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2019 AAAI AAAI 2019

Novelty Detection for Multispectral Images with Application to Planetary Exploration

Abstract

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.

🚀 Conference Pioneer — AAAI 2019
🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning and Machine Learning
🧭 Keyword Pioneer — anomaly classification
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Hannah R Kerner , Danika F Wellington , Kiri L Wagstaff , James F Bell , Chiman Kwan , Heni Ben Amor

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Unsupervised Learning Deep Learning > Architectures > Autoencoders Computer Vision > Analysis > Anomaly Detection Computer Vision > Domain-Specific > Remote Sensing

Keywords

anomaly detection multispectral imaging novelty detection reconstruction error multispectral image convolutional autoencoder anomaly classification planetary exploration
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