Semantic Segmentation in Red Relief Image Map by UX-Net

Tomoya Komiyama, Kazuhiro Hotta, Kazuo Oda, Satomi Kakuta, Mikako Sano

Abstract

This paper proposes a semantic segmentation method in Red Relief Image Map which a kind of aerial laser image. We modify the U-Net by adding the paths between convolutional layer and deconvolutional layer with different resolution. By using the feature maps obtained at different layers, the segmentation accuracy is improved. We compare the segmentation accuracy of the proposed UX-Net with the original U-net. Our proposed method improved class-average accuracy in comparison with the U-Net.

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Paper Citation


in Harvard Style

Komiyama T., Hotta K., Oda K., Kakuta S. and Sano M. (2018). Semantic Segmentation in Red Relief Image Map by UX-Net.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 597-602. DOI: 10.5220/0006716805970602


in Bibtex Style

@conference{icpram18,
author={Tomoya Komiyama and Kazuhiro Hotta and Kazuo Oda and Satomi Kakuta and Mikako Sano},
title={Semantic Segmentation in Red Relief Image Map by UX-Net},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={597-602},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006716805970602},
isbn={978-989-758-276-9},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Semantic Segmentation in Red Relief Image Map by UX-Net
SN - 978-989-758-276-9
AU - Komiyama T.
AU - Hotta K.
AU - Oda K.
AU - Kakuta S.
AU - Sano M.
PY - 2018
SP - 597
EP - 602
DO - 10.5220/0006716805970602