Ship fuzzy recognition based on superstructure with maximum membership rule

Yong Wang; Pengkai Zhu

doi:10.1007/978-981-10-6571-2_249

Ship fuzzy recognition based on superstructure with maximum membership rule

Yong Wang^*
, Pengkai Zhu

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology

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

2 Scopus citations

Abstract

The Automatic Target Recognition (ATR) of ship targets based on high resolution Inverse Synthetic Aperture Radar (ISAR) images has a good prospect of marine environmental protection, monitoring and traffic management. In this letter, a novel ship classification technique is proposed based on the ship superstructure by using the fuzzy recognition method. An improved segmentation algorithm of the ship silhouette is applied to obtain the segment comparative mean (SCM) feature. The SCM is used to calculate the target’s membership of each class and eventually the maximum membership rule is applied to determine the target’s class. The experimental results of applying the technique on real ISAR data demonstrate the effectiveness of the proposed approach.

Original language	English
Title of host publication	Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems
Editors	Qilian Liang, Min Jia, Jiasong Mu, Wei Wang, Xuhong Feng, Baoju Zhang
Publisher	Springer Verlag
Pages	2045-2050
Number of pages	6
ISBN (Print)	9789811065705
DOIs	https://doi.org/10.1007/978-981-10-6571-2_249
State	Published - 2019
Event	6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 - Harbin, China Duration: 14 Jul 2017 → 16 Jul 2017

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	463
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017
Country/Territory	China
City	Harbin
Period	14/07/17 → 16/07/17

UN SDGs

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

SDG 14 Life Below Water

Keywords

ATR
ISAR
Maximum membership rule
SCM

Access to Document

10.1007/978-981-10-6571-2_249

Cite this

Wang, Y., & Zhu, P. (2019). Ship fuzzy recognition based on superstructure with maximum membership rule. In Q. Liang, M. Jia, J. Mu, W. Wang, X. Feng, & B. Zhang (Eds.), Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems (pp. 2045-2050). (Lecture Notes in Electrical Engineering; Vol. 463). Springer Verlag. https://doi.org/10.1007/978-981-10-6571-2_249

Wang, Yong ; Zhu, Pengkai. / Ship fuzzy recognition based on superstructure with maximum membership rule. Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. editor / Qilian Liang ; Min Jia ; Jiasong Mu ; Wei Wang ; Xuhong Feng ; Baoju Zhang. Springer Verlag, 2019. pp. 2045-2050 (Lecture Notes in Electrical Engineering).

@inproceedings{6c59ccbe4b5c442597456bcd158957ed,

title = "Ship fuzzy recognition based on superstructure with maximum membership rule",

abstract = "The Automatic Target Recognition (ATR) of ship targets based on high resolution Inverse Synthetic Aperture Radar (ISAR) images has a good prospect of marine environmental protection, monitoring and traffic management. In this letter, a novel ship classification technique is proposed based on the ship superstructure by using the fuzzy recognition method. An improved segmentation algorithm of the ship silhouette is applied to obtain the segment comparative mean (SCM) feature. The SCM is used to calculate the target{\textquoteright}s membership of each class and eventually the maximum membership rule is applied to determine the target{\textquoteright}s class. The experimental results of applying the technique on real ISAR data demonstrate the effectiveness of the proposed approach.",

keywords = "ATR, ISAR, Maximum membership rule, SCM",

author = "Yong Wang and Pengkai Zhu",

note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017 ; Conference date: 14-07-2017 Through 16-07-2017",

year = "2019",

doi = "10.1007/978-981-10-6571-2\_249",

language = "英语",

isbn = "9789811065705",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "2045--2050",

editor = "Qilian Liang and Min Jia and Jiasong Mu and Wei Wang and Xuhong Feng and Baoju Zhang",

booktitle = "Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems",

address = "德国",

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Wang, Y & Zhu, P 2019, Ship fuzzy recognition based on superstructure with maximum membership rule. in Q Liang, M Jia, J Mu, W Wang, X Feng & B Zhang (eds), Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol. 463, Springer Verlag, pp. 2045-2050, 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017, Harbin, China, 14/07/17. https://doi.org/10.1007/978-981-10-6571-2_249

Ship fuzzy recognition based on superstructure with maximum membership rule. / Wang, Yong; Zhu, Pengkai.
Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. ed. / Qilian Liang; Min Jia; Jiasong Mu; Wei Wang; Xuhong Feng; Baoju Zhang. Springer Verlag, 2019. p. 2045-2050 (Lecture Notes in Electrical Engineering; Vol. 463).

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

TY - GEN

T1 - Ship fuzzy recognition based on superstructure with maximum membership rule

AU - Wang, Yong

AU - Zhu, Pengkai

N1 - Publisher Copyright: © Springer Nature Singapore Pte Ltd. 2019.

PY - 2019

Y1 - 2019

N2 - The Automatic Target Recognition (ATR) of ship targets based on high resolution Inverse Synthetic Aperture Radar (ISAR) images has a good prospect of marine environmental protection, monitoring and traffic management. In this letter, a novel ship classification technique is proposed based on the ship superstructure by using the fuzzy recognition method. An improved segmentation algorithm of the ship silhouette is applied to obtain the segment comparative mean (SCM) feature. The SCM is used to calculate the target’s membership of each class and eventually the maximum membership rule is applied to determine the target’s class. The experimental results of applying the technique on real ISAR data demonstrate the effectiveness of the proposed approach.

AB - The Automatic Target Recognition (ATR) of ship targets based on high resolution Inverse Synthetic Aperture Radar (ISAR) images has a good prospect of marine environmental protection, monitoring and traffic management. In this letter, a novel ship classification technique is proposed based on the ship superstructure by using the fuzzy recognition method. An improved segmentation algorithm of the ship silhouette is applied to obtain the segment comparative mean (SCM) feature. The SCM is used to calculate the target’s membership of each class and eventually the maximum membership rule is applied to determine the target’s class. The experimental results of applying the technique on real ISAR data demonstrate the effectiveness of the proposed approach.

KW - ATR

KW - ISAR

KW - Maximum membership rule

KW - SCM

UR - https://www.scopus.com/pages/publications/85048696018

U2 - 10.1007/978-981-10-6571-2_249

DO - 10.1007/978-981-10-6571-2_249

M3 - 会议稿件

AN - SCOPUS:85048696018

SN - 9789811065705

T3 - Lecture Notes in Electrical Engineering

SP - 2045

EP - 2050

BT - Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems

A2 - Liang, Qilian

A2 - Jia, Min

A2 - Mu, Jiasong

A2 - Wang, Wei

A2 - Feng, Xuhong

A2 - Zhang, Baoju

PB - Springer Verlag

T2 - 6th International Conference on Communications, Signal Processing, and Systems, CSPS 2017

Y2 - 14 July 2017 through 16 July 2017

ER -

Wang Y, Zhu P. Ship fuzzy recognition based on superstructure with maximum membership rule. In Liang Q, Jia M, Mu J, Wang W, Feng X, Zhang B, editors, Communications, Signal Processing, and Systems - Proceedings of the 2017 International Conference on Communications, Signal Processing, and Systems. Springer Verlag. 2019. p. 2045-2050. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-10-6571-2_249

Ship fuzzy recognition based on superstructure with maximum membership rule

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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