Ship Detection in SAR Using Extreme Learning Machine

Liyong Ma; Lidan Tang; Wei Xie; Shuhao Cai

doi:10.1007/978-3-319-73447-7_60

Ship Detection in SAR Using Extreme Learning Machine

Liyong Ma^*
, Lidan Tang
, Wei Xie
, Shuhao Cai

^*Corresponding author for this work

School of Information Science and Engineering, Harbin Institute of Technology Weihai

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

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.

Original language	English
Title of host publication	Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings
Editors	Bo Li, Xuemai Gu, Gongliang Liu
Publisher	Springer Verlag
Pages	558-568
Number of pages	11
ISBN (Print)	9783319734460
DOIs	https://doi.org/10.1007/978-3-319-73447-7_60
State	Published - 2018
Externally published	Yes
Event	2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017 - Weihai, China Duration: 5 Aug 2017 → 6 Aug 2017

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	227 LNICST
ISSN (Print)	1867-8211

Conference

Conference	2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017
Country/Territory	China
City	Weihai
Period	5/08/17 → 6/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 14 Life Below Water

Keywords

Extreme learning machine
Ship recognition
Synthetic aperture radar (SAR)

Access to Document

10.1007/978-3-319-73447-7_60

Cite this

Ma, L., Tang, L., Xie, W., & Cai, S. (2018). Ship Detection in SAR Using Extreme Learning Machine. In B. Li, X. Gu, & G. Liu (Eds.), Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings (pp. 558-568). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 227 LNICST). Springer Verlag. https://doi.org/10.1007/978-3-319-73447-7_60

Ma, Liyong ; Tang, Lidan ; Xie, Wei et al. / Ship Detection in SAR Using Extreme Learning Machine. Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. editor / Bo Li ; Xuemai Gu ; Gongliang Liu. Springer Verlag, 2018. pp. 558-568 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{e10a8145db6646fbb4f004b4b087c9ee,

title = "Ship Detection in SAR Using Extreme Learning Machine",

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 = "Extreme learning machine, Ship recognition, Synthetic aperture radar (SAR)",

author = "Liyong Ma and Lidan Tang and Wei Xie and Shuhao Cai",

note = "Publisher Copyright: {\textcopyright} 2018, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017 ; Conference date: 05-08-2017 Through 06-08-2017",

year = "2018",

doi = "10.1007/978-3-319-73447-7\_60",

language = "英语",

isbn = "9783319734460",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer Verlag",

pages = "558--568",

editor = "Bo Li and Xuemai Gu and Gongliang Liu",

booktitle = "Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings",

address = "德国",

}

Ma, L, Tang, L, Xie, W & Cai, S 2018, Ship Detection in SAR Using Extreme Learning Machine. in B Li, X Gu & G Liu (eds), Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 227 LNICST, Springer Verlag, pp. 558-568, 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017, Weihai, China, 5/08/17. https://doi.org/10.1007/978-3-319-73447-7_60

Ship Detection in SAR Using Extreme Learning Machine. / Ma, Liyong; Tang, Lidan; Xie, Wei et al.
Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. ed. / Bo Li; Xuemai Gu; Gongliang Liu. Springer Verlag, 2018. p. 558-568 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 227 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Ship Detection in SAR Using Extreme Learning Machine

AU - Ma, Liyong

AU - Tang, Lidan

AU - Xie, Wei

AU - Cai, Shuhao

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Extreme learning machine

KW - Ship recognition

KW - Synthetic aperture radar (SAR)

UR - https://www.scopus.com/pages/publications/85045270855

U2 - 10.1007/978-3-319-73447-7_60

DO - 10.1007/978-3-319-73447-7_60

M3 - 会议稿件

AN - SCOPUS:85045270855

SN - 9783319734460

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 558

EP - 568

BT - Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings

A2 - Li, Bo

A2 - Gu, Xuemai

A2 - Liu, Gongliang

PB - Springer Verlag

T2 - 2nd International Conference on Machine Learning and Intelligent Communications, MLICOM 2017

Y2 - 5 August 2017 through 6 August 2017

ER -

Ma L, Tang L, Xie W, Cai S. Ship Detection in SAR Using Extreme Learning Machine. In Li B, Gu X, Liu G, editors, Machine Learning and Intelligent Communications - Second International Conference, MLICOM 2017, Proceedings. Springer Verlag. 2018. p. 558-568. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-319-73447-7_60

Ship Detection in SAR Using Extreme Learning Machine

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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