Sea-Ice Classification Using Conditional Generative Adversarial Networks

In this letter, we develop a conditional generative adversarial network (cGAN) semantic segmentation model to detect and classify sea-ice in scenes captured onboard of a ship. Two datasets are utilized to train the cGAN model. The images in the first dataset capture four classes: sea-ice, open water, sky, and vessel. The images in the second dataset capture first year sea-ice, new sea-ice, and grey sea-ice in addition to the open water, sky, and vessel classes. Data augmentation operations are applied to both datasets to change the camera mounting angle and location and enhance the datasets' size. Results illustrate that the cGAN model outperforms the visual geometry group (VGG-16), fully convolutional network, and pyramid scene parsing network models in both datasets. Results also show that the cGAN model provides remarkable performance with rainy weather images, in which the performance of the other models degrades due to the raindrop.