loading
Papers

Research.Publish.Connect.

Paper

Authors: Zikun Liu 1 ; Liu Yuan 2 ; Lubin Weng 3 and Yiping Yang 3

Affiliations: 1 Institute of Automation Chinese Academy of Sciences and University of Chinese Academy of Sciences, China ; 2 China Academy of Electronics and Information Technology, China ; 3 Institute of Automation Chinese Academy of Sciences, China

ISBN: 978-989-758-222-6

Keyword(s): High Resolution Optical Remote Sensing Image, Sea-land Segmentation, Ship Detection, Ship Recognition, Dataset.

Related Ontology Subjects/Areas/Topics: Applications ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Classification ; Computational Intelligence ; Computer Vision, Visualization and Computer Graphics ; Feature Selection and Extraction ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Image Understanding ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Object Recognition ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Software Engineering ; Theory and Methods

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''. (More)

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.172.195.49

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
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

@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},
}

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.