Texture feature analysis in oil spill monitoring by SAR image

Author

Wei, Lai ; Hu, Zhuowei ; Meichen Guo ; Jiang, Minbin ; Zhang, Shuo

Author_Institution

Coll. of Resources Environ. & Tourism, Capital Normal Univ., Beijing, China

fYear

2012

fDate

15-17 June 2012

Firstpage

1

Lastpage

6

Abstract

This paper introduces the oil spill monitoring in SAR image by texture analysis and spectral information. In texture analysis, it discusses the parameters of texture extraction, and makes experiment. Then it determines the 17*17 as the windows size, 90 as the angle and 5 as distance. Through neural network classification, these parameters are suitable for oil spill monitoring. It can distinguish with oil and oil-like well. Its accuracy is satisfactory. These parameters are not only used for oil spill monitoring, but also provide a foundation for deeper SAR image classification.

Keywords

feature extraction; geophysical image processing; image classification; image texture; marine pollution; neural nets; oceanographic techniques; oil pollution; remote sensing by radar; synthetic aperture radar; SAR image classification; neural network classification; oil spill monitoring; spectral information; texture extraction; texture feature analysis; Correlation; Gravity; Satellites; Gray Level Co-occurrence Matrix (GLCM); Synthetic Aperture Radar (SAR); parameter of texture; texture analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoinformatics (GEOINFORMATICS), 2012 20th International Conference on

Conference_Location

Hong Kong

ISSN

2161-024X

Print_ISBN

978-1-4673-1103-8

Type

conf

DOI

10.1109/Geoinformatics.2012.6270284

Filename

6270284