International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
NRCS [dB] Probability 
  
Figure 5: Joint probabilities for the imagery processed in this 
study. a)Radarsat-2 27-Apr, b)Radarsat-2 01-May, c)Alos 01- 
May, d)Alos 04-May. Alos results are enlarged for clarity. 
4 CONCLUSIONS AND FUTURE WORK 
More and more remote sensing data is becoming available ev- 
ery day, some of which are applicable to the the detection of oil 
spills. In this paper, we presented a method to combine SAR data 
from multiple sensors, acquired at different times from different 
geometries, to obtain a combined oil slick probability map for 
the Deepwater Horizon oil spill. Furthermore, it is also possible 
to combine results from different algorithms applied to the same 
data set, to increase accuracy as presented in this paper. 
Our fully automatic algorithm can be expanded to include other 
sensors (e.g. optical, infra-red, hyperspectral), and can be cus- 
tomized to the operational needs. The algorithm can operate with 
any sensor and any algorithm that can provide a probability map. 
The intuitive probability maps assist operators and ground per- 
sonnel in determining which areas to prioritize. Expansion of the 
algorithm to utilize MODIS imagery, and a neural-network based 
SAR oil spill algorithm for a future study is currently under con- 
sideration. 
ACKNOWLEDGEMENTS 
The authors would like to thank MDA Geospatial Services for 
Radarsat-2 data, Alaska Satellite Facility for Alos-PALSAR data, 
and the European Space Agency for Envisat-ASAR data. 
66 
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