Automatic Synthetic Aperture Radar based oil spill detection and performance estimation via a semi-automatic operational service benchmark.

Today the health of ocean is in danger as it was never before mainly due to man-made pollutions. Operational activities show regular occurrence of accidental and deliberate oil spill in European waters. Since the areas covered by oil spills are usually large, satellite remote sensing particularly Synthetic Aperture Radar represents an effective option for operational oil spill detection. This paper describes the development of a fully automated approach for oil spill detection from SAR. Total of 41 feature parameters extracted from each segmented dark spot for oil spill and 'look-alike' classification and ranked according to their importance. The classification algorithm is based on a two-stage processing that combines classification tree analysis and fuzzy logic. An initial evaluation of this methodology on a large dataset has been carried out and degree of agreement between results from proposed algorithm and human analyst was estimated between 85% and 93% respectively for ENVISAT and RADARSAT.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  Camilla Brekke,et al.  Oil Spill Detection in Radarsat and Envisat SAR Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Konstantinos Karantzalos,et al.  Automatic detection and tracking of oil spills in SAR imagery with level set segmentation , 2008 .

[4]  Guido Ferraro,et al.  Oil spill detection using COSMO-SkyMed over the adriatic sea: The operational potential , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Monica Posada,et al.  Perspectives on Oil Spill Detection Using Synthetic Aperture Radar , 2010 .

[6]  Camilla Brekke,et al.  Feature Extraction for Oil Spill Detection Based on SAR Images , 2005, SCIA.

[7]  Camilla Brekke,et al.  Automatic screening of Synthetic Aperture Radar imagery for detection of oil pollution in the marine environment , 2007 .

[8]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[9]  B. Fiscella,et al.  Oil spill detection using marine SAR images , 2000 .

[10]  Terje Wahl,et al.  Oil spill detection using satellite based SAR: experience from a field experiment , 1993 .

[11]  Oscar Garcia-Pineda,et al.  Adaptive thresholding algorithm based on SAR images and wind data to segment oil spills along the northwest coast of the Iberian Peninsula. , 2012, Marine pollution bulletin.

[12]  Harm Greidanus,et al.  SAR Image Quality Assessment and Indicators for Vessel and Oil Spill Detection , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Anne H. Schistad Solberg,et al.  Automatic detection of oil spills in ENVISAT, Radarsat and ERS SAR images , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[14]  Chris T. Kiranoudis,et al.  Automatic identification of oil spills on satellite images , 2006, Environ. Model. Softw..

[15]  P. Trivero,et al.  Neural networks for the oil spill detection using ERS-SAR data , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[16]  Heinrich Hühnerfuss,et al.  Radar signatures of oil films floating on the sea surface and the Marangoni effect , 1988 .

[17]  Kostas Topouzelis,et al.  Oil spill detection: SAR multiscale segmentation and object features evaluation , 2003, SPIE Remote Sensing.

[18]  T. Wahl,et al.  Practical use of ERS-1 SAR images in pollution monitoring , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Mervin F. Fingas,et al.  Review of oil spill remote sensing , 1997 .

[20]  Konstantinos Topouzelis,et al.  Oil spill feature selection and classification using decision tree forest on SAR image data , 2012 .

[21]  Hailong Li,et al.  Long-term persistence of oil from the Exxon Valdez spill in two-layer beaches , 2010 .

[22]  K. Topouzelis,et al.  Detection and discrimination between oil spills and look-alike phenomena through neural networks , 2007 .

[23]  Jonathan Li,et al.  Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring. , 2010 .

[24]  Guido Ferraro,et al.  On the SAR derived alert in the detection of oil spills according to the analysis of the EGEMP. , 2010, Marine pollution bulletin.

[25]  P. Pavlakis,et al.  Dark formation detection using neural networks , 2008 .

[26]  Rune Solberg,et al.  Automatic detection of oil spills in ERS SAR images , 1999, IEEE Trans. Geosci. Remote. Sens..

[27]  A. Solberg,et al.  Oil spill detection by satellite remote sensing , 2005 .

[28]  Suman Singha,et al.  Satellite Oil Spill Detection Using Artificial Neural Networks , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[29]  Fabio Del Frate,et al.  Neural networks for oil spill detection using ERS-SAR data , 2000, IEEE Trans. Geosci. Remote. Sens..

[30]  Suman Singha,et al.  Detection and classification of oil spill and look-alike spots from SAR imagery using an Artificial Neural Network , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.