Segmentation of oil spills in SAR image based on improved CV model

Wenjie Tie; Chonglei Wang; Fukun Bi; Hao Shi; Xu Zhang; He Chen

Segmentation of oil spills in SAR image based on improved CV model

Wenjie Tie, Chonglei Wang, Fukun Bi, Hao Shi, Xu Zhang, He Chen^*

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to conference › Paper › peer-review

Abstract

Extracting the oil spills in SAR images has been challenged by the spatial heterogeneity caused by the speckles. In this paper, we present a novel variational formulation based on the local characteristics of SAR images for CV mode. The proposed method is aimed to solve the segmentation problem caused by SAR image speckles. Compared with the traditional CV model, the performance of the improved CV model is verified by plenty of real airborne SAR images and the experimental results on the real data show its efficiency and accuracy. By presenting various results in the classical threshold segmentation method and Markov random field method respectively, we conclude that the improved CV model is effective and accurate.

Original language	English
Publication status	Published - 2015
Event	IET International Radar Conference 2015 - Hangzhou, China Duration: 14 Oct 2015 → 16 Oct 2015

Conference

Conference	IET International Radar Conference 2015
Country/Territory	China
City	Hangzhou
Period	14/10/15 → 16/10/15

Keywords

CV model
Local characteristics
Oil spills segmentation
SAR image
Speckles

Cite this

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title = "Segmentation of oil spills in SAR image based on improved CV model",

abstract = "Extracting the oil spills in SAR images has been challenged by the spatial heterogeneity caused by the speckles. In this paper, we present a novel variational formulation based on the local characteristics of SAR images for CV mode. The proposed method is aimed to solve the segmentation problem caused by SAR image speckles. Compared with the traditional CV model, the performance of the improved CV model is verified by plenty of real airborne SAR images and the experimental results on the real data show its efficiency and accuracy. By presenting various results in the classical threshold segmentation method and Markov random field method respectively, we conclude that the improved CV model is effective and accurate.",

keywords = "CV model, Local characteristics, Oil spills segmentation, SAR image, Speckles",

author = "Wenjie Tie and Chonglei Wang and Fukun Bi and Hao Shi and Xu Zhang and He Chen",

year = "2015",

language = "English",

note = "IET International Radar Conference 2015 ; Conference date: 14-10-2015 Through 16-10-2015",

}

TY - CONF

T1 - Segmentation of oil spills in SAR image based on improved CV model

AU - Tie, Wenjie

AU - Wang, Chonglei

AU - Bi, Fukun

AU - Shi, Hao

AU - Zhang, Xu

AU - Chen, He

PY - 2015

Y1 - 2015

N2 - Extracting the oil spills in SAR images has been challenged by the spatial heterogeneity caused by the speckles. In this paper, we present a novel variational formulation based on the local characteristics of SAR images for CV mode. The proposed method is aimed to solve the segmentation problem caused by SAR image speckles. Compared with the traditional CV model, the performance of the improved CV model is verified by plenty of real airborne SAR images and the experimental results on the real data show its efficiency and accuracy. By presenting various results in the classical threshold segmentation method and Markov random field method respectively, we conclude that the improved CV model is effective and accurate.

AB - Extracting the oil spills in SAR images has been challenged by the spatial heterogeneity caused by the speckles. In this paper, we present a novel variational formulation based on the local characteristics of SAR images for CV mode. The proposed method is aimed to solve the segmentation problem caused by SAR image speckles. Compared with the traditional CV model, the performance of the improved CV model is verified by plenty of real airborne SAR images and the experimental results on the real data show its efficiency and accuracy. By presenting various results in the classical threshold segmentation method and Markov random field method respectively, we conclude that the improved CV model is effective and accurate.

KW - CV model

KW - Local characteristics

KW - Oil spills segmentation

KW - SAR image

KW - Speckles

UR - https://www.scopus.com/pages/publications/84973526791

M3 - Paper

AN - SCOPUS:84973526791

T2 - IET International Radar Conference 2015

Y2 - 14 October 2015 through 16 October 2015

ER -

Segmentation of oil spills in SAR image based on improved CV model

Abstract

Conference

Keywords

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