Exploration." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/kerner2019aaai-novelty/) doi:10.1609/AAAI.V33I01.33019484BibTeX
@inproceedings{kerner2019aaai-novelty,
title = {{Novelty Detection for Multispectral Images with Application to Planetary Exploration}},
author = {Kerner, Hannah Rae and Wellington, Danika F. and Wagstaff, Kiri L. and Bell, James F. and Kwan, Chiman and Amor, Heni Ben},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2019},
pages = {9484-9491},
doi = {10.1609/AAAI.V33I01.33019484},
url = {https://mlanthology.org/aaai/2019/kerner2019aaai-novelty/}
}