@inproceedings{5d3b50da5a6d45f58f2e5be5bf40f47d,
title = "Fast ship detection from optical satellite images based on ship distribution probability analysis",
abstract = "Automatic ship detection from optical satellite images remains a tough task. In this paper, a novel method of ship detection from optical satellites is proposed by analyzing the ship distribution probability. First, an anomaly detection model is constructed by the sea cluster histogram model; then, the ship distribution based on the ship safety navigational criterion is analyzed to obtain the ship candidates, and obvious non-ship objects are removed by the area properties from ship candidates; finally, a structural continuity descriptor is designed to remove false alarms from the ship candidates. Experiments on numerous satellite images from panchromatic and one band within multispectral sensors are conducted. The results verified that the proposed method outperforms existing methods in both effectiveness and efficiency.",
keywords = "Ship detection, optical satellite images, remote sensing",
author = "Xie Xiaoyang and Qizhi Xu and Lei Hu",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 4th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2016 ; Conference date: 04-07-2016 Through 06-07-2016",
year = "2016",
month = aug,
day = "25",
doi = "10.1109/EORSA.2016.7552774",
language = "English",
series = "4th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2016 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "97--101",
editor = "Paolo Gamba and George Xian and Shunlin Liang and Qihao Weng and Chen, {Jing Ming} and Shunlin Liang",
booktitle = "4th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2016 - Proceedings",
address = "United States",
}