Oil Spills Identification in SAR Image Using Mahalanobis Distance

Peng Chen; Hui Zhou; Xiao Tian Wang

doi:10.4028/www.scientific.net/AMR.466-467.246

Paper Titles

Synthesis and Properties of Thermo- and pH-Responsive PVA/P(DEA-co-MAA) Intelligent Semi-IPN Hydrogels
p.229

Solvent-Free Aerobic Oxidation of Benzyl Alcohol by Resin Supported Noble Metal Catalysts
p.234

Experimental Study on Mechanical Properties of Basalt Hybrid Fiber Reinforced Polymer Sheets
p.238

Investigation on Al-Cr₂O₃-Al₂O₃ Sliding Gate Performance
p.242

Oil Spills Identification in SAR Image Using Mahalanobis Distance
p.246

Study on Resistance Characteristics of Marine Moisture Filter
p.251

Web-Based Bioinformatics Analysis of TbpB from Actinobacillus pleuropneumonie L20 Strain
p.256

Biodegradation Mechanism of Aniline Aerofloat by Bacillus vallismortis Isolated from SBR System
p.262

Flammability and Mechanical Properties of EVA Composites Containing Ammonium Polyphosphate Microcapsules
p.267

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 466-467Oil Spills Identification in SAR Image Using...

Oil Spills Identification in SAR Image Using Mahalanobis Distance

Abstract:

This paper presents a method of oil spills identification in Synthetic Aperture Radar (SAR) image based on feature vector, it makes use of the advantages of SAR which can work on day and night and all weather conditions with high resolution monitoring for oil spills. Use the algorithm of Mahalanobis distance to identify the target object and gain the feature vector through evaluating SAR image of the dark area boundary. It is proved by experiment that the number of selected feature value is reasonable and more effective for estimating whether has oil spills than the traditional one. The accuracy rate can reach 96% or even more for using the algorithm of Mahalanobis distance and compare to the other methods of oil spills identification it is easy for programming implementation with less conditions .

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 466-467)

Pages:

246-250

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.466-467.246

Citation:

Cite this paper

Online since:

February 2012

Authors:

Peng Chen, Hui Zhou, Xiao Tian Wang

Keywords:

Feature Vector, Mahalanobis Distance, Oil Spill, SAR Image

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Espedal, H. A., & Johannessen, O. M. (2000). Detection of oil spills near offshore installations using synthetic aperture radar (SAR). International Journal of Remote Sensing, 21(11), 2141–2144).

DOI: 10.1080/01431160050029468

Google Scholar

[2] YUAN Li-hai, SONG Jian-she, XUE Wen-tong, ZHENG Yong-an. SAR image classification using grey and texture features [J]. Electronics Optics & Control, 2007, 14(4): 58-62.

Google Scholar

[3] KERAMITSOGLOU, I., CARTALIS, C. and KIRANOUDIS, C. T., Automatic identification of oil spill on satellite images[J]. Environmental Modelling & Software, 2005, 21(5): 640-652.

DOI: 10.1016/j.envsoft.2004.11.010

Google Scholar

[4] Fabio Del Frate, Andrea Petrocchi, Juerg Lichtenegger, and Gianna Calabresi, Neural Networks for Oil Spill Detection Using ERS-SAR Data[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2000, 38(5), 2282-2287.

DOI: 10.1109/36.868885

Google Scholar

[5] Brekke, C., Solberg, H. A, Oil spill detection by satellite remote sensing[J]. Remote Sens. Environ. 2005, 95, 1-13.

DOI: 10.1016/j.rse.2004.11.015

Google Scholar

[6] Jingxuan Lu, Hock Lim, Soo Chin Liew, Mingquan Bao, and Leong Keong Kwoh, Oil pollution statistics in SouthEast Asian waters compiled from ERS SAR imagery, ESA Earth Observation Quarterly, 61, (1999).

DOI: 10.1109/igarss.1999.773450

Google Scholar