Polsar data online classification based on multi-view learning

Xiangli Nie; Shuguang Ding; Bo Zhang; Hong Qiao; Xiayuan Huang

doi:10.1109/ICIP.2017.8296703

Polsar data online classification based on multi-view learning

Xiangli Nie
, Shuguang Ding
, Bo Zhang
, Hong Qiao
, Xiayuan Huang

CAS - Institute of Automation
CAS - Institute of Applied Mathematics
Chinese Academy of Sciences

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

5 Scopus citations

Abstract

Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multiview learning method based on the passive aggressive algorithm, named OMVPA, for PolSAR data real-time classification. The OMVPA method makes full use of the consistency and complementary properties of different views. Experimental results on real PolSAR data demonstrate that the proposed method maintain a smaller mistake rate compared with other methods.

Original language	English
Title of host publication	2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
Publisher	IEEE Computer Society
Pages	2354-2358
Number of pages	5
ISBN (Electronic)	9781509021758
DOIs	https://doi.org/10.1109/ICIP.2017.8296703
State	Published - 2 Jul 2017
Externally published	Yes
Event	24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 17 Sep 2017 → 20 Sep 2017

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2017-September
ISSN (Print)	1522-4880

Conference

Conference	24th IEEE International Conference on Image Processing, ICIP 2017
Country/Territory	China
City	Beijing
Period	17/09/17 → 20/09/17

Keywords

Multi-view learning
Online classification
Passive-aggressive (PA) algorithm
Polarimetric synthetic aperture radar (PolSAR)

Access to Document

10.1109/ICIP.2017.8296703

Cite this

Nie, X., Ding, S., Zhang, B., Qiao, H., & Huang, X. (2017). Polsar data online classification based on multi-view learning. In 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings (pp. 2354-2358). (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September). IEEE Computer Society. https://doi.org/10.1109/ICIP.2017.8296703

@inproceedings{5495cf71aad442a7a234c9b7589e451d,

title = "Polsar data online classification based on multi-view learning",

abstract = "Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multiview learning method based on the passive aggressive algorithm, named OMVPA, for PolSAR data real-time classification. The OMVPA method makes full use of the consistency and complementary properties of different views. Experimental results on real PolSAR data demonstrate that the proposed method maintain a smaller mistake rate compared with other methods.",

keywords = "Multi-view learning, Online classification, Passive-aggressive (PA) algorithm, Polarimetric synthetic aperture radar (PolSAR)",

author = "Xiangli Nie and Shuguang Ding and Bo Zhang and Hong Qiao and Xiayuan Huang",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",

year = "2017",

month = jul,

day = "2",

doi = "10.1109/ICIP.2017.8296703",

language = "英语",

series = "Proceedings - International Conference on Image Processing, ICIP",

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booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",

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Nie, X, Ding, S, Zhang, B, Qiao, H & Huang, X 2017, Polsar data online classification based on multi-view learning. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 2354-2358, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17/09/17. https://doi.org/10.1109/ICIP.2017.8296703

Polsar data online classification based on multi-view learning. / Nie, Xiangli; Ding, Shuguang; Zhang, Bo et al.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2017. p. 2354-2358 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).

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

TY - GEN

T1 - Polsar data online classification based on multi-view learning

AU - Nie, Xiangli

AU - Ding, Shuguang

AU - Zhang, Bo

AU - Qiao, Hong

AU - Huang, Xiayuan

PY - 2017/7/2

Y1 - 2017/7/2

N2 - Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multiview learning method based on the passive aggressive algorithm, named OMVPA, for PolSAR data real-time classification. The OMVPA method makes full use of the consistency and complementary properties of different views. Experimental results on real PolSAR data demonstrate that the proposed method maintain a smaller mistake rate compared with other methods.

AB - Polarimetric synthetic aperture radar (PolSAR) plays an indispensable part in remote sensing. With its development and application, rapid and accurate online classification for PolSAR data becomes more and more important. PolSAR data can be depicted by different features such as polarimetric, texture and color features, which can be considered as multiple views. In this paper, we propose an online multiview learning method based on the passive aggressive algorithm, named OMVPA, for PolSAR data real-time classification. The OMVPA method makes full use of the consistency and complementary properties of different views. Experimental results on real PolSAR data demonstrate that the proposed method maintain a smaller mistake rate compared with other methods.

KW - Multi-view learning

KW - Online classification

KW - Passive-aggressive (PA) algorithm

KW - Polarimetric synthetic aperture radar (PolSAR)

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U2 - 10.1109/ICIP.2017.8296703

DO - 10.1109/ICIP.2017.8296703

M3 - 会议稿件

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T3 - Proceedings - International Conference on Image Processing, ICIP

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BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings

PB - IEEE Computer Society

T2 - 24th IEEE International Conference on Image Processing, ICIP 2017

Y2 - 17 September 2017 through 20 September 2017

ER -

Polsar data online classification based on multi-view learning

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