A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines

Zikun Liu, Liu Yuan, Lubin Weng, Yiping Yang

Abstract

Ship recognition in high-resolution optical satellite images is an important task. However, it is difficult to recognize ships under complex backgrounds, which is the main bottleneck for ship recognition and needs to be further explored and researched. As far as we know, there is no public remote sensing ship dataset and few open source work. To facilitate future ship recognition related research, in this paper, we present a public high-resolution ship dataset, ``HRSC2016'', that covers not only bounding-box labeling way, but also rotated bounding box way with three-level classes including ship, ship category and ship types. We also provide the ship head position for all the ships with ``V'' shape heads and the segmentation mask for every image in ``Test set''. Besides, we volunteer a ship annotation tool and some development tools. Given these rich annotations we perform a detailed analysis of some state-of-the-art methods, introduce a novel metric, the separation fitness (SF), that is used for evaluating the performance of the sea-land segmentation task and we also build some new baselines for recognition. The latest dataset can be downloaded from ``http://www.escience.cn/people/liuzikun/DataSet.html''.

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Paper Citation


in Harvard Style

Liu Z., Yuan L., Weng L. and Yang Y. (2017). A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 324-331. DOI: 10.5220/0006120603240331


in Bibtex Style

@conference{icpram17,
author={Zikun Liu and Liu Yuan and Lubin Weng and Yiping Yang},
title={A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={324-331},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006120603240331},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselines
SN - 978-989-758-222-6
AU - Liu Z.
AU - Yuan L.
AU - Weng L.
AU - Yang Y.
PY - 2017
SP - 324
EP - 331
DO - 10.5220/0006120603240331