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Image-based Novelty Detection for Lunar Exploration


Image-based Novelty Detection for Lunar Exploration

Stefanuk, Braden (2021) Image-based Novelty Detection for Lunar Exploration. Masters thesis, Concordia University.

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With investment in lunar exploration growing, the landscape is rich with opportunities to build faster, better, more reliable systems. This work focuses on the crucial yet under-studied problem of image-based novelty detection. Applications within this domain are far-reaching, already having shown potential to alleviate data transmission constraints, accelerate the pace of operations, and increase the overall science return of
exploration missions. As the field matures, novelty detection will play an essential role in the development of fully autonomous science platforms.

In the first part of this work, we advance the foundations of novelty detection for lunar exploration by developing a large, fully labelled, high-fidelity lunar analogue dataset. This dataset contains features and lighting conditions found across the Moon’s surface including fresh and degraded craters, rock fields, boulders, outcrops, and hills. It also contains various novelties including volcanic rocks, pyroclastic deposits, and exposed bedrock.

In the second part of this work, we establish new approaches to novelty detection for image data. Developing and experimenting with various models and datasets, we introduce adversarial autoencoders into the field of planetary novelty detection and build state-of-the-art variational and convolutional autoencoders, beating comparable state-of-the-art methods by >7%. In conjunction with the models themselves, we develop a framework to integrate region proposals with novelty detection to annotate novel loci
with bounding boxes. We also extend the scope of novelty scoring measures by operating directly on low-dimensional representations of image data.

In the final part of this work, we synthesize our findings into the context of lunar exploration and discuss key outcomes and future work.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Concordia University > Research Units > Concordia Institute for Aerospace Design and Innovation
Item Type:Thesis (Masters)
Authors:Stefanuk, Braden
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:7 July 2021
Thesis Supervisor(s):Skonieczny, Krzysztof
ID Code:988786
Deposited By: Braden Stefanuk
Deposited On:29 Nov 2021 16:44
Last Modified:29 Nov 2021 16:44
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