Classification of Ocean Surface Slicks in SimulatedHybrid-Polarimetric SAR Data


In this paper, we consider hybrid-polarimetric SAR
data of ocean surface slicks, and hypothesize that we can design
a system that is able to discriminate between mineral oil, plant
oil and clean sea. We focus particularly on challenges related to
dataset shift between training and test data. In SAR images of
ocean surfaces, dataset shift is typically caused by variation of
wind level and incident angles that directly impact the backscatter
intensities. We evaluate several classifiers, domain adaptation
strategies and multilooking strategies. Hybrid-polarimetric SAR
data are simulated from the Radarsat-2 quad-pol images. The
proposed methodology was trained using five different Radarsat-
2 quad-pol images that cover slicks of known types, and tested
on 10 different Radarsat-2 quad-pol images covering various
ocean surface slicks. The results show that we were able, to
a large degree, to classify the type of various surface slicks. The
average classification accuracy obtained from cross-validation on
the training data was 91%. The results also show that we were
able to correctly classify surface slick in new test images, even
if the wind, surface and acquisition conditions were different
from the training images. We conclude that hybrid-polarity is
an attractive mode for future enhanced SAR-based oil spill
monitoring; however, to fully exploit the imaging mode singlelook
complex images are necessary.