  Crude oil is an integral component of the modern world economy. With the
growing demand for crude oil due to its widespread applications, accidental oil
spills are unavoidable. Even though oil spills are in and themselves difficult
to clean up, the first and foremost challenge is to detect spills. In this
research, the authors test the feasibility of deep encoder-decoder models that
can be trained effectively to detect oil spills. The work compares the results
from several segmentation models on high dimensional satellite Synthetic
Aperture Radar (SAR) image data. Multiple combinations of models are used in
running the experiments. The best-performing model is the one with the
ResNet-50 encoder and DeepLabV3+ decoder. It achieves a mean Intersection over
Union (IoU) of 64.868% and a class IoU of 61.549% for the "oil spill" class
when compared with the current benchmark model, which achieved a mean IoU of
65.05% and a class IoU of 53.38% for the "oil spill" class.
