@inproceedings{c4afc7a4c70e49f18b64833cd306070e,
title = "Fast and Accurate Sea-Land Segmentation based on Improved SeNet and Coastline Database for Large-Scale Image",
abstract = "Sea-land segmentation is an important and key procedure for the ocean target detection. Owing to complex coastline environment, the existing sea-land segmentation algorithms are difficult to extract helpful features while dealing with large-scale satellite remote sensing images. The paper presents a coarse-to-fine sea-land segmentation approach by combining the improved structured edge network (SeNet) and the coastline database. First of all the coarse latitude and longitude of the segmentation points between sea and land is obtained through applying coastline database to remote sensing images. Secondly on the basis of segmentation point, the region around coastline is divided into a group of blocks with 800×800 pixels. Finally SeNet is improved to separate the sea and land of the region. Experiential results on a large data set demonstrated that the proposed method achieved better accuracy and running time than the other methods.",
keywords = "coastline database, sea-land segmentation, structured edge network",
author = "Longyuan He and Qizhi Xu and Haimiao Hu and Jing Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018 ; Conference date: 18-06-2018 Through 20-06-2018",
year = "2018",
month = dec,
day = "31",
doi = "10.1109/EORSA.2018.8598546",
language = "English",
series = "5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Qihao Weng and Paolo Gamba and Ni-Bin Chang and Guangxing Wang and Wanqiang Yao",
booktitle = "5th International Workshop on Earth Observation and Remote Sensing Applications, EORSA 2018 - Proceedings",
address = "United States",
}