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Authors: Raffaele Imbriaco ; Tunc Alkanat ; Egor Bondarev and Peter H. N.  de With

Affiliation: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven 5612AZ, The Netherlands

Keyword(s): Content-based Image Retrieval, Remote Sensing, Convolutional Neural Networks, Local Feature Extraction.

Abstract: Context-based remote sensing image retrieval (CBRSIR) is an important problem in computer vision with many applications such as military, agriculture, and surveillance. In this study, inspired by recent developments in person re-identification, we design and fine-tune a multi-branch deep learning architecture that combines global and local features to obtain rich and discriminative image representations. Additionally, we propose a new evaluation strategy that fully separates the test and training sets and where new unseen data is used for querying, thereby emphasizing the generalization capability of retrieval systems. Extensive evaluations show that our method significantly outperforms the existing approaches by up to 10.7% in mean precision@20 on popular CBRSIR datasets. Regarding the new evaluation strategy, our method attains excellent retrieval performance, yielding more than 95% precision@20 score on the challenging PatternNet dataset.

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Paper citation in several formats:
Imbriaco, R.; Alkanat, T.; Bondarev, E. and de With, P. (2020). Multi-Branch Convolutional Descriptors for Content-based Remote Sensing Image Retrieval. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2; ISSN 2184-4321, SciTePress, pages 242-249. DOI: 10.5220/0008895702420249

@conference{visapp20,
author={Raffaele Imbriaco. and Tunc Alkanat. and Egor Bondarev. and Peter H. N. {de With}.},
title={Multi-Branch Convolutional Descriptors for Content-based Remote Sensing Image Retrieval},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={242-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008895702420249},
isbn={978-989-758-402-2},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Multi-Branch Convolutional Descriptors for Content-based Remote Sensing Image Retrieval
SN - 978-989-758-402-2
IS - 2184-4321
AU - Imbriaco, R.
AU - Alkanat, T.
AU - Bondarev, E.
AU - de With, P.
PY - 2020
SP - 242
EP - 249
DO - 10.5220/0008895702420249
PB - SciTePress

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