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Authors: Thorsten Wilhelm 1 ; Rene Grzeszick 2 ; Gernot A. Fink 2 and Christian Wöhler 1

Affiliations: 1 Image Analysis Group, Department of Electrical Engineering, TU Dortmund University, 44227 Dortmund and Germany ; 2 Pattern Recognition in Embedded Systems Group, Department of Computer Science, TU Dortmund Universtiy, 44227 Dortmund and Germany

Keyword(s): Remote Sensing, Scene Detection, CNN, Unsupervised Learning.

Abstract: Learning scene categories is a challenging task due to the high diversity of images. State-of-the-art methods are typically trained in a fully supervised manner, requiring manual labeling effort. In some cases, however, these manual labels are not available. In this work, an example of completely unlabeled scene images, where labels are hardly obtainable, is presented: orbital images of the lunar surface. A novel method that exploits feature representations derived from a CNN trained on a different data source is presented. These features are adapted to the lunar surface in an unsupervised manner, allowing for learning scene categories and detecting regions of interest. The experiments show that meaningful representatives and scene categories can be derived in a fully unsupervised fashion.

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Paper citation in several formats:
Wilhelm, T.; Grzeszick, R.; Fink, G. and Wöhler, C. (2019). Unsupervised Learning of Scene Categories on the Lunar Surface. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4; ISSN 2184-4321, SciTePress, pages 614-621. DOI: 10.5220/0007569506140621

@conference{visapp19,
author={Thorsten Wilhelm. and Rene Grzeszick. and Gernot A. Fink. and Christian Wöhler.},
title={Unsupervised Learning of Scene Categories on the Lunar Surface},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={614-621},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007569506140621},
isbn={978-989-758-354-4},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Unsupervised Learning of Scene Categories on the Lunar Surface
SN - 978-989-758-354-4
IS - 2184-4321
AU - Wilhelm, T.
AU - Grzeszick, R.
AU - Fink, G.
AU - Wöhler, C.
PY - 2019
SP - 614
EP - 621
DO - 10.5220/0007569506140621
PB - SciTePress

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