Ship Detection in SAR Using Extreme Learning Machine

Liyong Ma; Lidan Tang; Wei Xie; Shuhao Cai

Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II

Research Article

Ship Detection in SAR Using Extreme Learning Machine

Download

175 downloads

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-319-73447-7_60,
    author={Liyong Ma and Lidan Tang and Wei Xie and Shuhao Cai},
    title={Ship Detection in SAR Using Extreme Learning Machine},
    proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II},
    proceedings_a={MLICOM},
    year={2018},
    month={2},
    keywords={Ship recognition Extreme learning machine Synthetic aperture radar (SAR)},
    doi={10.1007/978-3-319-73447-7_60}
}

Liyong Ma
Lidan Tang
Wei Xie
Shuhao Cai
Year: 2018
Ship Detection in SAR Using Extreme Learning Machine
MLICOM
Springer
DOI: 10.1007/978-3-319-73447-7_60

Liyong Ma¹^,*, Lidan Tang¹, Wei Xie¹, Shuhao Cai¹

1: Harbin Institute of Technology

*Contact email: maly@hitwh.edu.cn

Abstract

Ship detection is an important issue in many aspects, vessel traffic services, fishery management and rescue. Synthetic aperture radar (SAR) can produce real high resolution images with relatively small aperture in sea surfaces. A novel method employing extreme learning machine is proposed to detect ship in SAR. After the image preprocessing, some features including entropy, contrast, energy, correlation and inverse difference moment are selected as features for ship detection. The experimental results demonstrate that the proposed ship detection method based on extreme learning machine is more efficient than other learning-based methods with prior performance of accuracy, time consumed and ROC.

Keywords: Ship recognition Extreme learning machine Synthetic aperture radar (SAR)

Published: 2018-02-09
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-73447-7_60