Oil Spill Segmentation in Fused Synthetic Aperture Radar Images

被引：0

作者：

Longman, Fodio S. ^{[1
]}

Mihaylova, Lyudmila ^{[1
]}

Coca, Daniel ^{[1
]}

机构：

[1] Univ Sheffield, Dept Automat Control & Syst Engn, Mappin St, Sheffield S1 3JD, S Yorkshire, England

来源：

2016 4TH INTERNATIONAL CONFERENCE ON CONTROL ENGINEERING & INFORMATION TECHNOLOGY (CEIT) | 2016年

关键词：

Oil Spill; Synthetic Aperture Radar (SAR); Registration; Image Fusion; Segmentation;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Synthetic Aperture Radar (SAR) satellite systems are very efficient in oil spill monitoring due to their capability to operate under all weather conditions. Systems such as the Envisat and RADARSAT have been used independently in many studies to detect oil spill. This paper presents an automatic feature based image registration and fusion algorithm for oil spill monitoring using SAR images. A range of metrics are used to evaluate the performance of the algorithm and to demonstrate the benefits of fusing SAR images of different modalities. The proposed framework has shown 45% improvement of the oil spill location when compared with the individual images before the fusion.

引用

页数：6

共 50 条

[1] Synthetic Aperture Radar Oil Spill Segmentation by Stochastic Complexity Minimization
Galland, Frederic
Refregier, Philippe
Germain, Olivier
[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2004, 1 (04) : 295 - 299
[2] Numerical Simulation of Synthetic Aperture Radar Images for Ocean Oil Spill Pollution
Yang, Wei
Qi, Cong-Hui
Zhao, Zhi-Qin
[J]. ELECTROMAGNETICS, 2016, 36 (01) : 32 - 43
[3] Adaptive stochastic minimization for measuring marine oil spill extent in synthetic aperture radar images
Moctezuma, Miguel
Parmiggiani, Flavio
[J]. JOURNAL OF APPLIED REMOTE SENSING, 2014, 8
[4] Oil spill detection using synthetic aperture radar images and feature selection in shape space
Guo, Yue
Zhang, Heng Zhen
[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2014, 30 : 146 - 157
[5] Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images
Chen, Guandong
Li, Yu
Sun, Guangmin
Zhang, Yuanzhi
[J]. APPLIED SCIENCES-BASEL, 2017, 7 (10):
[6] A new automatic segmentation for synthetic aperture radar images
Shi, QF
Li, Y
Zhang, YN
[J]. PROCEEDINGS OF THE 2004 INTERNATIONAL SYMPOSIUM ON INTELLIGENT MULTIMEDIA, VIDEO AND SPEECH PROCESSING, 2004, : 739 - 742
[7] Speckle reduction and segmentation of Synthetic Aperture Radar images
Smith, DM
[J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1996, 17 (11) : 2043 - 2057
[8] Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images
Amira S. Mahmoud
Sayed A. Mohamed
Reda A. El-Khoriby
Hisham M. AbdelSalam
Ihab A. El-Khodary
[J]. Journal of the Indian Society of Remote Sensing, 2023, 51 : 121 - 133
[9] Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images
Mahmoud, Amira S.
Mohamed, Sayed A.
El-Khoriby, Reda A.
AbdelSalam, Hisham M.
El-Khodary, Ihab A.
[J]. JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2023, 51 (01) : 121 - 133
[10] Oil Spill Detection in Synthetic Aperture Radar Images Using Lipschitz-Regularity and Multiscale Techniques
Ajadi, Olaniyi A.
Meyer, Franz J.
Tello, Marivi
Ruello, Giuseppe
[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2018, 11 (07) : 2389 - 2405

← 1 2 3 4 5 →