An automatic detection system for natural oil seep origin estimation in SAR images

Author

Suresh, Gopika ; Heygster, Georg ; Bohrmann, Gerhard ; Melsheimer, Christian ; Korber, Jan-Hendrik

Author_Institution

Inst. of Environ. Phys., Univ. of Bremen, Bremen, Germany

fYear

2013

fDate

21-26 July 2013

Firstpage

3566

Lastpage

3569

Abstract

A framework for the automatic detection of natural oil seeps using Synthetic Aperture Radar (SAR) images, implemented in Python, is presented. Dark objects are detected using morphological thresholding. For each object, features are computed, which are used to classify the object as either a natural oil slick or a look-alike. The classification scheme has been implemented using a rule-based approach. The slick origins are detected and clustered together spatially, in order to detect the seep origin. A dataset of 122 images from ENVISAT´s Advanced Synthetic Aperture Radar (ASAR) were used to test the algorithm. In this paper, only preliminary results are reported.

Keywords

feature extraction; geophysical image processing; geophysical techniques; hydrocarbon reservoirs; image classification; radar imaging; remote sensing by radar; synthetic aperture radar; ENVISAT ASAR; Python; SAR images; advanced synthetic aperture radar; automatic detection system; classification scheme; dark objects; morphological thresholding; natural oil seep origin estimation; object feature extraction; rule-based approach; Backscatter; Estimation; Feature extraction; Hydrocarbons; Remote sensing; Sea surface; Synthetic aperture radar; Automatic detection; Feature Extraction; Hydrocarbon seeps; Oil slick; SAR;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International

Conference_Location

Melbourne, VIC

ISSN

2153-6996

Print_ISBN

978-1-4799-1114-1

Type

conf

DOI

10.1109/IGARSS.2013.6723600

Filename

6723600