Title :
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
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;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6723600
