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Ocean surface monitoring, detection of oil spills, and accurate maritime vehicle detection is an important set of problem statements associated with ocean pollution monitoring. This paper proposes a novel study on Pix2Pix or generative adversarial networks for the aforementioned task. A comparative analysis is presented on various convolutional network-based encoders for semantic segmentation. A publicly available Synthetic Aperture Radar (SAR) dataset from the Sentinel-1 C-band sensor is used, constituting a total of 1112 images for the task of multiclass segmentation. Each integrant image pixel is classified into five classes, Oil Spills, Ships, Look-Alikes, Land Surface, and Ocean Surface. Deep architectures like U-Nets, SegNets, LinkNets, and FCNs are thoroughly assessed to obtain a practical analysis of this approach. Through a systematic exploration of hyperparameters, we identified the best-performing …