  In this study the detection of the oil spill using synthetic aperture radar
(SAR) imagery is considered. Detection of the oil spill is performed using
change detection algorithms between imagery acquired at different times. The
specific algorithms used are the correlation coefficient change statistic and
the intensity ratio change statistic algorithms. Therefore these algorithms and
the probabilistic selection of the threshold criteria is reviewed and
discussed. A recently offered change detection method which depends on the idea
of generating two different final change maps of two images in a sequence, is
used. First final change map is obtained by cumulatively adding the sequences
of change maps in such a manner that common change areas are excluded and
uncommon change areas are included. The second final change map is obtained by
comparing the first and the last images in the temporal sequence. This method
requires at least three images to be employed and can be generalized to longer
temporal image sequences. The purpose of this approach is to provide a double
check mechanism to the conventional approach and thus to reduce the probability
of false alarm and enhance the change detection. The algorithms mentioned are
applied to a 2010 Gulf of Mexico oil spill imagery together with the method
described. It is shown that intensity ratio change statistic is a better tool
for identification of the changes due to the oil spill compared to the
correlation coefficient change statistic. It is also shown that two final
change map method can reduce the probability of false alarm. The data used in
this study is acquired by the Japanese Aerospace Agency's Advanced Land
Observing Satellite (ALOS) through Alaska SAR Facility (ASF) at the University
of Alaska, Fairbanks, AK.
